Relevant bibliographies by topics / Computational docking

Academic literature on the topic 'Computational docking'

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Published: 14 March 2023

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Journal articles on the topic "Computational docking"

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Xing, Bo. "Computational Intelligence in Cross Docking." International Journal of Software Innovation 2, no. 1 (January 2014): 1–8. http://dx.doi.org/10.4018/ijsi.2014010101.

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Cross docking is a practice in logistics with the main operations of goods flow directly from receiving to the shipping docks without stopping or being put away into storage. It is a simple concept to talk about, but a challenging one to implement. So far, many different approaches have been followed in order to improve the efficiency of a cross docking system. However, as the complexity increases, the use of computational intelligence (CI) in those problems is becoming a unique tool of imperative value. In this paper, different CI methods, such as Tabu search, simulated annealing, genetic algorithm, and fuzzy logic. The key issues in implementing the proposed approaches are discussed, and finally the open questions are highlighted.
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Lengauer, Thomas, and Matthias Rarey. "Computational methods for biomolecular docking." Current Opinion in Structural Biology 6, no. 3 (June 1996): 402–6. http://dx.doi.org/10.1016/s0959-440x(96)80061-3.

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Khamis, Mohamed A., Walid Gomaa, and Walaa F. Ahmed. "Machine learning in computational docking." Artificial Intelligence in Medicine 63, no. 3 (March 2015): 135–52. http://dx.doi.org/10.1016/j.artmed.2015.02.002.

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Lee, Kyoungrim, and Joo-Woon Lee. "Computational Approaches to Protein-Protein Docking." Current Proteomics 5, no. 1 (April 1, 2008): 10–19. http://dx.doi.org/10.2174/157016408783955083.

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Hecht, David, and Gary Fogel. "Computational Intelligence Methods for Docking Scores." Current Computer Aided-Drug Design 5, no. 1 (March 1, 2009): 56–68. http://dx.doi.org/10.2174/157340909787580863.

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Al-hussaniy, Hany Akeel. "The development of molecular docking and molecular dynamics and their application in the field of chemistry and computer simulation." Journal of medical pharmaceutical and allied sciences 12, no. 1 (January 31, 2023): 5552–62. http://dx.doi.org/10.55522/jmpas.v12i1.4137.

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With the rapid development of modern life science, computational Molecular docking has gradually become one of the core disciplines and methods of modern life science research. Computational docking studies the relationship between the structure and pharmacodynamics of biological macromolecules and the interaction between biological macromolecules and ligands. It promotes the development of protein engineering, protein design, and computer-aided drug design with powerful and various docking software in predicting the three-dimensional structure and dynamic characteristics of proteins from protein sequences. Nowadays, this computing power can be provided by the GPU through the use of a general-purpose computing model on GPUs. This article presents two approaches to parallelizing the descriptive algorithms on the GPU to solve the molecular docking problem and then evaluating them in terms of the computation time achieved. The proposed approaches are effective in accelerating molecular docking on GPUs compared to a single-core or multicore CPU. Besides introducing parallelization approaches, we propose a new descriptive algorithm based on the bee swarm algorithm to solve the molecular docking problem as an alternative to traditional descriptive algorithms such as the genetic algorithm.
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Wang, Kai, Nan Lyu, Hongjuan Diao, Shujuan Jin, Tao Zeng, Yaoqi Zhou, and Ruibo Wu. "GM-DockZn: a geometry matching-based docking algorithm for zinc proteins." Bioinformatics 36, no. 13 (May 5, 2020): 4004–11. http://dx.doi.org/10.1093/bioinformatics/btaa292.

