Relevant bibliographies by topics / Protein Structure Networks (PSN)

Academic literature on the topic 'Protein Structure Networks (PSN)'

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Published: 6 September 2023

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Journal articles on the topic "Protein Structure Networks (PSN)"

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Felline, Angelo, Michele Seeber, and Francesca Fanelli. "webPSN v2.0: a webserver to infer fingerprints of structural communication in biomacromolecules." Nucleic Acids Research 48, W1 (May 19, 2020): W94—W103. http://dx.doi.org/10.1093/nar/gkaa397.

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Abstract A mixed Protein Structure Network (PSN) and Elastic Network Model-Normal Mode Analysis (ENM-NMA)-based strategy (i.e. PSN-ENM) was developed to investigate structural communication in bio-macromolecules. Protein Structure Graphs (PSGs) are computed on a single structure, whereas information on system dynamics is supplied by ENM-NMA. The approach was implemented in a webserver (webPSN), which was significantly updated herein. The webserver now handles both proteins and nucleic acids and relies on an internal upgradable database of network parameters for ions and small molecules in all PDB structures. Apart from the radical restyle of the server and some changes in the calculation setup, other major novelties concern the possibility to: a) compute the differences in nodes, links, and communication pathways between two structures (i.e. network difference) and b) infer links, hubs, communities, and metapaths from consensus networks computed on a number of structures. These new features are useful to identify commonalties and differences between two different functional states of the same system or structural-communication signatures in homologous or analogous systems. The output analysis relies on 3D-representations, interactive tables and graphs, also available for download. Speed and accuracy make this server suitable to comparatively investigate structural communication in large sets of bio-macromolecular systems. URL: http://webpsn.hpc.unimore.it.
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Duong, Vy T., Elizabeth M. Diessner, Gianmarc Grazioli, Rachel W. Martin, and Carter T. Butts. "Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures." Biomolecules 11, no. 12 (November 30, 2021): 1788. http://dx.doi.org/10.3390/biom11121788.

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Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail—an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This “neural upscaling” procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 μs atomistic molecular dynamics trajectory of Aβ1–40, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs.
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Aydınkal, Rasim Murat, Onur Serçinoğlu, and Pemra Ozbek. "ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism." Nucleic Acids Research 47, W1 (May 22, 2019): W471—W476. http://dx.doi.org/10.1093/nar/gkz390.

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AbstractProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.
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Newaz, Khalique, Mahboobeh Ghalehnovi, Arash Rahnama, Panos J. Antsaklis, and Tijana Milenković. "Network-based protein structural classification." Royal Society Open Science 7, no. 6 (June 2020): 191461. http://dx.doi.org/10.1098/rsos.191461.

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Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, protein structural classification (PSC) can help, by allowing for determining structural classes of currently unclassified proteins based on their features, and then relying on the fact that proteins with similar structures have similar functions. Existing PSC approaches rely on sequence-based or direct three-dimensional (3D) structure-based protein features. By contrast, we first model 3D structures of proteins as protein structure networks (PSNs). Then, we use network-based features for PSC. We propose the use of graphlets, state-of-the-art features in many research areas of network science, in the task of PSC. Moreover, because graphlets can deal only with unweighted PSNs, and because accounting for edge weights when constructing PSNs could improve PSC accuracy, we also propose a deep learning framework that automatically learns network features from weighted PSNs. When evaluated on a large set of approximately 9400 CATH and approximately 12 800 SCOP protein domains (spanning 36 PSN sets), the best of our proposed approaches are superior to existing PSC approaches in terms of accuracy, with comparable running times. Our data and code are available at https://doi.org/10.5281/zenodo.3787922
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Fanelli, Francesca, Angelo Felline, Francesco Raimondi, and Michele Seeber. "Structure network analysis to gain insights into GPCR function." Biochemical Society Transactions 44, no. 2 (April 11, 2016): 613–18. http://dx.doi.org/10.1042/bst20150283.

