Thèses : « Ab initio prediction »

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Thomas, Geraint Llewllyn. « Ab initio protein fold prediction ». Thesis, University of Leeds, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436019.

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Meyer, Irmtraud Margret. « Mathematical methods for comparative Ab initio gene prediction ». Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.619669.

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Djurdjević, Dušan. « Ab initio protein fold prediction using evolutionary algorithms ». Thesis, University of Edinburgh, 2006. http://hdl.handle.net/1842/13660.

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A comprehensive study was undertaken for ab initio protein fold prediction using a fully atomistic protein model and a physicochemical potential. Twenty four EA designs where initially assessed on polyalanine, a prototypical α-helical polypeptide. Design aspects varied include the encoding alphabet, crossover operator, replacement strategy and selection strategy. By undertaking a comprehensive parameter study, the best performing designs and associated control parameter values were identified for polyalanine. The scaling between the performance and polyalinine size was also identified for these best designs. This initial study was followed by a similar parametric study for met-enkephalin, a five residue polypeptide that has long been used as a de facto standard test case for protein structure prediction algorithms. It was found that the control parameter scalings identified from the polyalinine study were transferable to this real protein, and that the EA is superior to all existing ab initio approaches for met-enkephalin. The best design was finally applied to a series of real proteins ranging in size up to 45 residues to more generally assess the EA’s performance. The thesis is concluded with consideration of the future work required to extend the EA to larger proteins and ab initio structure prediction for non-native environments such as at interfaces, which are of relevance to, for example, biosensors.

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Wang, Guisheng. « Ab initio prediction of the mechanical properties of alloys ». Doctoral thesis, KTH, Tillämpad materialfysik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-169511.

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At the time of the 50th anniversary of the Kohn-Sham method, ab initio calculations based on density functional theory have formed an accurate, efficient, and reliable method to work on the properties of engineering materials. In this thesis, we use the exact muffin-tin orbitals method combined with the coherent-potential approximation to study the mechanical properties of high-technology materials. The thesis includes two parts: a study of long-range chemical order effects and a study of alloying effects on the mechanical properties of alloys. In the first part, we concentrate on the impact of chemical ordering on the mechanical properties. The long range order effect on the elastic constants behaves in a very different way for non-magnetic materials and ferromagnetic materials. For a non-magnetic Cu3Au, the long-range order effect on the elastic constants is very small. The Debye temperature does not show a strong chemical order dependence either. For a ferromagnetic material, on the other hand, the long-range chemical order produces considerable influence on C' in the ferromagnetic state, but negligible effect on C' in the paramagnetic state. The lattice parameter, bulk modulus $B$, and shear elastic constant C44 change slightly with the degree of long-rang order for both magnetic states. The Young's modulus E and the shear modulus G increase significantly with the degree of order in the ferromagnetic state, but the effect becomes weak as the system approaches the random regime.In the second part, the alloying effect of Mn/Ni on the lattice parameter, elastic constants, surface energy, and unstable stacking fault energy of bcc Fe is examined. The calculated results show that the lattice parameter of ferrite Fe is slightly altered upon Ni/Mn alloying the trend of which can be explained by the magnetism-induced pressure. Nickel addition decreases C' but has a negligible effect on C44, whereas manganese addition increases C44 and has a weak influence on C'. In both systems, the bulk modulus B shows a smooth second order variation. On the other hand, the surface energy and the unstable stacking fault (USF) energy decrease by adding Mn or Ni to Fe. For both planar fault energies, Ni shows a stronger effect than Mn. Segregation seems to have a minor effect on the surface and USF energies for dilute Fe-Ni and Fe-Mn alloys. The ductility can be estimated using available physical parameters via traditional phenomenological criteria like the Pugh ratio B/G, the Poisson ratio ν, the Cauchy pressure C12-C44, and the Rice ratio γs/γu .According to dislocation theory, a dislocation can not cross a grain boundary. Therefore, the study of dislocations is assumed to be limited to single-crystals. Several theoretical studies indicate that the cleavage plane is {001} in bcc crystals. Based on these information, we suggest that the resolved single-crystal tensile strength E[001] and the resolved single crystal shear strength G{110}<111> should be used to describe brittle cleavage and dislocation movement rather than polycrystalline parameters such as B and G. We demonstrate that all shear moduli G{lmn}<111> associated with the <111> Burgers vector take the same value 3C44C'/(C'+2C44), which could in fact explain the observed multiple slip in bcc systems. Due to the discrepancy between the resolved single-crystal elastic constants and the averaged polycrystalline elastic constants, the Pugh ratio B/G and the traditional criteria based on polycrystalline elastic constants lead to large differences for magnetic systems. Finally, we propose a new measure of the ductile-bittle behavior based on the ratio σclevage/Gresolved which gives the right experimental trend for Fe-Mn and Fe-Ni system.

QC 20150616

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Kang, ShinYoung. « Ab initio prediction of thermodynamics in alkali metal-air batteries ». Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/89952.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 93-100).
Electric vehicles ("EVs") require high-energy-density batteries with reliable cyclability and rate capability. However, the current state-of-the-art Li-ion batteries only exhibit energy densities near ~150 Wh/kg, limiting the long-range driving of EVs with one charge and hindering their wide-scale commercial adoption.1-3 Recently, non-aqueous metal-O₂ batteries have drawn attention due to their high theoretical specific energy.2, 4-6 Specifically, the issues surrounding battery studies involve Li-O₂ and Na-O₂ batteries due to their high theoretical specific energies of 3.5 kWh/kg (assuming Li 20 2 as a discharge product in Li-O₂ batteries) and 1.6 and 1.1 kWh/kg (assuming Na₂O₂ and NaO₂ as discharge products, respectively, in Na-O₂ batteries). Since the potential of Li-O₂ batteries as an energy storage system was first proposed in 1996,1 various studies have criticized and verified their shortcomings, such as their low power density, poor cyclability, and poor rate capability. ₇, ₈ Substantial research attempts have been made to identify the cause of the high overpotentials and electrolyte decomposition and to search for better cathode/electrolyte/anode and/or catalyst material combinations. However, Li-O₂ battery technology remains in its infancy primarily due to the lack of understanding of the underlying mechanisms. Therefore, we investigate the charging mechanism, which contributes to the considerable energy loss using first-principles calculations and propose a new charging mechanism based on experimental observations and knowledge concerning Li-ion and Na-ion batteries. Most studies on metal-O₂ batteries have mainly focused on Li-O₂ batteries. However, recently, the promising performance of Na-O₂ systems has been reported.₉, ₁₀ Although Na-O₂ batteries exhibit slightly lower theoretical specific energies than those of the Li-O₂ batteries as specified above, the chemical difference between the two alkali metals substantially distinguishes the electrochemistry properties of Na-O₂ and Li-O₂. In the Na-O₂ system, both NaO₂ and Na₂O₂ are stable compounds, while in the Li-O system, LiO₂ is not a stable compound under standard state conditions (300 K and 1 atm).₁₁, ₁₂ Presumably, due to this chemical difference, the Na-O₂ system has exhibited a much smaller charging overpotential, as low as 0.2 V, when NaO₂ is formed as a discharge product, compared with that in Li-O₂ system, >1 V. Such a low charging overpotential in Na-O₂ batteries demonstrates their potential as a next generation electrochemical system for commercially viable EVs .₉,₁₀ In this thesis, we study the thermodynamic stability of Na-O compounds to identify the phase selection conditions that affect the performance of Na-O₂ batteries.
by ShinYoung Kang.
Ph. D.

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Mijajlovic, Milan. « Ab initio prediction of the conformation of solvated and adsorbed proteins ». Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/3173.

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Proteins are among the most important groups of biomolecules, with their biological functions ranging from structural elements to signal transducers between cells. Apart from their biological role, phenomena related to protein behaviour in solutions and at solid interfaces can find a broad range of engineering applications such as in biomedical implants, scaffolds for artificial tissues, bioseparations, biomineralization and biosensors. For both biological and engineering applications, the functionality of a protein is directly related to its three-dimensional structure (i.e. conformation). Methods such as homology and threading that depend on a large database of existing experimental knowledge are the most popular means of predicting the conformation of proteins in their native environment. Lack of sufficient experimentally-derived information for non-native environments such as general solutions and solid interfaces prevents these knowledge-based methods being used for such environments. Resort must, instead, be made to so-called ab initio methods that rely upon knowledge of the primary sequence of the protein, its environment, and the physics of the interatomic interactions. The development of such methods for non-native environments is in its infancy – this thesis reports on the development of such a method and its application to proteins in water and at gas/solid and water/solid interfaces. After introducing the approach used – which is based on evolutionary algorithms (EAs) – we first report a study of polyalanine adsorbed at a gas/solid interface in which a switching behaviour is observed that, to our knowledge, has never been reported before. The next section reports work that shows the combination of the Langevin dipole (LD) solvent method with the Amber potential energy (PE) model is able to yield solvation energies comparable to those of more sophisticated methods at a fraction of the cost, and that the LD method is able to capture effects that arise from inhomogenities in the water structure such as H-bond bridges. The third section reports a study that shows that EA performance and optimal control parameters vary substantially with the PE model. The first three parts form the basis of the last part of the thesis, which reports pioneering work on predicting ab initio the conformation of proteins in solutions and at water/solid interfaces.

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DePristo, Mark Andrew. « Ab initio conformational sampling for protein structure determination, analysis, and prediction ». Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615942.

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Simons, Kim T. « Deciphering the protein folding code : ab initio prediction of protein structure / ». Thesis, Connect to this title online ; UW restricted, 1998. http://hdl.handle.net/1773/9234.

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Shi, Jingming. « Ab initio prediction of crystalline phases and their electronic properties : from ambient to extreme pressures ». Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1110/document.