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Abstract Motivation Molecular docking is a widely used technique for large-scale virtual screening of the interactions between small-molecule ligands and their target proteins. However, docking methods often perform poorly for metalloproteins due to additional complexity from the three-way interactions among amino-acid residues, metal ions and ligands. This is a significant problem because zinc proteins alone comprise about 10% of all available protein structures in the protein databank. Here, we developed GM-DockZn that is dedicated for ligand docking to zinc proteins. Unlike the existing docking methods developed specifically for zinc proteins, GM-DockZn samples ligand conformations directly using a geometric grid around the ideal zinc-coordination positions of seven discovered coordination motifs, which were found from the survey of known zinc proteins complexed with a single ligand. Results GM-DockZn has the best performance in sampling near-native poses with correct coordination atoms and numbers within the top 50 and top 10 predictions when compared to several state-of-the-art techniques. This is true not only for a non-redundant dataset of zinc proteins but also for a homolog set of different ligand and zinc-coordination systems for the same zinc proteins. Similar superior performance of GM-DockZn for near-native-pose sampling was also observed for docking to apo-structures and cross-docking between different ligand complex structures of the same protein. The highest success rate for sampling nearest near-native poses within top 5 and top 1 was achieved by combining GM-DockZn for conformational sampling with GOLD for ranking. The proposed geometry-based sampling technique will be useful for ligand docking to other metalloproteins. Availability and implementation GM-DockZn is freely available at www.qmclab.com/ for academic users. Supplementary information Supplementary data are available at Bioinformatics online.
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Butt, Sania Safdar, Yasmin Badshah, Maria Shabbir, and Mehak Rafiq. "Molecular Docking Using Chimera and Autodock Vina Software for Nonbioinformaticians." JMIR Bioinformatics and Biotechnology 1, no. 1 (June 19, 2020): e14232. http://dx.doi.org/10.2196/14232.

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In the field of drug discovery, many methods of molecular modeling have been employed to study complex biological and chemical systems. Experimental strategies are integrated with computational approaches for the identification, characterization, and development of novel drugs and compounds. In modern drug designing, molecular docking is an approach that explores the confirmation of a ligand within the binding site of a macromolecule. To date, many software and tools for docking have been employed. AutoDock Vina (in UCSF [University of California, San Francisco] Chimera) is one of the computationally fastest and most accurate software employed in docking. In this paper, a sequential demonstration of molecular docking of the ligand fisetin with the target protein Akt has been provided, using AutoDock Vina in UCSF Chimera 1.12. The first step involves target protein ID retrieval from the protein database, the second step involves visualization of the protein structure in UCSF Chimera, the third step involves preparation of the target protein for docking, the fourth step involves preparation of the ligand for docking, the fifth step involves docking of the ligand and the target protein as Mol.2 files in Chimera by using AutoDock Vina, and the final step involves interpretation and analysis of the docking results. By following the guidelines and steps outlined in this paper, researchers with no previous background in bioinformatics research can perform computational docking in an easier and more user-friendly manner.
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Taufer, M., R. Armen, Jianhan Chen, P. Teller, and C. Brooks. "Computational multiscale modeling in protein--ligand docking." IEEE Engineering in Medicine and Biology Magazine 28, no. 2 (March 2009): 58–69. http://dx.doi.org/10.1109/memb.2009.931789.

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Baskaran, C., and M. Ramachandran. "Computational molecular docking studies on anticancer drugs." Asian Pacific Journal of Tropical Disease 2 (January 2012): S734—S738. http://dx.doi.org/10.1016/s2222-1808(12)60254-0.

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Dissertations / Theses on the topic "Computational docking"

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Totrov, Maxim. "Computational studies on protein-ligand docking." Thesis, Open University, 1999. http://oro.open.ac.uk/58005/.