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G protein coupled receptors (GPCRs) are allosteric proteins whose functioning fundamentals are the communication between the two poles of the helix bundle. Protein structure network (PSN) analysis is one of the graph theory-based approaches currently used to investigate the structural communication in biomolecular systems. Information on system's dynamics can be provided by atomistic molecular dynamics (MD) simulations or coarse grained elastic network models paired with normal mode analysis (ENM–NMA). The present review article describes the application of PSN analysis to uncover the structural communication in G protein coupled receptors (GPCRs). Strategies to highlight changes in structural communication upon misfolding, dimerization and activation are described. Focus is put on the ENM–NMA-based strategy applied to the crystallographic structures of rhodopsin in its inactive (dark) and signalling active (meta II (MII)) states, highlighting changes in structure network and centrality of the retinal chromophore in differentiating the inactive and active states of the receptor.
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Chasapis, Christos T., and Alexios Vlamis-Gardikas. "Probing Conformational Dynamics by Protein Contact Networks: Comparison with NMR Relaxation Studies and Molecular Dynamics Simulations." Biophysica 1, no. 2 (April 8, 2021): 157–67. http://dx.doi.org/10.3390/biophysica1020012.

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Protein contact networks (PCNs) have been used for the study of protein structure and function for the past decade. In PCNs, each amino acid is considered as a node while the contacts among amino acids are the links/edges. We examined the possible correlation between the closeness centrality measure of amino acids within PCNs and their mobility as known from NMR spin relaxation experiments and molecular dynamic (MD) simulations. The pivotal observation was that plasticity within a protein stretch correlated inversely to closeness centrality. Effects on protein conformational plasticity caused by the formation of disulfide bonds or protein–protein interactions were also identified by the PCN analysis measure closeness centrality and the hereby introduced percentage of closeness centrality perturbation (% CCP). All the comparisons between PCN measures, NMR data, and MDs were performed in a set of proteins of different biological functions and structures: the core protease domain of anthrax lethal factor, the N-terminal RING domain of E3 Ub ligase Arkadia, the reduced and oxidized forms of human thioredoxin 1, and the ubiquitin molecules (Ub) of the catalytic Ub–RING–E3–E2–Ub complex of E3 ligase Ark2.The graph theory analysis of PCNs could thus provide a general method for assessing the conformational dynamics of free proteins and putative plasticity changes between different protein forms (apo/complexed or reduced/oxidized).
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Mahmud, Khandakar Abu Hasan Al, Fuad Hasan, Md Ishak Khan, and Ashfaq Adnan. "Shock-Induced Damage Mechanism of Perineuronal Nets." Biomolecules 12, no. 1 (December 22, 2021): 10. http://dx.doi.org/10.3390/biom12010010.

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The perineuronal net (PNN) region of the brain’s extracellular matrix (ECM) surrounds the neural networks within the brain tissue. The PNN is a protective net-like structure regulating neuronal activity such as neurotransmission, charge balance, and action potential generation. Shock-induced damage of this essential component may lead to neuronal cell death and neurodegenerations. The shock generated during a vehicle accident, fall, or improvised device explosion may produce sufficient energy to damage the structure of the PNN. The goal is to investigate the mechanics of the PNN in reaction to shock loading and to understand the mechanical properties of different PNN components such as glycan, GAG, and protein. In this study, we evaluated the mechanical strength of PNN molecules and the interfacial strength between the PNN components. Afterward, we assessed the PNN molecules’ damage efficiency under various conditions such as shock speed, preexisting bubble, and boundary conditions. The secondary structure altercation of the protein molecules of the PNN was analyzed to evaluate damage intensity under varying shock speeds. At a higher shock speed, damage intensity is more elevated, and hyaluronan (glycan molecule) is most likely to break at the rigid junction. The primary structure of the protein molecules is least likely to fail. Instead, the molecules’ secondary bonds will be altered. Our study suggests that the number of hydrogen bonds during the shock wave propagation is reduced, which leads to the change in protein conformations and damage within the PNN structure. As such, we found a direct connection between shock wave intensity and PNN damage.
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Lubovac, Zelmina. "Investigating Topological and Functional Features of Multimodular Proteins." Journal of Biomedicine and Biotechnology 2009 (2009): 1–10. http://dx.doi.org/10.1155/2009/472415.