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Dans cette thèse nous utilisons des méthodes globaux de prédiction des structures cristallographiques combinés à des techniques de grande capacité de traitement de données afin de prédire la structure cristalline de différents systèmes et dans des conditions thermodynamiques variées. Nous avons réalisé des prédictions structurales utilisant l'analyse cristalline par optimisation par essaims particuliers (CALYPSO) combinés avec la Théorie Fonctionnel de la Densité (DFT) ce qui a permis de mettre en évidence la stabilité de plusieurs composés jusqu'à la inconnus dans le digramme de phases du système Ba-Si et dans le système N-H-O. Nous avons également réalisé une étude à haute capacité de traitement de données sur un système ternaire de composition ABX2. Nous avons utilisé la Théorie Fonctionnel de la Densité combinant calculs de prototypes structuraux à partir des prédictions structurelles avec la méthode. Dans les paragraphes suivants nous résumons le contenu de différents chapitres de cette thèse. Le premier chapitre qui constitue une brève introduction au travail de cette thèse est suivi du chapitre 2 présentant les aspects théoriques utilisés dans ce travail. D'abord il est fait une brève introduction à la Théorie Fonctionnel de la Densité. A continuation nous décrivons quelques fonctions d'échange-corrélation choisies qui constituent des approximations rendant l'utilisation de la DFT efficace. Ensuite nous présentons différents procédés de prédiction structurale, et en particulier les algorithmes d'optimisation par essaims particuliers et de « Minima Hopping » qeu nous avons utilisés dans cette thèse. Finalement il est discuté comment doit-on se prendre pour évaluer la stabilité thermodynamique des nouvelles phases identifiées. Dans le chapitre 3, nous considérons le système Ba-Si. A travers l'utilisation d'une recherche structurale non-biaisée basée sur l'algorithme d'optimisation par essaims particuliers combinée avec des calculs DFT, nous faisons une étude systématique de la stabilité des phases et de la diversité structurale du système binaire Ba-Si sous haute pression. Le diagramme de phases résultant est assez complexe avec plusieurs compositions se stabilisant et se déstabilisant en fonction de la pression. En particulier, nous avons identifié des nouvelles phases de stœchiométrie BaSi, BaSi2, BaSi3 et BaSi5 qui devraient pouvoir être synthétisées expérimentalement dans un domaine de pressions étendu. Dans le chapitre 4 est présentée notre étude du diagramme de phases du système N-H-O. S'appuyant sur une recherche structural «évolutive » de type ab initio, nous prédisons deux nouvelles phases du système ternaire N-H-O qui sont NOH4 et HNO3 à de pressions allant jusqu'à 150 GPa. La nouvelle phase de NOH4 est stable entre 71 et 150 GPa, tandis que HNO3 est stable entre 39 et 150 GPa (la pression maximum de cette étude). Ces deux nouvelles phases sont lamellaires. Nous confirmons également que la composition NOH5 perd son stabilité pour des pressions supérieures à 122 GPa se décomposant en NH3 et H2O à cette pression. Le chapitre 5 se focalise sur les électrodes transparentes de type-p à base des chalcogénures ternaires. Nous utilisons une approche à grande capacité de traitement de données basée sur la DFT pour obtenir la delafossite et d'autres phases voisines de composition ABX2. Nous trouvons 79 systèmes qui sont absents de la base de données « Materials project database », qui sont stables du point de vue thermodynamique et qui cristallisent soit dans la structure delafossite, soit dans des structures très proches. Cette caractérisation révèle une grande diversité de propriétés allant depuis les métaux ordinaires aux métaux magnétiques et permettant d'identifier quelques candidats pour des électrodes transparents de type-p. Nous présentons enfin à la fin du manuscrit nos conclusions générales et les perspectives de ce travail
In this thesis we use global structural prediction methods (Particle Swarm Optimization and Minima Hopping Method) and high-throughput techniques to predict crystal structures of different systems under different conditions. We performed structural prediction by using the Crystal structure Analysis by Particle Swarm Optimization (CALYPSO) combined with Density Functional Theory (DFT) that made possible to unveil several stable compounds, so far unknown, on the phase diagrams of Ba-Si systerm and N-H-O system. Afterwards, we performed a high-throughput investigation on ternary compounds of composition ABX2, where A and B are elements of the periodic table up to Bi, and X is a chalcogen (O, S, Se, and Te) by using density functional theory and combining calculations of crystal prototypes with structural prediction (Minima Hopping Method). The following paragraphs summarize the content by chapter of this document. Chapter 1 is a short introduction of this thesis. Chapter 2 consists of the basic theory used in this thesis. Firstly, a short introduction of Density Function Theory (DFT) is presented. Then, we describe some approximate exchange- correlation functions that make DFT practical. Next, we introduce different structural prediction algorithms, especially Particle Swarm Optimization and Minima Hopping Method which we used in this thesis. Finally, we discuss the thermodynamic stablility criteria for a new a new structure. In Chapter 3, we first consider Ba–Si system. Using an unbiased structural search based on a particle-swarm optimization algorithm combined with DFT calculations, we investigate systematically the ground-state phase stability and structural diversity of Ba–Si binaries under high pressure. The phase diagram turns out to be quite intricate, with several compositions stabilizing/destabilizing as a function of pressure. In particular, we identify novel phases of BaSi, BaSi2, BaSi3, and BaSi5 that might be synthesizable experimentally over a wide range of pressures. Chapter 4 contains the investigation of the phases diagram of the N–H–O system. By using ab initio evolutionary structural search, we report the prediction of two novel phases of the N–H–O ternary system, namely NOH4 and HNO3 (nitric acid) at pressure up to 150 GPa. Our calculations show that the new C2/m phase of NOH4 is stable under a large range of pressure from 71 GPa to 150 GPa while the P21/m phase of HNO3 (nitric acid) is stable from 39 GPa to 150 GPa (the maximum pressure which we have studied). We also confirmed that the composition NOH5 (NH3H2O) becomes unstable for pressures above 122 GPa. It decomposes into NH3 and H2O at this pressure. Chapter 5 focuses on p-type transparent electrodes of ternary chalcogenides. We use a high-throughput approach based on DFT to find delafossite and related layered phases of composition ABX2, where A and B are elements of the periodic table, and X is a chalcogen (O, S, Se, and Te). From the 15 624 compounds studied in the trigonal delafossite prototype structure, 285 are within 50 meV/atom from the convex hull of stability. These compounds are further investigated using global structural prediction methods to obtain their lowest- energy crystal structure. We find 79 systems not present in the "Materials project database" that are thermodynamically stable and crystallize in the delafossite or in closely related structures. These novel phases are then characterized by calculating their band gaps and hole effective masses. This characterization unveils a large diversity of properties, ranging from normal metals, magnetic metals, and some candidate compounds for p-type transparent electrodes. At the end of the thesis, we give our general conclusion and an outlook

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McLean, Malcolm Arthur. « Potential energy functions and search routines for ab initio protein structure prediction ». Thesis, University of Leeds, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522963.

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Chan, Maria Kai Yee. « Atomistic and ab initio prediction and optimization of thermoelectric and photovoltaic properties ». Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53198.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 123-130).
The accurate prediction of physical properties in the vast spaces of nanoscale structures and chemical compounds is made increasingly possible through the use of atomistic and ab initio computation. In this thesis we investigate lattice thermal conductivities KL and electronic band gaps E,, which are relevant to thermoelectric and photovoltaic applications, respectively, and develop or modify computational tools for predicting and optimizing these properties. For lattice thermal conductivity, we study SiGe nanostructures, which are technologically important for thermoelectric applications. From computing aL for various SiGe nanostructures, we establish that the Kubo-Green approach using classical molecular dynamics (MD) gives additional quantitative predictions not available from phenomenological models, such as the existences of a minimum value of KL as the nanostructure size is varied and of configurational dependence of KL. We carry out the minimizatin of KL in the space of atomic configurations in SiGe alloy nanowires and demonstrate the feasibility of using the cluster expansion technique to parameterize KL. We find that the use of coarse graining and a meta cluster expansion approach is effective, in conjunction with a genetic algorithm, to find configurations which drastically lower KL. The low values of KL obtained, close to the bulk amorphous limit, are due to the absence of long-range order, and such absence allows a local cluster expansion approach to optimize KL. We examine ab initio bandgap prediction for semiconductor compounds, and address the large errors of Kohn-Sham band gaps in density functional theory (DFT).
(cont.) We apply corrections using the self-energy approach in the GW approximation, which includes non-local screened exchange and correlation, and find that the G₀W₀ approximation significantly reduces prediction errors compared to Kohn-Sham band gaps, though at much higher computational cost. We propose a new method involving total energies in DFT to predict the fundamental gap, by use of the properties of the screening or exchange-correlation hole in an electron gas. With this method, we are able to efficiently predict band gaps that are in agreement with experimental values.
by Maria Kai Yee Chan.
Ph.D.

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De, Bakker Paul I. Wen. « Ab initio sampling of polypeptide conformations and the prediction of protein structure ». Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.619861.

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Kashiwabara, Andre Yoshiaki. « MYOP : um arcabouço para predição de genes ab initio\" ». Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-25112009-151237/.

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A demanda por abordagens eficientes para o problema de reconhecer a estrutura de cada gene numa sequência genômica motivou a implementação de um grande número de programas preditores de genes. Fizemos uma análise dos programas de sucesso com abordagem probabilística e reconhecemos semelhanças na implementação dos mesmos. A maior parte desses programas utiliza a cadeia oculta generalizada de Markov (GHMM - generalized hiddenMarkov model) como um modelo de gene. Percebemos que muitos preditores têm a arquitetura da GHMM fixada no código-fonte, dificultando a investigação de novas abordagens. Devido a essa dificuldade e pelas semelhanças entre os programas atuais, implementamos o sistema MYOP (Make Your Own Predictor) que tem como objetivo fornecer um ambiente flexível o qual permite avaliar rapidamente cada modelo de gene. Mostramos a utilidade da ferramenta através da implementação e avaliação de 96 modelos de genes em que cada modelo é formado por um conjunto de estados e cada estado tem uma distribuição de duração e um outro modelo probabilístico. Verificamos que nem sempre um modelo probabilísticomais sofisticado fornece um preditor melhor, mostrando a relevância das experimentações e a importância de um sistema como o MYOP.
The demand for efficient approaches for the gene structure prediction has motivated the implementation of different programs. In this work, we have analyzed successful programs that apply the probabilistic approach. We have observed similarities between different implementations, the same mathematical framework called generalized hidden Markov chain (GHMM) is applied. One problem with these implementations is that they maintain fixed GHMM architectures that are hard-coded. Due to this problem and similarities between the programs, we have implemented the MYOP framework (Make Your Own Predictor) with the objective of providing a flexible environment that allows the rapid evaluation of each gene model. We have demonstrated the utility of this tool through the implementation and evaluation of 96 gene models in which each model has a set of states and each state has a duration distribution and a probabilistic model. We have shown that a sophisticated probabilisticmodel is not sufficient to obtain better predictor, showing the experimentation relevance and the importance of a system as MYOP.

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Mayo, Martin. « Ab initio anode materials discovery for Li- and Na-ion batteries ». Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/270545.

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This thesis uses first principles techniques, mainly the ab initio random structure searching method (AIRSS), to study anode materials for lithium- and sodium- ion batteries (LIBs and NIBs, respectively). Initial work relates to a theoretical structure prediction study of the lithium and sodium phosphide systems in the context of phosphorus anodes as candidates for LIBs and NIBs. The work reveals new Li-P and Na-P phases, some of which can be used to better interpret previous experimental results. By combining AIRSS searches with a high-throughput screening search from structures in the Inorganic Crystal Structure Database (ICSD), regions in the phase diagram are correlated to different ionic motifs and NMR chemical shielding is predicted from first principles. An electronic structure analysis of the Li-P and Na-P compounds is performed and its implication on the anode performance is discussed. The study is concluded by exploring the addition of aluminium dopants to the Li-P compounds to improve the electronic conductivity of the system. The following work deals with a study of tin anodes for NIBs. The structure prediction study yields a variety of new phases; of particular interest is a new NaSn$_2$ phase predicted by AIRSS. This phase plays a crucial role in understanding the alloying mechanism of high-capacity tin anodes, work which was done in collaboration with experimental colleagues. Our predicted theoretical voltages give excellent agreement with the experimental electrochemical cycling curve. First principles molecular dynamics is used to propose an amorphous Na$_1$Sn$_1$ model which, in addition to the newly derived NaSn$_2$ phase, provides help in revealing the electrochemical processes. In the subsequent work, we study Li-Sn and Li-Sb intermetallics in the context of alloy anodes for LIBs. A rich phase diagram of Li-Sn is present, exhibiting a variety of new phases. The calculated voltages show excellent agreement with previously reported cycling measurements and a consistent structural evolution of Li-Sn phases as Li concentration increases is revealed. The study concluded by calculating NMR parameters on the hexagonal- and cubic-Li$_3$Sb phases which shed light on the interpretation of reported experimental data. We conclude with a structure prediction study of the pseudobinary Li-FeS$_2$ system, where FeS$_2$ is considered as a potential high-capacity electrochemical energy storage system. Our first principles calculations of intermediate structures help to elucidate the mechanism of charge storage observed by our experimental collaborators via $\textit{in operando}$ studies.