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This thesis describes the development and refinement of a number of techniques for molecular docking and ligand database screening, as well as the application of these techniques to predict the structures of several protein-ligand complexes and to discover novel ligands of an important receptor protein. Global energy optimisation by Monte-Carlo minimisation in internal co-ordinates was used to predict bound conformations of eight protein-ligand complexes. Experimental X-ray crystallography structures became available after the predictions were made. Comparison with the X-ray structures showed that the docking procedure placed 30 to 70% of the ligand molecule correctly within 1.5A from the native structure. The discrimination potential for identification of high-affinity ligands was derived and optimised using a large set of available protein-ligand complex structures. A fast boundary-element solvation electrostatic calculation algorithm was implemented to evaluate the solvation component of the discrimination potential. An accelerated docking procedure utilising pre-calculated grid potentials was developed and tested. For 23 receptors and 63 ligands extracted from X-ray structures, the docking and discrimination protocol was capable of correct identification of the majority of native receptor-ligand couples. 51 complexes with known structures were predicted. 35 predictions were within 3A from the native structure, giving correct overall positioning of the ligand, and 26 were within 2A, reproducing a detailed picture of the receptor-ligand interaction. Docking and ligand discrimination potential evaluation was applied to screen the database of more than 150000 commercially available compounds for binding to the fibroblast growth factor receptor tyrosine kinase, the protein implicated in several pathological cell growth aberrations. As expected, a number of compounds selected by the screening protocol turned out to be known inhibitors of the tyrosine kinases. 49 putative novel ligands identified by the screening protocol were experimentally tested and five compounds have shown inhibition of phosphorylation activity of the kinase. These compounds can be used as leads for further drug development.
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Livoti, Elsa Livoti. "Experimentally validated computational docking to characterize protein-protein interactions." Thesis, University of Kent, 2017. https://kar.kent.ac.uk/67450/.

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Each biomolecule in a living organism needs to adopt a specific threedimensional conformation to function properly. Function itself is usually achieved by specific interactions between biomolecular units. Structural knowledge at atomic level of biomolecules and their interaction is important to understand the mechanisms leading to biological response and to develop strategies to interfere with them when necessary. Antibodies are molecules of the immune system playing an ever more prominent role in basic research as well as in the biotechnology and pharmaceutical sectors. Characterizing their region of interaction with other proteins (epitopes) is useful for purposes ranging from molecular biology research to vaccine design. During my PhD studies I used a combination of solution NMR mapping, molecular biology and computational docking to provide a structural and biophysical characterization of new neutralizing antibodies from Dengue virus recovered subjects, comparing the binding of the same antibody to the four Dengue serotypes and the binding of different antibodies to the same serotype. We were able to rationally mutate an antibody to first alter its selectivity for different viral strains and then increase its neutralization by ~40 folds. For the first time, this was achieved without the availability of an x-ray structure. In a second sub-project, I investigated the interaction of the chemokine CXCL12 with the chromatin-associated protein HMGB1, confirming their direct interaction (only proposed but never proved before) and providing a structural explanation for the HMGB1 dependent increase of CXCL12 cellular activity. High profile publications resulted from the two above projects. The above mentioned projects relied heavily on solution NMR spectroscopy, which is ideally suited to the atomic level characterization of intermolecular interfaces and, as a consequence, to antibody epitope discovery. Having provided a residue-level description of a protein-protein interface by NMR, we subsequently used this experimental information to guide and validate computational docking experiments aimed at providing a three dimensional structure of the protein-protein (or antibody-protein) complex of interest. In collaboration with other members of my research group I validated the use of NMR and computational simulations to study antibody-antigen interactions, publishing two reviews in collaboration with other members of my research group.
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Moont, Gidon. "Computational modelling of protein/protein and protein/DNA docking." Thesis, University College London (University of London), 2005. http://discovery.ucl.ac.uk/1445703/.

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The docking problem is to start with unbound conformations for the components of a complex, and computationally model a near-native structure for the complex. This thesis describes work in developing computer programs to tackle both protein/protein and protein/DNA docking. Empirical pair potential functions are generated from datasets of residue/residue interactions. A scoring function was parameterised and then used to screen possible complexes, generated by the global search computer algorithm FTDOCK using shape complementarity and electrostatics, for 9 systems. A correct docking (RMSD < 2.5A) is placed within the top 12% of the pair potential score ranked complexes for all systems. The computer software FTDOCK is modified for the docking of proteins to DNA, starting from the unbound protein and DNA coordinates modelled computationally. Complexes are then ranked by protein/DNA pair potentials derived from a database of 20 protein/DNA complexes. A correct docking (at least 65% of correct contacts) was identified at rank < 4 for 3 of the 8 complexes. This improved to 4 out of 8 when the complexes were filtered using experimental data defining the DNA footprint. The FTDOCK program was rewritten, and improved pair potential functions were developed from a set of non-homologous protein/protein interfaces. The algorithms were tested on a non-homologous set of 18 protein/protein complexes, starting with unbound conformations. Us ing cross-validated pair potential functions and the energy rninimisation software MultiDock, a correct docking ( RMSD of CQ interface 25% correct contacts) is found in the top 10 ranks in 6 out of 18 systems. The current best computational docking algorithms are discussed, and strategies for improvement are suggested.
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Tantar, Alexandru-Adrian. "Hybrid parallel metaheuristics for molecular docking on computational grids." Thesis, Lille 1, 2009. http://www.theses.fr/2009LIL10166.

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Cette thèse porte sur les méta-heuristiques hiérarchiques parallèles adaptatives pour l'échantillonnage conformationnel. Étant un problème hautement combinatoire et multlmodal, l'échantillonnage conformationnel requière la construction d'approches hybrides à large échelle. Après une analyse dei modèles mathématiques, nécessitant l'examen des différentes formulations du champ de force, nous avons proposé une étude des opérateurs de variation et des méthodes de recherche locale adaptés au problème ainsi que leur hybridation dynamique et adaptative. Cette étude nous a conduit à la proposition de mécanismes d'adaptation des paramètres des algorithmes utilisés en fonction du processus d'évolution. Dans cette thèse, nous proposons également des algorithmes adaptatifs hybndes hiérarchiques distribués, fortement extensibles. L'expérimentation, basée sur l'utilisation de multiples modèles parallèles, démontre la grande efficacité de ces algorithmes. En effet, les résultats obtenus montrent que des RMSD moyens en dessous de 1.0 A peuvent être obtenus sur des instances difficiles des problèmes de prédiction de la structure des protéines et de docking moléculaire. La validation des approches hybrides proposées a été effectuée sur Grid'5000, une grille expérimentale d'échelle nationale composée d'environ 5000 coeurs de calcul. Une image système a été développée en utilisant Globus pour permettre des déploiements distribués à large échelle. L'approche hiérarchique distribuée construite a été ainsi déployée sur plusieurs grappes, avec près de 1000 coeurs de calcul
The thesis proposes an extensive analysis of adaptive hierarchical parallel metaheuristics for ab initio conformational sampling. Standing as an NP, combinatorial, highly multi-modal optimization problem, conformational sampling requires for high-performance large scale hybrid approaches to be constructed. Following an incremental definition, minimum complexity conformational sampling mathematical models are first analyzed, entailing a review of different force field formulations. A comprehensive analysis is conducted on a large set of operators and local search algorithms including adaptive and dynamic mechanisms. As determined by the analysis outcomes, complex a priori and online parameter tuning stages are designed. finally, highly scalable hierarchical hybrid distributed algorithm designs are proposed. Experimentation is carried over multiple parallelization models with afferent cooperation topologies. Expenmentations resulted in unprecedented results to be obtained. Multiple perfect conformational matches have been determined, on highly difficult protein structure prediction and molecular docking benchmarks, with RMSD average values below 1.0A. The validation of the proposed hybrid approaehes was performed on Grid'5000, a French computational grid, with almost 5000 computational cores. A Globus Toolkit hased Grid'SOOO system image has been developed, sustaining large scale distributed deployments. The constructed hierarchical hybrid distributed algorithm has been deployed on multiple clusters, with almost 1000 computing cores. Finally, a parallel AutoDock version was developed using the ParadisEO framework, integrating the developed algorithms
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Turzo, SM Bargeen Alam. "Computational Investigation of Protein Assemblies." Cleveland State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=csu1532714714406789.

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Jiménez, García Brian. "Development and optimization of high-performance computational tools for protein-protein docking." Doctoral thesis, Universitat de Barcelona, 2016. http://hdl.handle.net/10803/398790.