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Zhu, Wenhan. « Improvement of ab initio methods of gene prediction in genomic and metagenomic sequences ». Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33869.

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A metagenome originated from a shotgun sequencing of a microbial community is a heterogeneous mixture of rather short sequences. A vast majority of microbial species in a given community (99%) are likely to be non-cultivable. Many protein-coding regions in a new metagenome are likely to code for barely detectable homologs of already known proteins. Therefore, an ab initio method that would accurately identify the new genes is a vitally important tool of metagenomic sequence analysis. However, a heuristic model method for finding genes in short prokaryotic sequences with anonymous origin was proposed in 1999 prior to the advent of metagenomics. With hundreds of new prokaryotic genomes available it is now possible to enhance the original approach and to utilize direct polynomial and logistic approximations of oligonucleotide frequencies. The idea was to bypass traditional ways of parameter estimation such as supervised training on a set of validated genes or unsupervised training on an anonymous sequence supposed to contain a large enough number of genes. The codon frequencies, critical for the model parameterization, could be derived from frequencies of nucleotides observed in the short sequence. This method could be further applied for initializing the algorithms for iterative parameters estimation for prokaryotic as well as eukaryotic gene finders.

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Gibbs, Nicholas. « Ab initio protein tertiary structure prediction using restricted ramachandran geometries and physio-chemical potentials ». Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340353.

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Sarmiento, Pérez Rafael. « Ab initio prediction of crystalline phases and electronic properties of alloys and other compounds ». Thesis, Lyon 1, 2015. http://www.theses.fr/2015LYO10155/document.

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La thèse présente une étude dans le cadre de la conception ab initio de nouveaux matériaux, avec des applications aux alliages intermétalliques et semi-conducteurs, aux oxydes transparents conducteurs et aux solides moléculaires. Des simulations avec la méthode Minima Hopping combinée avec la théorie de la fonctionnelle de la densité ont été utilisées pour trouver des nouveaux composés dans les diagrammes de phase des composés binaires de Lithium-Aluminium et Sodium-Or, aussi bien que des géométries de faible symétrie de CuBO2 à plus basses énergies que la structure delafossite qui était considérée comme son état fondamental. Nous avons aussi couplé la méthode Minima Hopping et la recherche de structures avec prototypes pour trouver de nouvelles perovskites de nitrure. Egalement, nous avons trouvé que la molécule H3 peut être stabilisée dans des structures à cages de CI à pressions d'environ 100 GPa. Nous avons aussi étudié les propriétés électroniques des alliages de chalcopyrite Cu(In, Ga)S2. Dans un sujet plus fondamental, nous proposons une fonctionnelle d'échange-corrélation semi-empirique optimisée pour obtenir des énergies de formation plus précises pour les solides
In this work we present an ab initio materials design study of several systems covering intermetallic and semiconducting alloys, transparent conductive oxides and molecular solids. We performed Minima Hopping calculations combined with Density Functional Theory that made possible to unveil several stable compounds in the phase diagrams of lithium-aluminium and sodium-gold binary alloys, as well as low-symmetry geometries of CuBO2, significantly lower in energy than the controversial delafossite structure reported as its ground state. We also found that the H3 molecule can be stabilized inside Cl cages at pressures of around 100 GPa. Additionally, we combined high-throughput techniques and global structure prediction methods to find nitride perovskites structures. In a different line, we studied the change in the absorption properties of the Cu(In,Ga)S2 chalcopyrite alloys as it was unexpectedly observed in experiment that with the change of the In/Ga ratio, the S K-absorption edge shifts, while the absorption edges of the other species is largely independent of the composition. In a more fundamental chapter, we propose a semi empirical exchange correlation functional optimized to yield accurate energies of formation of solids. The manuscript is organized as follows

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Parra, Farré Genís. « Computational identification of genes : ab initio and comparative approaches ». Doctoral thesis, Universitat Pompeu Fabra, 2004. http://hdl.handle.net/10803/7082.

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El trabajo que aquí se presenta, estudia el reconocimiento de las señales que delimitan y definen los genes que codifican para proteínas, así como su aplicabilidad en los programas de predicción de genes. La tesis que aquí se presenta, también explora la utilitzación de la genómica comparativa para mejorar la identificación de genes en diferentes especies simultaniamente. También se explica el desarrollo de dos programas de predicción computacional de genes: geneid y sgp2. El programa geneid identifica los genes codificados en una secuencia anónima de DNA basandose en sus propiedades intrínsecas (principalmente las señales de splicing y el uso diferencial de codones). sgp2 permite utilitzar la comparación entre dos genomas, que han de estar a una cierta distancia evolutiva óptima, para mejorar la predicción de genes, bajo la hipotesis que las regiones codificantes están mas conservadas que las regiones que no codifican para proteínas.
The motivation of this thesis is to give a little insight in how genes are encoded and recognized by the cell machinery and to use this information to find genes in unannotated genomic sequences. One of the objectives is the development of tools to identify eukaryotic genes through the modeling and recognition of their intrinsic signals and properties. This thesis addresses another problem: how the sequence of related genomes can contribute to the identification of genes. The value of comparative genomics is illustrated by the sequencing of the mouse genome for the purpose of annotating the human genome. Comparative gene predictions programs exploit this data under the assumption that conserved regions between related species correspond to functional regions (coding genes among them). Thus, this thesis also describes a gene prediction program that combines ab initio gene prediction with comparative information between two genomes to improve the accuracy of the predictions.

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Laury, Marie L. « Accurate and Reliable Prediction of Energetic and Spectroscopic Properties Via Electronic Structure Methods ». Thesis, University of North Texas, 2013. https://digital.library.unt.edu/ark:/67531/metadc500071/.

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Computational chemistry has led to the greater understanding of the molecular world, from the interaction of molecules, to the composition of molecular species and materials. Of the families of computational chemistry approaches available, the main families of electronic structure methods that are capable of accurate and/or reliable predictions of energetic, structural, and spectroscopic properties are ab initio methods and density functional theory (DFT). The focus of this dissertation is to improve the accuracy of predictions and computational efficiency (with respect to memory, disk space, and computer processing time) of some computational chemistry methods, which, in turn, can extend the size of molecule that can be addressed, and, for other methods, DFT, in particular, gain greater insight into which DFT methods are more reliable than others. Much, though not all, of the focus of this dissertation is upon transition metal species – species for which much less method development has been targeted or insight about method performance has been well established. The ab initio approach that has been targeted in this work is the correlation consistent composite approach (ccCA), which has proven to be a robust, ab initio computational method for main group and first row transition metal-containing molecules yielding, on average, accurate thermodynamic properties, i.e., within 1 kcal/mol of experiment for main group species and within 3 kcal/mol of experiment for first row transition metal molecules. In order to make ccCA applicable to systems containing any element from the periodic table, development of the method for second row transition metals and heavier elements, including lower p-block (5p and 6p) elements was pursued. The resulting method, the relativistic pseudopotential variant of ccCA (rp-ccCA), and its application are detailed for second row transition metals and lower p-block elements. Because of the computational cost of ab initio methods, DFT is a popular choice for the study of transition metals. Despite this, the most reliable density functionals for the prediction of energetic properties (e.g. enthalpy of formation, ionization potential, electron affinity, dissociation energy) of transition metal species, have not been clearly identified. The examination of DFT performance for first and second row transition metal thermochemistry (i.e., enthalpies of formation) was conducted and density functionals for the study of these species were identified. And, finally, to address the accuracy of spectroscopic and energetic properties, improvements for a series of density functionals have been established. In both DFT and ab initio methods, the harmonic approximation is typically employed. This neglect of anharmonic effects, such as those related to vibrational properties (e.g. zero-point vibrational energies, thermal contributions to enthalpy and entropy) of molecules, generally results in computational predictions that are not in agreement with experiment. To correct for the neglect of anharmonicity, scale factors can be applied to these vibrational properties, resulting in better alignment with experimental observations. Scale factors for DFT in conjunction with both the correlation and polarization consistent basis sets have been developed in this work.

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Bonneau, Richard A. « Gene annotation using Ab initio protein structure prediction : method development and application to major protein families / ». Thesis, Connect to this title online ; UW restricted, 2001. http://hdl.handle.net/1773/9241.

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Markov, Maksim. « Prediction of thermal conductivity and strategies for heat transport reduction in bismuth : an ab initio study ». Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLX010/document.

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Cette thèse de doctorat porte sur l'étude théorique de la conductivité thermique du réseau dans le bismuth semi-métallique et sur les stratégies pour réduire la conductivité thermique en vue d'applications pour réduire l'échauffement dans les circuits électroniques, et pour la thermoélectricité. J'ai utilisé des méthodes avancées de résolution de l'équation de transport de Boltzmann pour les phonons, et de calcul ab initio des éléments de matrice de l'interaction phonon-phonon. J'ai calculé la dépendance en température de la conductivité thermique du réseau dans le matériau en volume en excellent accord avec les rares expériences disponibles. J'ai obtenu une description très précise, à l'échelle microscopique, du transport de la chaleur et j'ai quantifié la contribution des porteurs de charge à la conductivité thermique totale. J'ai démontré que la nano-structuration et la photo-excitation sont des moyens très efficaces dans le bismuth de contrôler la diffusion des phonons qui portent la chaleur, respectivement par interaction avec les bords de l'échantillon, et par interaction phonon-phonon. En contrôlant l'équilibre entre ces deux derniers effets, j'ai prédit de façon exhaustive l'effet de réduction pour différentes températures et tailles de nanostructures, pour des mono et poly-cristaux, semi-conducteurs ou semi-métalliques. Enfin, j'ai étudié l'élargissement anharmonique des phonons acoustiques et optiques, et j'ai déterminé pour chacun les interactions majeures qui contribuent à l'élargissement. L'atténuation du son a été prédite dans le bismuth pour de futures expériences. L'approximation des grandes longueurs d'ondes [long-wave approximation (LWA)] a été validée pour le bismuth et ses limites ont été déterminées
This work is devoted to the theoretical investigation of the heat conduction in bulk bismuth and the possible strategies for its reduction. Thermal properties of Bi are extremely interesting because of its low thermal conductivity that makes this material suitable for the thermal management applications. Moreover, bismuth is an excellent model substance for the study of thermoelectricity and bismuth-based compounds such as Bi2 Te3 and Bi2 Se3 which are typical thermoelectric materials used in industrial applications.In collaboration with L. Paulatto (IMPMC), G. Fugallo (Ecole Polytechnique), F. Mauri(IMPMC) and M. Lazzeri (IMPMC) I have applied the recently developed advanced methods of the solution of the Boltzmann transport equation (BTE) and of the phonon-phonon matrix elements calculation to describe thermal transport in bismuth. I have obtained the temperature dependence of the lattice thermal conductivity which is in excellent agreement with experiment. Moreover I am able to predict the lattice thermal conductivity (LTC) at temperatures at which it has not been measured. I have found that most of heat is carried by the acoustic phonons. However, the optical phonons were shown to play an important role by modulating the magnitude of the acoustic-optical phonon interaction (AOPI) and thus the value of the lattice thermal conductivity. Furthermore, I have shown that the available experimental data for the lattice thermal conductivity for polycrystalline thin-films are remarkably explained by my calculations, which enables me to predict the effect of the LTC size reduction for various temperatures and nanostructure shapes and sizes.The methods I use contain no empirical fitting parameters and give a direct insight into the microscopic mechanisms determining the transport and anharmonic properties of the materials. This allows me to analyze the anharmonic broadening that is inversely proportional to the phonon lifetime, for the various phonon modes along the high symmetry directions in the Brillouin zone and show what are the major scattering channels for coalescence/decays of phonons that govern the thermal transport in Bi

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Brasil, Christiane Regina Soares. « Algoritmo evolutivo de muitos objetivos para predição ab initio de estrutura de proteínas ». Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-20072012-163056/.