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Computing has pushed a paradigm shift in many disciplines, including structural biology and chemistry. This change has been mainly driven by the increase in performance of computers, the capacity of dealing with huge amounts of experimental and analysis data and the development of new algorithms. Thanks to these advances, our understanding on the chemistry that supports life has increased and it is even more sophisticated that we had never imagined before. Proteins play a major role in nature and are often described as the factories of the cell as they are involved in virtually all important function in living organisms. Unfortunately, our understanding of the function of many proteins is still very poor due to the actual limitations in experimental techniques which, at the moment, they can not provide crystal structure for many protein complexes. The development of computational tools as protein-protein docking methods could help to fill this gap. In this thesis, we have presented a new protein-protein docking method, LightDock, which supports the use of different custom scoring functions and it includes anisotropic normal analysis to model backbone flexibility upon binding process. Second, several interesting web-based tools for the scientific community have been developed, including a web server for protein-protein docking, a web tool for the characterization of protein-protein interfaces and a web server for including SAXS experimental data for a better prediction of protein complexes. Moreover, the optimizations made in the pyDock protocol and the increase in th performance helped our group to score in the 5th position among more than 60 participants in the past two CAPRI editions. Finally, we have designed and compiled the Protein-Protein (version 5.0) and Protein-RNA (version 1.0) docking benchmarks, which are important resources for the community to test and to develop new methods against a reference set of curated cases.
Gràcies als recents avenços en computació, el nostre coneixement de la química que suporta la vida ha incrementat enormement i ens ha conduït a comprendre que la química de la vida és més sofisticada del que mai haguéssim pensat. Les proteïnes juguen un paper fonamental en aquesta química i són descrites habitualment com a les fàbriques de les cèl·lules. A més a més, les proteïnes estan involucrades en gairebé tots els processos fonamentals en els éssers vius. Malauradament, el nostre coneixement de la funció de moltes proteïnes és encara escaig degut a les limitacions actuals de molts mètodes experimentals, que encara no són capaços de proporcionar-nos estructures de cristall per a molts complexes proteïna-proteïna. El desenvolupament de tècniques i eines informàtiques d’acoblament proteïna-proteïna pot ésser crucial per a ajudar-nos a reduir aquest forat. En aquesta tesis, hem presentat un nou mètode computacional de predicció d’acoblament proteïna-proteïna, LightDock, que és capaç de fer servir diverses funcions energètiques definides per l’usuari i incloure un model de flexibilitat de la cadena principal mitjançant la anàlisis de modes normals. Segon, diverses eines d’interès per a la comunitat científica i basades en tecnologia web han sigut desenvolupades: un servidor web de predicció d’acoblament proteïna-proteïna, una eina online per a caracteritzar les interfícies d’acoblament proteïna-proteïna i una eina web per a incloure dades experimentals de tipus SAXS. A més a més, les optimitzacions fetes al protocol pyDock i la conseqüent millora en rendiment han propiciat que el nostre grup de recerca obtingués la cinquena posició entre més de 60 grups en les dues darreres avaluacions de l’experiment internacional CAPRI. Finalment, hem dissenyat i compilat els banc de proves d’acoblament proteïna-proteïna (versió 5) i proteïna-ARN (versió 1), molt importants per a la comunitat ja que permeten provar i desenvolupar nous mètodes i analitzar-ne el rendiment en aquest marc de referència comú.
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Buonfiglio, Rosa <1985&gt. "Computational strategies to include protein flexibility in Ligand Docking and Virtual Screening." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6330/1/Tesi_Buonfiglio.pdf.

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The dynamic character of proteins strongly influences biomolecular recognition mechanisms. With the development of the main models of ligand recognition (lock-and-key, induced fit, conformational selection theories), the role of protein plasticity has become increasingly relevant. In particular, major structural changes concerning large deviations of protein backbones, and slight movements such as side chain rotations are now carefully considered in drug discovery and development. It is of great interest to identify multiple protein conformations as preliminary step in a screening campaign. Protein flexibility has been widely investigated, in terms of both local and global motions, in two diverse biological systems. On one side, Replica Exchange Molecular Dynamics has been exploited as enhanced sampling method to collect multiple conformations of Lactate Dehydrogenase A (LDHA), an emerging anticancer target. The aim of this project was the development of an Ensemble-based Virtual Screening protocol, in order to find novel potent inhibitors. On the other side, a preliminary study concerning the local flexibility of Opioid Receptors has been carried out through ALiBERO approach, an iterative method based on Elastic Network-Normal Mode Analysis and Monte Carlo sampling. Comparison of the Virtual Screening performances by using single or multiple conformations confirmed that the inclusion of protein flexibility in screening protocols has a positive effect on the probability to early recognize novel or known active compounds.
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Buonfiglio, Rosa <1985&gt. "Computational strategies to include protein flexibility in Ligand Docking and Virtual Screening." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6330/.

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The dynamic character of proteins strongly influences biomolecular recognition mechanisms. With the development of the main models of ligand recognition (lock-and-key, induced fit, conformational selection theories), the role of protein plasticity has become increasingly relevant. In particular, major structural changes concerning large deviations of protein backbones, and slight movements such as side chain rotations are now carefully considered in drug discovery and development. It is of great interest to identify multiple protein conformations as preliminary step in a screening campaign. Protein flexibility has been widely investigated, in terms of both local and global motions, in two diverse biological systems. On one side, Replica Exchange Molecular Dynamics has been exploited as enhanced sampling method to collect multiple conformations of Lactate Dehydrogenase A (LDHA), an emerging anticancer target. The aim of this project was the development of an Ensemble-based Virtual Screening protocol, in order to find novel potent inhibitors. On the other side, a preliminary study concerning the local flexibility of Opioid Receptors has been carried out through ALiBERO approach, an iterative method based on Elastic Network-Normal Mode Analysis and Monte Carlo sampling. Comparison of the Virtual Screening performances by using single or multiple conformations confirmed that the inclusion of protein flexibility in screening protocols has a positive effect on the probability to early recognize novel or known active compounds.
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Moreno, Nascimento Érica Cristina. "Understanding Acetylcholinesterase Inhibitors: Computational Modeling Approaches." Doctoral thesis, Universitat Jaume I, 2017. http://hdl.handle.net/10803/406125.

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La presente Tesis Doctoral constituye un estudio teórico sobre el proceso de inhibición de la acetilcolinesterasa por moléculas que bloquean el sitio activo de la proteína. Se han estudiado un conjunto de 44 inhibidores, dividido en 8 clases distintas, por método QM/MM, docking molecular, análisis estadístico utilizando los métodos de análisis multivariados de datos, reconocimiento de patrones y de algoritmos de agrupamiento. Se ha estudiado la interacción y se ha calculado la energía libre de enlace (Gbind) utilizando los métodos híbridos QM/MM MD asociados a los métodos de la FEP y del BIE. De esta forma, podemos entender la interacción entre los residuos de la enzima, las moléculas de agua estructurales y los diversos inhibidores, a nivel atómico, que son fundamentales para el diseño de nuevos inhibidores y drogas con aplicaciones directas en la enfermedad de Alzheimer.
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Patel, Dharmeshkumar. "Computational Studies of Ion Channel Blockers and Protein Aggregation." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17134.

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The ion channels are important membrane bound proteins and multi-therapeutic target for a number of diseases. There are many scorpion toxins reported to bind with KV1 channels. We have identified a plant toxin. We have studied binding of two different sequence length peptides, 1-47 and 10-44 with KV1.3 and KV1.1 channels. The binding complex structures are obtained by molecular docking and stability of the complexes are checked by MD simulations. The pore inserting residue is predicted as K33. The KV1.1-toxin complexes are found to be unstable, and toxin doesn't block the pore in MD simulations. The predicted binding free energies for both complexes of KV1.3-toxin are within the range of experimental values with picomolar and nanomolar activities, respectively. Experiments have also confirmed that Toxin(1-47) does not block the current with KV1.1 channel as well. The inward-rectifier potassium (Kir) channels play significant roles in several physiological disorders. I have studied binding of honey bee toxin called tertiapin (TPN) with Kir3.x channels. K21 is predicted to be the pore inserting residue. The binding free energies are calculated and validated with experimental value. I have studied TPN complexes with Kir3.1 and Kir 3.3 and explained insensitivity of TPN to these channels. A few crystal structures of bacterial sodium channels have been determined but the crystal structures of mammalian ones have not been resolved yet. So, it is important to understand similarities and differences between the bacterial and mammalian channels. The validated homology modeled NaV1.4 complexed with GIIIA system provides a good model for such comparisons. Here I have studied the binding of GIIIA to the bacterial sodium channels NaVAb and NaVRh by combination of docking and MD simulations then the potential mean forces are constructed. Comparison of the binding mode of GIIIA between mammalian and bacterial channels. Protein aggregation affects both human physiological functions and bioengineered products so finding methods to prevent aggregation will we very useful. It is an important area of investigation which could be facilitated by a molecular-level understanding of dimer formation, which is the first step in aggregation. Here we propose a computational method based on molecular dynamics simulations that will facilitate finding aggregation-prone regions on human lysozyme HL[D67H], noting that the wild type HL doesn't aggregate.
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Books on the topic "Computational docking"