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Este trabalho foca o desenvolvimento de algoritmos de otimização para o problema de PSP puramente ab initio. Algoritmos que melhor exploram o espaço de potencial de soluções podem, em geral, encontrar melhores soluções. Esses algoritmos podem beneficiar ambas abordagens de PSP, tanto o modelo ab initio quanto os baseados em conhecimento a priori. Pesquisadores tem mostrado que Algoritmos Evolutivos Multiobjetivo podem contribuir significativamente no contexto do problema de PSP puramente ab initio. Neste contexto, esta pesquisa investiga o Algoritmo Evolutivo Multiobjetivo baseado em Tabelas aplicado ao PSP puramente ab initio, que apresenta interessantes resultados para proteínas relativamente simples. Por exemplo, um desafio para o PSP puramente ab initio é a predição de estruturas com folhas-. Para trabalhar com tais proteínas, foi desenvolvido procedimentos computacionalmente eficientes para estimar energias de ligação de hidrogênio e solvatação. Em geral, estas não são consideradas no PSP por abordagens que combinam métodos de otimização e conhecimento a priori. Considerando somente van der Waals e eletrostática, as duas energias de interação que mais contribuem para a definição da estrutura de uma proteína, com as energias de ligação de hidrogênio e solvatação, o problema de PSP tem quatro objetivos. Problemas combinatórios (tais como o PSP), com mais de três objetivos, geralmente requerem métodos específicos capazes de lidar com muitos critérios. Para resolver essa limitação, este trabalho propõe um novo método para a otimização dos muitos objetivos, chamado Algoritmo Evolutivo Multiobjetivo com Muitas Tabelas (AEMMT). Esse método executa uma amostragem mais adequada do espaço de funções objetivo e, portanto, pode mapear melhor as regiões promissoras deste espaço. A capacidade de lidar com muitos objetivos capacita o AEMMT a utilizar melhor a informação oriunda das energias de solvatação e de ligação de hidrogênio, e então predizer estruturas com folhas- e algumas proteínas relativamente mais complexas. Do ponto de vista computacional, o AEMMT é um novo método que lida com muitos objetivos (mais de dez) encontrando soluções relevantes
This work focuses on the development of optimization algorithms for the purely ab initio Protein Structure Prediction (PSP) problem. Algorithms that better explore the space of potential solutions can in general find better solutions. Such algorithms can benefit both ab initio and template-based PSP, that uses priori knowledge. Researches have shown that Multiobjective evolutionary algorithms can contribute significantly in the context of purely ab initio PSP. In this context, this research investigates the Multiobjective Evolutionary Algorithm based on Tables applied to purely ab initio PSP, which has shown interesting results for relatively simple proteins. For example, one challenge for purely ab initio PSP is the prediction of structures with -sheets. To work with such proteins, this research has developed computationally efficient procedures to estimate hydrogen bond and solvation energies. In general, they are not considered by PSP approaches combining optimization methods with priori knowledge. Only by considering van der Waals and electrostatic, the two interaction energies that mostly contribute to defining a protein structure, and the hydrogen bond and solvation energies, the PSP problem has four objectives. Combinatorial problems (such as the PSP) with more than three objective usually require specific methods capable of dealing with many goals. To address this limitation, we propose a new method for many objective optimization, called Multiobjective Evolutionary Algorithm with Many Tables (MEAMT). This method performs a more adequate sampling of the space of objective functions and, therefore, can better map the promising regions of this space. The ability of dealing with many objectives enables the MEANT to better use information generated by solvation and hydrogen bond energies, and then predict structures with -sheets and some relatively complex proteins. From the computational point of view, the MEAMT is a new method for dealing with many objectives (more than ten) finding relevant solutions

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Griffiths, Mark. « Towards ligand design : Quantum Chemical Topology descriptors of heterocyclic compounds and pKa prediction from ab initio bond lengths ». Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/towards-ligand-design-quantum-chemical-topology-descriptors-of-heterocyclic-compounds-and-pka-prediction-from-ab-initio-bond-lengths(cea30196-c1ce-4801-b6d9-c81c330ae7e4).html.

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Bioisosterism is a field that is widely applied to biological molecules, including drugs and agrochemicals. Bioisosterism is the replacement of an active fragment in a molecule with another fragment similar in activity. The replacement is designed to alter the behavior of the molecule in its target environment. In previous work a bioisostere database called the Quantum Isostere Database (QID) was built out of descriptors derived from the theory of Quantum Chemical Topology (QCT). The current work aims to expand the existing QID to include ring fragments. A series of rings were characterised by QCT properties taken from the ring. It was found that four features of a ring each independently have a systematic effect on the ring’s properties. In other words, each of the characteristics of a ring can be changed and have the same effect on the ring’s properties irrespective of the other ring features. The rings were also characterised using the three QCT properties taken from a point within the ring. The three properties established a space where rings were positioned based on their respective three properties. The positions of the rings showed that the space was able to discern between ring types, and that the features of a ring could be predicted if only its three properties were known. To improve the QID the alignment method and scoring were tested. The alignment procedure is unable to correctly align collinear fragments. Therefore, a principal axis alignment procedure was successfully employed to align collinear fragments. For terminal fragments an alternative alignment procedure was proposed to account for the increased rotational freedom. A global axis system meant that the direction dependent properties for all fragments were expressed in this new axis system. This idea was extended further and it was found that the geometry of a molecule was imprinted in the electrostatics when they were expressed in the global axis system. Finally, a pKa prediction method which correlates a single ab initio bond length was tested against two data sets (enols and guanidines). The method relies on subsets to form, where molecules within a subset share a chemical or structural commonality. These subsets were able to distinguish between the five tautomeric forms for the guanidines and different conformations for the enols. All predictions were within 1.0 pKa units of experimental values.

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Bonetti, Daniel Rodrigo Ferraz. « Algoritmos de estimação de distribuição para predição ab initio de estruturas de proteínas ». Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-03082015-193613/.

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As proteínas são moléculas que desempenham funções essenciais para a vida. Para entender a função de uma proteína é preciso conhecer sua estrutura tridimensional. No entanto, encontrar a estrutura da proteína pode ser um processo caro e demorado, exigindo profissionais altamente qualificados. Neste sentido, métodos computacionais têm sido investigados buscando predizer a estrutura de uma proteína a partir de uma sequência de aminoácidos. Em geral, tais métodos computacionais utilizam conhecimentos de estruturas de proteínas já determinadas por métodos experimentais, para tentar predizer proteínas com estrutura desconhecida. Embora métodos computacionais como, por exemplo, o Rosetta, I-Tasser e Quark tenham apresentado sucesso em suas predições, são apenas capazes de produzir estruturas significativamente semelhantes às já determinadas experimentalmente. Com isso, por utilizarem conhecimento a priori de outras estruturas pode haver certa tendência em suas predições. Buscando elaborar um algoritmo eficiente para Predição de Estruturas de Proteínas livre de tendência foi desenvolvido um Algoritmo de Estimação de Distribuição (EDA) específico para esse problema, com modelagens full-atom e algoritmos ab initio. O fato do algoritmo proposto ser ab initio é mais interessante para aplicação envolvendo proteínas com baixa similaridade, com relação às estruturas já conhecidas. Três tipos de modelos probabilísticos foram desenvolvidos: univariado, bivariado e hierárquico. O univariado trata o aspecto de multi-modalidade de uma variável, o bivariado trata os ângulos diedrais (Φ Ψ) de um mesmo aminoácido como variáveis correlacionadas. O hierárquico divide o problema em subproblemas e tenta tratá-los separadamente. Os resultados desta pesquisa mostraram que é possível obter melhores resultados quando considerado a relação bivariada (Φ Ψ). O hierárquico também mostrou melhorias nos resultados obtidos, principalmente para proteínas com mais de 50 resíduos. Além disso, foi realiza uma comparação com algumas heurísticas da literatura, como: Busca Aleatória, Monte Carlo, Algoritmo Genético e Evolução Diferencial. Os resultados mostraram que mesmo uma metaheurística pouco eficiente, como a Busca Aleatória, pode encontrar a solução correta, porém utilizando muito conhecimento a priori (predição que pode ser tendenciosa). Por outro lado, o algoritmo proposto neste trabalho foi capaz de obter a estrutura da proteína esperada sem utilizar conhecimento a priori, caracterizando uma predição puramente ab initio (livre de tendência).
Proteins are molecules that perform critical roles in the living organism and they are essential for their lifes. To understand the function of a protein, its 3D structure should be known. However, to find the protein structure is an expensive and a time-consuming task, requiring highly skilled professionals. Aiming to overcome such a limitation, computational methods for Protein Structure Prediction (PSP) have been investigated, in order to predict the protein structure from its amino acid sequence. Most of computational methods require knowledge from already determined structures from experimental methods in order to predict an unknown protein. Although computational methods such as Rosetta, I-Tasser and Quark have showed success in their predictions, they are only capable to predict quite similar structures to already known proteins obtained experimentally. The use of such a prior knowledge in the predictions of Rosetta, I-Tasser and Quark may lead to biased predictions. In order to develop a computational algorithm for PSP free of bias, we developed an Estimation of Distribution Algorithm applied to PSP with full-atom and ab initio model. A computational algorithm with ab initio model is mainly interesting when dealing with proteins with low similarity with the known proteins. In this work, we developed an Estimation of Distribution Algorithm with three probabilistic models: univariate, bivariate and hierarchical. The univariate deals with multi-modality of the distribution of the data of a single variable. The bivariate treats the dihedral angles (Proteins are molecules that perform critical roles in the living organism and they are essential for their lifes. To understand the function of a protein, its 3D structure should be known. However, to find the protein structure is an expensive and a time-consuming task, requiring highly skilled professionals. Aiming to overcome such a limitation, computational methods for Protein Structure Prediction (PSP) have been investigated, in order to predict the protein structure from its amino acid sequence. Most of computational methods require knowledge from already determined structures from experimental methods in order to predict an unknown protein. Although computational methods such as Rosetta, I-Tasser and Quark have showed success in their predictions, they are only capable to predict quite similar structures to already known proteins obtained experimentally. The use of such a prior knowledge in the predictions of Rosetta, I-Tasser and Quark may lead to biased predictions. In order to develop a computational algorithm for PSP free of bias, we developed an Estimation of Distribution Algorithm applied to PSP with full-atom and ab initio model. A computational algorithm with ab initio model is mainly interesting when dealing with proteins with low similarity with the known proteins. In this work, we developed an Estimation of Distribution Algorithm with three probabilistic models: univariate, bivariate and hierarchical. The univariate deals with multi-modality of the distribution of the data of a single variable. The bivariate treats the dihedral angles (Φ Ψ) within an amino acid as correlated variables. The hierarchical approach splits the original problem into subproblems and attempts to treat these problems in a separated manner. The experiments show that, indeed, it is possible to achieve better results when modeling the correlation (Φ Ψ). The hierarchical model also showed that is possible to improve the quality of results, mainly for proteins above 50 residues. Besides, we compared our proposed techniques among other metaheuristics from literatures such as: Random Walk, Monte Carlo, Genetic Algorithm and Differential Evolution. The results show that even a less efficient metaheuristic such as Random Walk managed to find the correct structure, however using many prior knowledge (prediction that may be biased). On the other hand, our proposed EDA for PSP was able to find the correct structure with no prior knowledge at all, so we can call this prediction as pure ab initio (biased-free).