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Zaheer Ul-Haq and Angela K. Wilson, eds. Frontiers in Computational Chemistry: Volume 6. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150368481220601.

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Frontiers in Computational Chemistry presents contemporary research on molecular modeling techniques used in drug discovery and the drug development process: computer aided molecular design, drug discovery and development, lead generation, lead optimization, database management, computer and molecular graphics, and the development of new computational methods or efficient algorithms for the simulation of chemical phenomena including analyses of biological activity. The sixth volume of this series features these six different perspectives on the application of computational chemistry in rational drug design: 1. Computer-aided molecular design in computational chemistry 2. The role of ensemble conformational sampling using molecular docking & dynamics in drug discovery 3. Molecular dynamics applied to discover antiviral agents 4. Pharmacophore modeling approach in drug discovery against the tropical infectious disease malaria 5. Advances in computational network pharmacology for Traditional Chinese Medicine (TCM) research 6. Progress in electronic-structure based computational methods: from small molecules to large molecular systems of biological significance
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Book chapters on the topic "Computational docking"

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Ehrlich, Lutz P., and Rebecca C. Wade. "Protein-Protein Docking." In Reviews in Computational Chemistry, 61–97. New York, USA: John Wiley & Sons, Inc., 2001. http://dx.doi.org/10.1002/0471224413.ch2.

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Muegge, Ingo, and Matthias Rarey. "Small Molecule Docking and Scoring." In Reviews in Computational Chemistry, 1–60. New York, USA: John Wiley & Sons, Inc., 2001. http://dx.doi.org/10.1002/0471224413.ch1.

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Bitencourt-Ferreira, Gabriela, and Walter Filgueira de Azevedo. "SAnDReS: A Computational Tool for Docking." In Methods in Molecular Biology, 51–65. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9752-7_4.

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Prandi, Davide. "A Formal Approach to Molecular Docking." In Computational Methods in Systems Biology, 78–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11885191_6.

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Walters, D. Eric. "Computational Docking to Sweet Taste Receptor Models." In Sweetness and Sweeteners, 162–67. Washington, DC: American Chemical Society, 2008. http://dx.doi.org/10.1021/bk-2008-0979.ch011.

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Machado, K. S., A. T. Winck, D. D. Ruiz, and O. Norberto de Souza. "Discretization of Flexible-Receptor Docking Data." In Advances in Bioinformatics and Computational Biology, 75–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15060-9_10.

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Kim, Chong-Min, Chung-In Won, Jae-Kwan Kim, Joonghyun Ryu, Jong Bhak, and Deok-Soo Kim. "Protein-Ligand Docking Based on Beta-Shape." In Transactions on Computational Science IX, 123–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16007-3_6.

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Novoa, Eva Maria, Lluis Ribas de Pouplana, and Modesto Orozco. "Small Molecule Docking from Theoretical Structural Models." In Computational Modeling of Biological Systems, 75–95. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-2146-7_4.

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Peh, Sally Chen Woon, and Jer Lang Hong. "Protein Ligand Docking Using Simulated Jumping." In Computational Science and Its Applications -- ICCSA 2016, 1–10. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42111-7_1.

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Ohue, Masahito. "Re-ranking of Computational Protein–Peptide Docking Solutions with Amino Acid Profiles of Rigid-Body Docking Results." In Advances in Computer Vision and Computational Biology, 749–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71051-4_58.

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Conference papers on the topic "Computational docking"

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Hui, Liu, Lin Feng, Yang Jianli, and Liu Xiu-Ling. "Side-chain flexibility in protein docking." In 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2015. http://dx.doi.org/10.1109/cibcb.2015.7300315.

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Janežič, Dušanka, Janez Konc, Matej Penca, Ksenija Poljanec, George Maroulis, and Theodore E. Simos. "Protein Binding Sites Prediction and Docking." In COMPUTATIONAL METHODS IN SCIENCE AND ENGINEERING: Advances in Computational Science: Lectures presented at the International Conference on Computational Methods in Sciences and Engineering 2008 (ICCMSE 2008). AIP, 2009. http://dx.doi.org/10.1063/1.3225337.

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VAUGHAN, R., and E. BERGMANN. "Manually augmented proximity operations and docking control." In 7th Computational Fluid Dynamics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1985. http://dx.doi.org/10.2514/6.1985-1941.

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Boisson, Jean-Charles, Laetitia Jourdan, El-Ghazali Talbi, and Dragos Horvath. "Parallel multi-objective algorithms for the molecular docking problem." In 2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2008). IEEE, 2008. http://dx.doi.org/10.1109/cibcb.2008.4675777.

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Li, Hongjian, Kwong-Sak Leung, and Man-Hon Wong. "idock: A multithreaded virtual screening tool for flexible ligand docking." In 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2012. http://dx.doi.org/10.1109/cibcb.2012.6217214.

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Ghosh, Preetam, Samik Ghosh, Kalyan Basu, Sajal K. Das, and Simon Daefler. "A Stochastic model to estimate the time taken for Protein-Ligand Docking." In 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology. IEEE, 2006. http://dx.doi.org/10.1109/cibcb.2006.330963.

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Cinar, Iraz, Irem Aksoy, and Gunnur Guler. "Spectroscopic and Computational Molecular Docking studies on the protein-drug interactions." In 2020 Medical Technologies Congress (TIPTEKNO). IEEE, 2020. http://dx.doi.org/10.1109/tiptekno50054.2020.9299322.

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Liu, Shang, and Wei Huo. "Terminal Sliding Mode Control for Space Rendezvous and Docking." In 2015 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2015. http://dx.doi.org/10.1109/cicn.2015.297.

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Du, Xiaoxu, and Huan Wang. "Analysis of Hydrodynamic Characteristics in the Process of Autonomous Underwater Vehicle Docking." In ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/omae2015-42323.

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Abstract:
The successful operation of an Autonomous Underwater Vehicle (AUV) requires the capability to return to a dock. A number of underwater docking technologies have been proposed and tested in the past. The docking allows the AUV to recharge its batteries, download data and upload new instructions, which is helpful to improve the working time and efficiency. During the underwater docking process, unsteady hydrodynamic interference occurs between the docking device and an AUV. To ensure a successful docking, it is very important that the underwater docking hydrodynamics of AUV is understood. In this paper, numerical simulations based on the computational fluid dynamics (CFD) solutions were carried out for a 1.85m long AUV with maximum 0.2 m in diameter during the docking process. The two-dimensional AUV model without fin and rudder was used in the simulation. The mathematical model based on the Reynolds-averaged Navier-Stokes (RANS) equations was established. The finite volume method (FVM) and the dynamic structured mesh technique were used. SIMPLE algorithm and the k-ε turbulence model in the Descartes coordinates were also adopted. The hydrodynamics characteristics of different docking states were analyzed, such as the different docking velocity, the docking device including baffle or not. The drag coefficients of AUV in the process of docking were computed for various docking conditions, i.e., the AUV moving into the docking in the speed of 1m/s, 2m/s, 5m/s. The results indicate that the drag coefficient increases slowly in the process of AUV getting close to the docking device. When the AUV moves into the docking device, the drag coefficient increases rapidly. Then the drag coefficient decreases rapidly. The drag coefficient decreases with the increase of velocity when AUV enters the docking device. It was also found that the drag coefficient can be effectively reduced by dislodging the baffle of docking device.
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Adamson, Torin, Selina Bauernfeind, Bruna Jacobson, and Lydia Tapia. "Using player generated data to elucidate molecular docking." In BCB '20: 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3388440.3414704.

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