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Lv, Duchao. « A Multi-Scale Simulation Approach to Deformation Mechanism Prediction in Superalloys ». The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1469009668.

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Čančarević, Željko P. « Prediction of not-yet-synthesized solids at extreme pressures, and the development of algorithms for local optimization on ab-initio level ». [S.l. : s.n.], 2006. http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-28944.

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Faccioli, Rodrigo Antonio. « Implementação de um framework de computação evolutiva multi-objetivo para predição Ab Initio da estrutura terciária de proteínas ». Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-09052013-145839/.

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A demanda criada pelos estudos biológicos resultou para predição da estrutura terciária de proteínas ser uma alternativa, uma vez que menos de 1% das sequências conhecidas possuem sua estrutura terciária determinada experimentalmente. As predições Ab initio foca nas funções baseadas da física, a qual se trata apenas das informações providas pela sequência primária. Por consequência, um espaço de busca com muitos mínimos locais ótimos deve ser pesquisado. Este cenário complexo evidencia uma carência de algoritmos eficientes para este espaço, tornando-se assim o principal obstáculo para este tipo de predição. A optimização Multi-Objetiva, principalmente os Algoritmos Evolutivos, vem sendo aplicados na predição da estrutura terciária já que na mesma se envolve um compromisso entre os objetivos. Este trabalho apresenta o framework ProtPred-PEO-GROMACS, ou simplesmente 3PG, que não somente faz predições com a mesma acurácia encontrada na literatura, mas também, permite investigar a predição por meio da manipulação de combinações de objetivos, tanto no aspecto energético quanto no estrutural. Além disso, o 3PG facilita a implementação de novas opções, métodos de análises e também novos algoritmos evolutivos. A fim de salientar a capacidade do 3PG, foi então discorrida uma comparação entre os algoritmos NSGA-II e SPEA2 aplicados na predição Ab initio da estrutura terciária de proteínas em seis combinações de objetivos. Ademais, o uso da técnica de refinamento por Dinâmica Molecular é avaliado. Os resultados foram adequados quando comparado com outras técnicas de predições: Algoritmos Evolutivo Multi-Objetivo, Replica Exchange Molecular Dynamics, PEP-FOLD e Folding@Home.
The demand created by biological studies resulted the structure prediction as an alternative, since less than 1% of the known protein primary sequences have their 3D structure experimentally determined. Ab initio predictions focus on physics-based functions, which regard only information about the primary sequence. As a consequence, a search space with several local optima must be sampled, leading to insucient sampling of this space, which is the main hindrance towards better predictions. Multi-Objective Optimization approaches, particularly the Evolutionary Algorithms, have been applied in protein structure prediction as it involves a compromise among conicting objectives. In this paper we present the ProtPred-PEO-GROMACS framework, or 3PG, which can not only make protein structure predictions with the same accuracy standards as those found in the literature, but also allows the study of protein structures by handling several energetic and structural objective combinations. Moreover, the 3PG framework facilitates the fast implementation of new objective options, method analysis and even new evolutionary algorithms. In this study, we perform a comparison between the NSGA-II and SPEA2 algorithms applied on six dierent combinations of objectives to the protein structure. Besides, the use of Molecular Dynamics simulations as a renement technique is assessed. The results were suitable when comparated with other prediction methodologies, such as: Multi-Objective Evolutionary Algorithms, Replica Exchange Molecular Dynamics, PEP-FOLD and Folding@Home.

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Kashiwabara, André Yoshiaki. « MYOP/ToPS/SGEval : Um ambiente computacional para estudo sistemático de predição de genes ». Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-02042012-184145/.

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O desafio de encontrar corretamente genes eucarioticos codificadores de proteinas nas sequencias genomicas e um problema em aberto. Neste trabalho, implementamos uma plata- forma, com o objetivo de melhorar a forma com que preditores de genes sao implementados e avaliados. Tres novas ferramentas foram implementadas: ToPS (Toolkit of Probabilistic Models of Sequences) foi o primeiro arcabouco orientado a objetos que fornece ferramentas para implementacao, manipulacao, e combinacao de modelos probabilisticos para representar sequencias de simbolos; MYOP (Make Your Own Predictor) e um sistema que tem como objetivo facilitar a construcao de preditores de genes; e SGEval utiliza grafos de splicing para comparar diferente anotacoes com eventos de splicing alternativos. Utilizamos nossas ferramentas para o desenvolvimentos de preditores de genes em onze genomas distintos: A. thaliana, C. elegans, Z. mays, P. falciparum, D. melanogaster, D. rerio, M. musculus, R. norvegicus, O. sativa, G. max e H. sapiens. Com esse desenvolvimento, estabelecemos um protocolo para implementacao de novos preditores. Alem disso, utilizando a nossa plata- forma, desenvolvemos um fluxo de trabalho para predicao de genes no projeto do genoma da cana de acucar, que ja foi utilizado em 109 sequencias de BAC geradas pelo BIOEN (FAPESP Bioenergy Program).
The challenge of correctly identify eukaryotic protein-coding genes in the genomic se- quences is an open problem. In this work, we implemented a plataform with the aim of improving the way that gene predictors are implemented and evaluated. ToPS (Toolkit of Probabilistic Models of Sequence) was the first object-oriented framework that provides tools for implementation, manipulation, and combination of probabilistic models that represent sequences of symbols. MYOP (Make Your Own Predictor) facilitates the construction of gene predictors. SGEval (Splicing Graph Evaluation) uses splicing graphs to compare dif- ferent annotations with alternative splicing events. We used our plataform to develop gene finders in eleven distinct genomes: A. thaliana, C. elegans, Z. mays, P. falciparum, D. me- lanogaster, D. rerio, M. musculus, R. norvegicus, O. sativa, G. max e H. sapiens. With this development, we established a protocol for implementing new gene predictors. In addi- tion, using our platform, we developed a pipeline to find genes in the 109 sugarcane BAC sequences produced by BIOEN (FAPESP Bioenergy Program).

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Bonetti, Daniel Rodrigo Ferraz. « Aumento da eficiência do cálculo da energia de van der Waals em algoritmos genéticos para predição de estruturas de proteínas ». Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-20052010-110415/.

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As proteínas são moléculas presentes nos seres vivos e essenciais para a vida deles. Para entender a função de uma proteína, devese conhecer sua estrutura tridimensional (o posicionamento correto de todos os seus átomos no espaço). A partir da estrutura de uma proteína vital de um organismo causador de uma doença é possível desenvolver fármacos para o tratamento da doença. Para encontrar a estrutura de uma proteína, métodos biofísicos, como Cristalografia de Raio-X e Ressonância Nuclear Magnética têm sido empregados. No entanto, o uso desses métodos tem restrições práticas que impedem a determinação de várias estruturas de proteínas. Para contornar essas limitações, métodos computacionais para o problema de predição da estrutura da proteína (PSP, Protein Structure Prediction) têm sido investigados. Várias classes de métodos computacionais têm sido desenvolvidas para o problema de PSP. Entre elas, as abordagens ab initio são muito importantes, pois não utilizam nenhuma informação prévia de outras estruturas de proteínas para fazer o PSP, apenas a sequência de aminoácidos da proteína e o gráfico de Ramachandran são empregados. O PSP ab initio é um problema combinatorial que envolve relativamente grandes instâncias na prática, por exemplo, as proteínas em geral têm centenas ou milhares de variáveis para determinar. Para vencer esse entrave, metaheurísticas como os Algoritmos Genéticos (AGs) têm sido investigados. As soluções geradas por um AG são avaliadas pelo cálculo da energia potencial da proteína. Entre elas, o cálculo da interação da energia de van der Waals é custoso computacionalmente tornando o processo evolutivo do AG muito lento mesmo para proteínas pequenas. Este trabalho investiga técnicas para reduzir significativamente o tempo de execução desse cálculo. Basicamente, foram propostas modificações de técnicas de paralelização utilizando MPI e OpenMP para os algoritmos resultantes. Os resultados mostram que o cálculo pode ser 1.500 vezes mais rápido para proteínas gigantes quando aplicadas as técnicas investigadas neste trabalho
Proteins are molecules present in the living organism and essential for their life. To understand the function of a protein, its threedimensional structure (the correct positions of all its atoms in the space) should be known. From the structure of a vital protein of an organism that causes a human disease, it is possible to develop medicines for treatment of the disease. To find a protein structure, biophysical methods, as Crystallography of X-Ray and Magnetic Nuclear Resonance, have been employed. However, the use of those methods have practical restrictions that impede the determination of several protein structures. Aiming to overcome such limitation, computational methods for the problem of protein structure prediction (PSP) has been investigated. Several classes of computational methods have been developed for PSP. Among them, ab initio approaches are very important since they use no previous information from other protein structure, only the sequence of amino acids of the protein and the Ramachandran graph are employed. The ab initio PSP is a combinatorial problem that involves relatively large instances in practice, i. e. proteins in general have hundreds or thousands of variables to be determined. To deal with such problem, metaheuristics as Genetic Algorithms (GAs) have been investigated. The solutions generated by a GA are evaluated by the calculus of the potencial energies of the protein. Among them, the calculation of the interaction of van der Waals energy is computationally intense making the evolutionary process of a GA very slow even for non-large proteins. This work investigated techniques to significantly reduce the running time for that calculus. Basically, we proposed modifications parallelization of the resultant algorithm using MPI and OpenMP techniques. The results show that such calculus can be 1.500 times faster when applying the techniques investigated in this work for large proteins

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Tuček, Jaroslav. « Předpovídání struktury proteinů ». Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236732.

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This work describes the three dimensional structure of protein molecules and biological databases used to store information about this structure or its hierarchical classification. Current methods of computational structure prediction are overviewed with an emphasis on comparative modeling. This particular method is also implemented in a proof-of-concept program and finally, the implementation is analysed.

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Kulkarni, Aniket [Verfasser], et Martin [Akademischer Betreuer] Jansen. « Structure prediction of lithium, calcium carbide, and (per)nitride compounds at ambient and high pressure on the ab-initio level / Aniket Kulkarni. Betreuer : Martin Jansen ». Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2012. http://d-nb.info/102604328X/34.

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Harding, Alexander. « The prediction of mutagenicity and pKa for pharmaceutically relevant compounds using 'quantum chemical topology' descriptors ». Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/the-prediction-of-mutagenicity-and-pka-for-pharmaceutically-relevant-compounds-using-quantum-chemical-topology-descriptors(40e87ff2-e161-4f4c-9e90-3a4e9087dc9e).html.

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Quantum Chemical Topology (QCT) descriptors, calculated from ab initio wave functions, have been utilised to model pKa and mutagenicity for data sets of pharmaceutically relevant compounds. The pKa of a compound is a pivotal property in both life science and chemistry since the propensity of a compound to donate or accept a proton is fundamental to understanding chemical and biological processes. The prediction of mutagenicity, specifically as determined by the Ames test, is important to aid medicinal chemists select compounds avoiding this potential pitfall in drug design. Carbocyclic and heterocyclic aromatic amines were chosen because this compounds class is synthetically very useful but also prone to positive outcomes in the battery of genotoxicity assays.The importance of pKa and genotoxic characteristics cannot be overestimated in drug design, where the multivariate optimisations of properties that influence the Absorption-Distribution-Metabolism-Excretion-Toxicity (ADMET) profiles now features very early on in the drug discovery process.Models were constructed using carboxylic acids in conjunction with the Quantum Topological Molecular Similarity (QTMS) method. The models produced Root Mean Square Error of Prediction (RMSEP) values of less than 0.5 pKa units and compared favourably to other pKa prediction methods. The ortho-substituted benzoic acids had the largest RMSEP which was significantly improved by splitting the compounds into high-correlation subsets. For these subsets, single-term equations containing one ab initio bond length were able to accurately predict pKa. The pKa prediction equations were extended to phenols and anilines.Quantitative Structure Activity Relationship (QSAR) models of acceptable quality were built based on literature data to predict the mutagenic potency (LogMP) of carbo- and heterocyclic aromatic amines using QTMS. However, these models failed to predict Ames test values for compounds screened at GSK. Contradictory internal and external data for several compounds motivated us to determine the fidelity of the Ames test for this compound class. The systematic investigation involved recrystallisation to purify compounds, analytical methods to measure the purity and finally comparative Ames testing. Unexpectedly, the Ames test results were very reproducible when 14 representative repurified molecules were tested as the freebase and the hydrochloride salt in two different solvents (water and DMSO). This work formed the basis for the analysis of Ames data at GSK and a systematic Ames testing programme for aromatic amines. So far, an unprecedentedly large list of 400 compounds has been made available to guide medicinal chemists. We constructed a model for the subset of 100 meta-/para-substituted anilines that could predict 70% of the Ames classifications. The experimental values of several of the model outliers appeared questionable after closer inspection and three of these have been retested so far. The retests lead to the reclassification of two of them and thereby to improved model accuracy of 78%. This demonstrates the power of the iterative process of model building, critical analysis of experimental data, retesting outliers and rebuilding the model.

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Bohonak, Noni McCullough. « Finding a Fitness Function to be Used with Genetic Algorithms to Solve a Protein Folding Problem : The ab initio Prediction of a Protein Using Torsion Angles ». NSUWorks, 2000. http://nsuworks.nova.edu/gscis_etd/418.

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This dissertation shows that the ab initio prediction of a protein using torsion angles will work using the correct fitness function. It shows that work can be done on a high-end workstation using a small model of a protein. It was based on the previous work of Dr. Steffen Schulze-Kremer who received limited success with a faculty fitness function and a massively parallel system. The purpose of this work was to not only find the solution but to demonstrate how our rapidly advancing technology will permit this type of research to be moved from the costly parallel systems, nuclear magnetic resonance, and x-ray crystallography to a less costly microcomputer system. In order to accomplish this, the code was run with Microsoft's Visual C++ (version 6) on Intel systems running at 220 MHz, 550 MHz, and 700 MHz with 40 MB, 512 MB, and 256 MB of memory. The results of this work will pave the way for further research in this area on less costly hardware.

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Flores, Livas José. « Computational and experimental studies of sp3-materials at high pressure ». Thesis, Lyon 1, 2012. http://www.theses.fr/2012LYO10127.

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Nous présentons des études expérimentales et théoriques de disiliciures alcalino-terreux, le disilane (Si2H6) et du carbone à haute pression. Nous étudions les disiliciures et en particulier le cas d’une phase plane de BaSI2 qui a une structure hexagonale avec des liaisons sp3 entre les atomes de silicium. Cet environnement électronique conduit à un gaufrage de feuilles du silicium. Nous démontrons alors une amélioration de la température de transition supraconductrice de 6 à 8.9 K lorsque les couches de silicium s’aplanissent dans cette structure. Des calculs ab initio basés sur DFT ont guidé la recherche expérimentale et permettent d’expliquer comment les propriétés électroniques et des phonons sont fortement affectés par les fluctuations du flambage des plans de silicium. Nous avons aussi étudié les phases cristallines de disilane à très haute pression et une nouvelle phase métallique est proposé en utilisant les méthodes de prédiction de structure cristalline. Les températures de transition calculées donnant un supraconducteur autour de 20 K à 100 GPa. Ces valeurs sont significativement plus faibles comparées à celles avancées dans la littérature. Finalement, nous présentons des études de structures de carbone à haute pression à travers une recherche de structure systématique. Nous avons trouvé une nouvelle forme allotropique du carbone avec une symétrie Cmmm que nous appelons Z-carbone. Cette phase est prévue pour être plus stable que le graphite pour des pressions supérieures à 10 GPa. Des expériences et simulation de rayon-X et spectre Raman sugèrent l’existence de Z-carbone dans des micro-domaines de graphite sous pression
We present experimental and theoretical studies of sp3 materials, alkaline-earth-metal (AEM) disilicides, disilane (Si2H6) and carbon at high pressure. First, we study the AEM disilicides and in particular the case of a layered phase of BaSi2 which has an hexagonal structure with sp3 bonding of the silicon atoms. This electronic environment leads to a natural corrugated Si-sheets. Extensive ab initio calculations based on DFT guided the experimental research and permit explain how electronic and phonon properties are strongly affected by changes in the buckling of the silicon plans. We demonstrate experimentally and theoretically an enhancement of superconducting transition temperatures from 6 to 8.9 K when silicon planes flatten out in this structure. Second, we investigated the crystal phases of disilane at the megabar range of pressure. A novel metallic phase of disilane is proposed by using crystal structure prediction methods. The calculated transition temperatures yielding a superconducting Tc of around 20 K at 100 GPa and decreasing to 13 K at 220 GPa. These values are significantly smaller than previously predicted Tc’s and put serious drawbacks in the possibility of high-Tc superconductivity based on silicon-hydrogen systems. Third, we studied the sp3-carbon structures at high pressure through a systematic structure search. We found a new allotrope of carbon with Cmmm symmetry which we refer to as Z-carbon. This phase is predicted to be more stable than graphite for pressures above 10 GPa and is formed by sp3-bonds. Experimental and simulated XRD, Raman spectra suggest the existence of Z-carbon in micro-domains of graphite under pressure

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Yao, Yongxin. « Thermodynamic prediction of glass formation tendency, cluster-in-jellium model for metallic glasses, ab initio tight-binding calculations, and new density functional theory development for systems with strong electron correlation ». [Ames, Iowa : Iowa State University], 2009.

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Asthana, Abhishek. « Model Development and Application of Molecular Simulations for the Study of Proton Transport in Bulk Water and for the Prediction of Dipole Moments of Organic Compounds ». BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3389.

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The present work demonstrates the application of molecular simulations (MD) in two different areas: proton transport in bulk water and estimation of the dipole moment of polar organic compounds. In both areas, relatively few successful and robust methodologies exist. In the first part, a new polarizable water model is developed for MD simulations of the proton transport process. The model was parametrized from a combination of quantum chemical calculations and experimental water properties. The model was implemented in MD simulation studies of liquid water at room temperature, as well as with excess protons. For pure water the model gave good agreement with experimental properties. The proton transport rate for a single excess proton also gave a good match with the experimental value. The water model was further extended to include chloride ions. At 0.2 M concentration the resulting density and structure agreed well with experiment, and the proton transport rate was found to be slightly reduced. The model was further extended to include multiple excess protons. For the second part of the project, an open source ab initio MD program, SIESTA, was used to perform simulations of several organic compounds which potentially have multiple stable conformations, to determine their average dipole moments. A series of methods was developed. The most robust method involved modifications to the SIESTA code and statistical analysis of the resulting configurations, in order to more accurately predict the average dipole moment. The resulting dipole moments were in good agreement with the experimental values for cases in which experimental values were reliable. Based on this study, a general method to estimate the average dipole moment of any compound is proposed.

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Mishra, Avdesh. « Effective Statistical Energy Function Based Protein Un/Structure Prediction ». ScholarWorks@UNO, 2019. https://scholarworks.uno.edu/td/2674.

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Proteins are an important component of living organisms, composed of one or more polypeptide chains, each containing hundreds or even thousands of amino acids of 20 standard types. The structure of a protein from the sequence determines crucial functions of proteins such as initiating metabolic reactions, DNA replication, cell signaling, and transporting molecules. In the past, proteins were considered to always have a well-defined stable shape (structured proteins), however, it has recently been shown that there exist intrinsically disordered proteins (IDPs), which lack a fixed or ordered 3D structure, have dynamic characteristics and therefore, exist in multiple states. Based on this, we extend the mapping of protein sequence not only to a fixed stable structure but also to an ensemble of protein conformations, which help us explain the complex interaction within a cell that was otherwise obscured. The objective of this dissertation is to develop effective ab initio methods and tools for protein un/structure prediction by developing effective statistical energy function, conformational search method, and disulfide connectivity patterns predictor. The key outcomes of this dissertation research are: i) a sequence and structure-based energy function for structured proteins that includes energetic terms extracted from hydrophobic-hydrophilic properties, accessible surface area, torsion angles, and ubiquitously computed dihedral angles uPhi and uPsi, ii) an ab initio protein structure predictor that combines optimal energy function derived from sequence and structure-based properties of proteins and an effective conformational search method which includes angular rotation and segment translation strategies, iii) an SVM with RBF kernel-based framework to predict disulfide connectivity pattern, iv) a hydrophobic-hydrophilic property based energy function for unstructured proteins, and v) an ab initio conformational ensemble generator that combines energy function and conformational search method for unstructured proteins which can help understand the biological systems involving IDPs and assist in rational drugs design to cure critical diseases such as cancer or cardiovascular diseases caused by challenging states of IDPs.

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Lefèvre, Gauthier. « Propriétés physico-chimiques de nouveaux matériaux en couches minces pour le stockage d'hydrogène ». Thesis, Artois, 2018. http://www.theses.fr/2018ARTO0406.

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Le stockage d'hydrogène est sans doute le dernier verrou au développement à grande échelle des piles à combustible. Utiliser l'hydrogène comme vecteur énergétique, produire efficacement de l'électricité sans avoir recours aux énergies fossiles et rejeter uniquement de l'eau, il s'agit là peut-être de la prochaine révolution technologique, écologique et qui signera la fin des problèmes environnementaux en terme d'énergie.L'hydrogène gazeux est dangereux et son stockage à l'état solide représente une solution mais au détriment de la quantité stockée et des conditions d'utilisation.Dans ce contexte, la recherche de nouveaux matériaux avec des propriétés physico-chimiques nouvelles est souhaitable.Cette thèse s’inscrit dans cette démarche d'investigation : d'une part mettre en oeuvre et utiliser de nouvelles techniques de recherche structurale théorique pour explorer les possibilités qu'offrent les alliages métalliques ; ensuite entreprendre la synthèse de couches minces de métaux et d'alliages au moyen de l’ablation laser pulsé pour bénéficier des atouts de cette méthode.L'étude théorique menée au cours de cette thèse a permis de montrer l'impact des contraintes de pression sur la formation et la stabilité d'alliages dans de nombreux systèmes binaires. Des pistes sur l'hydrogénation possible de nouvelles structures ont également été présentées.D'autre part, l'adversité de l'ablation laser pulsé pour la synthèse de couches minces a été mise en lumière et de grandes disparités dans les conditions de dépôts sont à déplorer. Cette méthode permet de parvenir à des morphologies singulières, ouvrant ainsi à des perspectives dans la conception de ces nouveaux matériaux
Hydrogen storage is probably the last lock facing the development of fuel cells system.Hydrogen is a non-harmful, non-polluting that can be used as an energy vector, allowing to produce fossil fuel free electricity efficiently and releasing only water.It could trigger the next technological and green revolution, marking the end of environmental concerns related to energy.Hydrogen is the most energetic gas. These double-edged caracteristics makes it attractive and unsafe at the same time. Solid state storage can be seen as a solution in spite of a moderate hydrogen uptake and a poor desorption process.In this context, research of new materials with enhanced physico-chemical properties is desirable and represent the aim of this work.This thesis is an investigation study. On the one hand, with the help of efficient theoretical structural prediction systems, an exploration of the infinite possibilities offered by metal alloys has been performed. On the other hand, pulsed laser deposition of metal thin films has been implemented to make use of its benefits.The present theoretical study has highlighted the influence of external strains on stability and emergence of alloys in numerous binary systems. In addition, a search for potential hydrides was carried out. Informations obtained are encouraging the use of similar prediction schemes in order to identify new systems.From metallic thin films made by pulsed laser ablation, deposition difficulties and disparities in procedures have been put forward. Nonetheless, singular morphologies have been achieved by this process, opening new insights for designing novel materials

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Votroubek, Lukáš. « Webový server pro predikci 3D struktury proteinu ». Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236225.

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This work deals with proteins, especially with their structure and kinds of tertiary, or 3D, structure prediction. Tertiary structure prediction is very important for function prediction of this vitally important substance. Bioinformatics do this prediction much more effective and faster, because classical methods of structure prediction directly from molecule are very expensive and slow. On the other hand they are much more exact. Objective of this thesis is to describe tertiary structure prediction methods, describe used tools and possibility of automatic communication with them. Next objective is describe implementation of server, that will serve to protein engineers for more effective finding of information about tertiary structure from more servers without requesting each of them separately. Results of testing will be described in this work too.

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Mooßen, Oliver [Verfasser], Michael [Gutachter] Dolg et Michael [Gutachter] Hanrath. « Interpretation of ab initio Calculations of Cerium Compounds and Predictive Power of Density Functional Theory Calculations for Iodine Catalysis / Oliver Mooßen ; Gutachter : Michael Dolg, Michael Hanrath ». Köln : Universitäts- und Stadtbibliothek Köln, 2018. http://d-nb.info/1162273518/34.

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Joubert, Pierre. « Inhomogeneites dues a la dependance en vitesse de la largeur et du deplacement collisionnels de h#2 et hf. Calculs ab initio et predictions des profils spectraux a haute temperature ». Besançon, 1997. http://www.theses.fr/1997BESA2045.

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Ce travail est consacre a la comprehension des differents mecanismes collisionnels affectant les profils spectraux dans le cadre du diagnostic optique des milieux en combustion. Cette these est plus particulierement consacree a la determination des largeurs et deplacements des raies de h#2 et hf dans divers melanges (avec he, ar et n#2). Celle-ci est effectuee a partir de l'experience par ajustement avec des modeles cinetiques ou par des calculs ab initio a partir du potentiel intermoleculaire. Les spectres de hf perturbe par l'argon obtenus a basse pression presentent une asymetrie. Afin d'interpreter ces anomalies, un modele cinetique incluant une correlation partielle entre les differentes collisions mises en jeu est developpe. A plus haute densite, les effets collisionnels sont dominants. Pour les raies rovibrationnelles q(j) de h#2 en presence de perturbateurs lourds, des asymetries sont observees ainsi qu'une non-linearite de la largeur observee en fonction de la concentration. Ces anomalies sont interpretees a l'aide d'un modele incluant la dependance de l'elargissement et du deplacement collisionnel avec le module de la vitesse v de la molecule active. L'influence de ces effets a ete montree etre negligeable pour le couple hf-ar. Une etude complete pour un systeme test, h#2 perturbe par l'helium, a ete realisee. Pour ce systeme, aucun effet de vitesses n'est attendu meme a haute temperature. A partir d'une surface de potentiel tres precise, des calculs ab initio bases sur la theorie du profil spectral est possible en utilisant les methodes semiclassique et quantique. Ces calculs sont compares aux donnees experimentales pour les raies q(j) et s#0(j) entre 300 et 1000 k. L'accord etant tres satisfaisant, une extension jusqu'a 3000 k a ete effectuee avec fiabilite par les deux approches theoriques. A ces hautes temperatures, la methode semiclassique a ete validee pour l'etude de systemes plus complexes intervenant dans les milieux en combustion pour lesquels le calcul purement quantique n'est pas envisageable. Des travaux complementaires sur la determination des parametres de raies sont presentes. Ils portent sur une reecriture de la description de la trajectoire dans l'approche semiclassique et sur l'etude de donnees experimentales de la raie de rotation pure s#0(3) de h#2 perturbe par des gaz rares.

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Prascher, Brian P. « Systematic Approaches to Predictive Computational Chemistry using the Correlation Consistent Basis Sets ». Thesis, University of North Texas, 2009. https://digital.library.unt.edu/ark:/67531/metadc9920/.

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The development of the correlation consistent basis sets, cc-pVnZ (where n = D, T, Q, etc.) have allowed for the systematic elucidation of the intrinsic accuracy of ab initio quantum chemical methods. In density functional theory (DFT), where the cc-pVnZ basis sets are not necessarily optimal in their current form, the elucidation of the intrinsic accuracy of DFT methods cannot always be accomplished. This dissertation outlines investigations into the basis set requirements for DFT and how the intrinsic accuracy of DFT methods may be determined with a prescription involving recontraction of the cc-pVnZ basis sets for specific density functionals. Next, the development and benchmarks of a set of cc-pVnZ basis sets designed for the s-block atoms lithium, beryllium, sodium, and magnesium are presented. Computed atomic and molecular properties agree well with reliable experimental data, demonstrating the accuracy of these new s-block basis sets. In addition to the development of cc-pVnZ basis sets, the development of a new, efficient formulism of the correlation consistent Composite Approach (ccCA) using the resolution of the identity (RI) approximation is employed. The new formulism, denoted 'RI-ccCA,' has marked efficiency in terms of computational time and storage, compared with the ccCA formulism, without the introduction of significant error. Finally, this dissertation reports three separate investigations of the properties of FOOF-like, germanium arsenide, and silicon hydride/halide molecules using high accuracy ab initio methods and the cc-pVnZ basis sets.

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Aminpour, Maral. « Theoretical Studies of Nanostructure Formation and Transport on Surfaces ». Doctoral diss., University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/6239.

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This dissertation undertakes theoretical and computational research to characterize and understand in detail atomic configurations and electronic structural properties of surfaces and interfaces at the nano-scale, with particular emphasis on identifying the factors that control atomic-scale diffusion and transport properties. The overarching goal is to outline, with examples, a predictive modeling procedure of stable structures of novel materials that, on the one hand, facilitates a better understanding of experimental results, and on the other hand, provide guidelines for future experimental work. The results of this dissertation are useful in future miniaturization of electronic devices, predicting and engineering functional novel nanostructures. A variety of theoretical and computational tools with different degrees of accuracy is used to study problems in different time and length scales. Interactions between the atoms are derived using both ab-initio methods based on Density Functional Theory (DFT), as well as semi-empirical approaches such as those embodied in the Embedded Atom Method (EAM), depending on the scale of the problem at hand. The energetics for a variety of surface phenomena (adsorption, desorption, diffusion, and reactions) are calculated using either DFT or EAM, as feasible. For simulating dynamic processes such as diffusion of ad-atoms on surfaces with dislocations the Molecular Dynamics (MD) method is applied. To calculate vibrational mode frequencies, the infinitesimal displacement method is employed. The combination of non-equilibrium Green's function (NEGF) and DFT is used to calculate electronic transport properties of molecular devices as well as interfaces and junctions.
Ph.D.
Doctorate
Physics
Sciences
Physics

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Tran, Thuong Van Du. « Modeling and predicting super-secondary structures of transmembrane beta-barrel proteins ». Phd thesis, Ecole Polytechnique X, 2011. http://tel.archives-ouvertes.fr/tel-00647947.

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Les protéines transmembranaires canaux-β (TMBs) se trouvent dans les membranes externes des bactéries à Gram négatif, des mitochondries ainsi que des chloroplastes. Elles traversent entièrement la membrane cellulaire et exercent différentes fonctions importantes. Vu qu'il y a un petit nombre des structures des TMBs déterminées, en raison des difficultés avec les méthodes expérimentales, il est douteux que ces approches puis- sent bien trouver et prédire les TMBs qui ne sont pas homologues avec celles connues. Nous construisons un modèle de graphe pour la classification et la prédiction de structures super-secondaires permutées des TMBs à partir de leur séquence d'acides aminés, en se basant sur la minimisation d'énergie. Le modèle ne dépend essentiellement pas de l'apprentissage. Les algorithmes sont rapides, robustes avec des performances com- parables à celles des meilleures méthodes actuelles qui utilisent l'apprentissage. Cette méthode peut être donc utile pour le screening des génomes. Outre la performance de prédiction et de classification, cette étude donne une vue plus profonde de la structure des TMBs en tenant compte des contraintes physicochimiques des membranes biologiques. Les structures permutées prédites peuvent aussi aider à mieux comprendre le mécanisme du repliement des TMBs.

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« Ab initio methods for protein structure prediction ». Thesis, 2010. http://hdl.handle.net/1911/61957.

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Recent breakthroughs in DNA and protein sequencing have unlocked many secrets of molecular biology. A complete understanding of gene function, however, requires a protein structure in addition to its sequence. Modern protein structure determination methods such as NMR, cryo-EM and X-ray crystallography are woefully unable to keep pace with automated sequencing techniques, creating a serious gap between available sequences and structures. This thesis describes several ab initio computational methods designed in the near-term to facilitate structure determination experiments, and in the long-term goal to predict protein structure completely and reliably. First, VecFold is a novel method for predicting the global tertiary structure topologies of proteins. VecFold applies fragment assembly to construct structural models from a target sequence by folding a chain of predicted secondary structure elements; these elements are represented either as Calpha-based rigid bodies or as vectors. The knowledge-based energy function OPUS-Ca or a knowledge-based geometric packing potential is used to guide the folding process. The newest version of VecFold is demonstrated to modestly outperform Rosetta, one of the leading ab initio predictors, on the CASP8 benchmark set. In our protein domain boundary prediction method OPUS-Dom, VecFold generates a large ensemble of folded structure models, and the domain boundaries of each model are labeled by a domain parsing algorithm. OPUS-Dom then derives consensus domain boundaries from the statistical distribution of the putative boundaries; the original version is also aided by three empirical sequence-based domain profiles. The latest version of OPUS-Dom outperformed, in terms of prediction sensitivity, several state-of-the-art domain prediction algorithms over various multi-domain protein sets. Even though many VecFold-generated structures contain large errors, collectively these structures provide a more robust delineation of domain boundaries. The success of OPUS-Dom suggests that the arrangement of protein domains is more a consequence of limited coordination patterns per domain arising from tertiary packing of secondary structure segments, rather than sequence-specific constraints. Finally, the knowledge-based energy function OPUS-Core was applied to the problem of protein folding core prediction, and it was shown to outpredict two leading computational methods on a benchmark set of 29 well-characterized protein targets.

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Li, Shuai Cheng. « New Approaches to Protein Structure Prediction ». Thesis, 2009. http://hdl.handle.net/10012/4846.

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Protein structure prediction is concerned with the prediction of a protein's three dimensional structure from its amino acid sequence. Such predictions are commonly performed by searching the possible structures and evaluating each structure by using some scoring function. If it is assumed that the target protein structure resembles the structure of a known protein, the search space can be significantly reduced. Such an approach is referred to as comparative structure prediction. When such an assumption is not made, the approach is known as ab initio structure prediction. There are several difficulties in devising efficient searches or in computing the scoring function. Many of these problems have ready solutions from known mathematical methods. However, the problems that are yet unsolved have hindered structure prediction methods from more ideal predictions. The objective of this study is to present a complete framework for ab initio protein structure prediction. To achieve this, a new search strategy is proposed, and better techniques are devised for computing the known scoring functions. Some of the remaining problems in protein structure prediction are revisited. Several of them are shown to be intractable. In many of these cases, approximation methods are suggested as alternative solutions. The primary issues addressed in this thesis are concerned with local structures prediction, structure assembly or sampling, side chain packing, model comparison, and structural alignment. For brevity, we do not elaborate on these problems here; a concise introduction is given in the first section of this thesis. Results from these studies prompted the development of several programs, forming a utility suite for ab initio protein structure prediction. Due to the general usefulness of these programs, some of them are released with open source licenses to benefit the community.

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Liao, Min-Hung, et 廖敏宏. « Constructing Structural Model of Protein Functional Domains using ab initio Structure Prediction ». Thesis, 2008. http://ndltd.ncl.edu.tw/handle/20715227811664794191.

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碩士
國立臺灣大學
生物產業機電工程學研究所
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The basic units of all kinds of organisms are proteins. In order to learn more about the protein function, scientists try to understand proteins from different points of view, including protein sequence, protein structure, location of functional residues, and so forth. In the fields of computational biology, the prediction of protein structure has always been the core issue that researchers indefatigably focus on. It has never been easy to tell the protein structure merely through protein sequence; hence methods for predicting the structures are being developed one after another, including approaches based on homology modeling, fold recognition, and ab initio. The issue of determining protein structure is equivalent to the identification of protein folding. Protein structure can be derived from experimental methods such as X-ray crystallography and NMR spectroscopy. Although the accuracy of the experimental methods is higher, their cost is relatively higher and more time consuming as well; researchers therefore come up with the idea of employing theoretical simulation to predict protein structure. The results of computational approaches may not be as precise as that of experimental methods, but some of them are fairly acceptable. For example, the accuracy of secondary structure prediction is about 80%, which provides valuable clues for biologists. This dissertation focuses mainly on how to determine the structure model of a protein functional domain. In the process of biological evolution, important regions are usually conserved. Our method is based on the information derived from three predictors, including the prediction of conserved residues, ordered regions, and domain boundaries. These characteristics of protein sequences are used as the basis for selecting initial functional regions for structure prediction. Furthermore, a structure comparison program is incorporated to evaluate the quality of the estimated region boundary, in order to increase the accuracy of the structure prediction. Results conducted in this thesis show that the proposed method is effectively in identifying the functional regions and delivering satisfied structure model for the proteins of interest.

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Pacheco, José Carlos Ribeiro. « PGP : prokaryote gene prediction software ». Master's thesis, 2013. http://hdl.handle.net/1822/27894.

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Dissertação de mestrado em Bioinformática
A correta previsão e anotação de genes bacterianos é essencial para a aplicação da informação contida no ADN em muitos tópicos de pesquisa (bio)médica, como microbiologia, imunologia e doenças infeciosas. Embora existam vários softwares de previsão de genes bacterianos como GenemarkHMM, Glimmer e Prodigal e pipelines completos como ISGA, xBASE, Maker e Consensus Prediction, a previsão de genes pode ser melhorada. O principal objetivo deste trabalho foi o desenvolvimento de um pipeline de previsão de genes bacterianos, o Prokaryote Gene Prediction (PGP), que combina métodos de ab initio e de homologia. Uma vez que o software ab initio Prodigal mostrou um melhor desempenho relativamente a outros softwares estudados, foi usado como o passo inicial para o PGP. Considerando as proteínas previstas pelo Prodigal, o PGP a) analisa os alinhamentos obtidos, b) determina a necessidade de encurtar ou estender genes, c) introduz as correções necessárias, d) faz a previsão de ARNr e ARNt utilizando os programas RNAmmer e tRNA-scan2 e e) determina a existência de eventuais genes não identificados nas regiões intergénicas, através de um BLASTx. Quando comparados os resultados do PGP com os dados produzidos pelo Prodigal utilizando 4 genomas com conteúdo G+C% moderado e 3 com conteúdo em G+C% extremo, o PGP apresentou melhorias de 1% tanto na taxa de erro como na especificidade, exibindo a mesma sensibilidade. Foi observado que para genomas com conteúdos G+C% extremos, o PGP tem mais impacto e portanto realiza mais correções. Os resultados do PGP ainda foram comparados com os pipelines ISGA, xBASE e Consensus Prediction. O PGP melhorou a previsão de genes corretos em 4,4%, comparativamente com ISGA e xBASE e ainda 3,1% em relação à previsão do Consensus Prediction, mantendo uma sensibilidade idêntica entre previsões. No que respeita à deteção de genes na região intergénica verificou-se um acréscimo na ordem de 9 falsos positivos em 12 genomas modelo, necessitando esta vertente de um melhor desenvolvimento. Concluiu-se que o PGP melhora a correta previsão de genes, especialmente em genomas bacterianos com conteúdos G+C% extremos, contribuindo para a anotação automática de genomas bacterianos de elevada qualidade.
The correct bacterial gene prediction and annotation is essential for the application of the information contained in DNA in several areas of (bio)medicine, like microbiology, immunology and infection diseases. Although there are several softwares to perform bacterial gene prediction, like GenemarkHMM, Glimmer and Prodigal and also full pipelines as ISGA, xBASE, Maker and Consensus Prediction, gene prediction can be improved. The main objective of this work was the development of a bacterial gene prevision pipeline, the Prokaryote Gene Prediction (PGP) which combines ab initio and homology methods. Since the ab initio software Prodigal showed a better performance relatively to others studied softwares, it was used as the beginning step for the PGP. Taking into account the proteins predicted by Prodigal, the PGP a) analyses the results of the alignment, b) determines if it is necessary to shorten or extend or extension of genes, c) introduces the necessary corrections, d) predictsrRNA and tRNA using the RNAmmer and tRNA-scan2 programs and e) determines possible missing genes in intergenics regions through BLASTx. When comparing the results of PGP with data produced by Prodigal, the PGP showed improvements in both the error rate, and in the specificity, while displaying the same sensitivity. For genomes with extreme G+C% content, the PGP has higher impact and therefore performs more corrections. The results obtained with PGP were also compared with ISGA, xBASE and Consensus Prediction pipelines. The PGP improved the precision of correct genes in 4,4%, comparatively with ISGA and xBASE and 3,1% relative to the prediction of Consensus Prediction, keeping a similar sensibility among predictions. As regards the detection of genes in the intergenic region there was an increase in the range of 9 false positive in 12 model genomes, requiring this part a better development. It was concluded that PGP improves the correct prediction of genes, especially in bacterial genomes with extreme G+C% content, contributing to a high quality in automatic bacterial gene annotation.

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Ngoc, Quoc-Vu Ha [Verfasser]. « Protein structure and enzyme catalysis : knowledge-based protein loop prediction and ab initio equilibrium constant estimation / vorgelegt von Quoc-Vu Ha Ngoc ». 2008. http://d-nb.info/989787516/34.

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Dal, Molin Alessandra. « Structural annotation of eukaryotic genomes in 2nd generation sequencing era ». Doctoral thesis, 2016. http://hdl.handle.net/11562/940817.

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Nell'ultimo decennio, l'aumento dell’efficienza e la diminuzione del costo delle nuove tecniche di sequenziamento ha portato ad un accumulo di sequenze genomiche nei database pubblici. Con questa enorme quantità di sequenze a disposizione, la necessità di generare delle annotazioni precise e dettagliate non è mai stata così grande. L’annotazione strutturale del genoma è il processo di identificazione di elementi strutturali in una sequenza di DNA, classificandoli in base al loro ruolo biologico. L’approccio computazionale viene sempre più utilizzato per poter eseguire l'annotazione strutturale in maniera automatica, con tempi di esecuzione generalmente brevi e che soddisfano le esigenze ‘high-throughput’ dei progetti di sequenziamento del genoma, anche se sono meno precisi rispetto alla cura manuale, che rimane il 'golden standard' per valutare l’affidabilità dell’annotazione prodotta.Lo scopo di questo progetto è quello di produrre un’annotazione del genoma in maniera veloce ed accurata, in linea con le esigenze attuali, mediante un approccio computazionale applicato a diversi casi sperimentali, in base alle conoscenze biologiche di base e alla natura dei dati di partenza. Per la completezza dello studio, il contributo dei vari metodi utilizzati per produrre l'annotazione finale è stato analizzato insieme alla valutazione della qualità dei dati prodotti.I risultati ottenuti hanno confermato il fatto che la complessità dei genomi eucariotici influisce notevolmente sul processo di annotazione. Una vasta porzione di geni può essere annotata grazie principalmente all’omologia con geni o proteine di altre specie evolutivamente vicine, oppure con l'utilizzo di predittori ab initio ed evidenze sperimentali specie-specifiche. L'integrazione di molteplici evidenze migliora notevolmente l'accuratezza delle annotazioni finali, tuttavia la valutazione della qualità dei risultati e il filtraggio di sequenze poco affidabili, insieme alla cura manuale, sono tuttora necessari per garantire un risultato ottimale.
In the last decade the increase in efficiency and decrease in cost of new sequencing techniques led to a growing amount of genomic sequences in publicdatabases. With this huge volume of sequences being generated from highthroughput sequencing projects, the requirement for providing accurate anddetailed genome annotations has never been greater. Structural genome annotation is the process of identifying structural features in a DNA sequence and classifying them based on their biological role. Computer programs are increasingly used to perform structural annotation since they meet the high-throughput demands of genome sequencing projects even if they are less accurate than manual gene annotation which remains the ‘golden-standard’ for evaluating annotation confidence and quality.The aim of this project is to meet the need of producing fast and accurate genome annotation by applying available computational means to different experimental cases, depending on the biological knowledge achieved so far and the quality of starting data. The contribution of different methods used to produce the final annotation has been analyzed along with the evaluation of results for the completeness of the study.The results obtained showed that the complexity of eukaryotic genomes greatly affects the annotation process; a big fraction of the genes in a genome sequence can be found mostly by homology to other known genes or proteins and by the use of ab initio predictors and species-specific evidence. The integration of multiple sources of annotation greatly improved the accuracy of the final genome annotations, anyway being not error free. Quality assessment of results and filtering of low confidence sequences together with manual revision are Always required to achieve higher accuracy.

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