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To generate functional modules as functionally and structurally cohesive formations in protein interaction networks (PINs) constitutes an important step towards understanding how modules communicate on a higher level of the PIN organisation that underlies cell functionality. However, we need to understand how individual modules communicate and are organized into the higher-order structure(s) of the PIN organization that underlies cell functionality. In an attempt to contribute to this understanding, we make an assumption that the proteins reappearing in several modules, termed here as multimodular proteins (MMPs), may be useful in building higher-order structure(s) as they may constitute communication points between different modules. In this paper, we investigate common properties shared by these proteins and compare them with the properties of so-called single-modular proteins (SMPs) by analyzing three aspects: functional aspect, that is, annotation of the proteins, topological aspect that is betweenness centrality of the proteins, and lethality. Furthermore, we investigate the interconnectivity role of some proteins that are identified as functionally and topologically important.
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Drago, Valentina, Luisa Di Paola, Claire Lesieur, Renato Bernardini, Claudio Bucolo, and Chiara Bianca Maria Platania. "In-Silico Characterization of von Willebrand Factor Bound to FVIII." Applied Sciences 12, no. 15 (August 4, 2022): 7855. http://dx.doi.org/10.3390/app12157855.

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Factor VIII belongs to the coagulation cascade and is expressed as a long pre-protein (mature form, 2351 amino acids long). FVIII is deficient or defective in hemophilic A patients, who need to be treated with hemoderivatives or recombinant FVIII substitutes, i.e., biologic drugs. The interaction between FVIII and von Willebrand factor (VWF) influences the pharmacokinetics of FVIII medications. In vivo, full-length FVIII (FL-FVIII) is secreted in a plasma-inactive form, which includes the B domain, which is then proteolyzed by thrombin protease activity, leading to an inactive plasma intermediate. In this work, we analyzed through a computational approach the binding of VWF with two structure models of FVIII (secreted full-length with B domain, and B domain-deleted FVIII). We included in our analysis the atomic model of efanesoctocog alfa, a novel and investigational recombinant FVIII medication, in which the VWF is covalently linked to FVIII. We carried out a structural analysis of VWF/FVIII interfaces by means of protein–protein docking, PISA (Proteins, Interfaces, Structures and Assemblies), and protein contact networks (PCN) analyses. Accordingly, our computational approaches to previously published experimental data demonstrated that the domains A3-C1 of B domain-deleted FVIII (BDD-FVIII) is the preferential binding site for VWF. Overall, our computational approach applied to topological analysis of protein–protein interface can be aimed at the rational design of biologic drugs other than FVIII medications.
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DANICH, V. M., and S. M. SHEVCHENKO. "FORMALIZATION OF THE CONCEPT OF SOCIAL SPACE OF THE SUBJECT THROUGH THE CONCEPT OF SOCIAL NETWORKS." REVIEW OF TRANSPORT ECONOMICS AND MANAGEMENT, no. 4(20) (November 30, 2020): 182–94. http://dx.doi.org/10.15802/rtem2020/228878.

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The purpose. To study the structure and properties of social space from the point of view of the subject's social networks, to find out the mechanisms of forming social contacts in modern conditions. Methods. The concept of "social network" is studied from the point of view of modern tools for their creation. Mechanisms for forming a personal social network are presented on the example of the "work" group from the list of "friends" of the profile. Highlighting the subject's personal social network made it possible to identify information transmission channels. The analysis of corporate social networks of enterprises, technologies of their implementation and features of functioning is made. The functionality of modern corporate social network services is studied. A survey of social networks of higher educational institutions in the context of distance education, as well as the use of existing social network services by higher educational institutions in the context of distance education, was conducted. Results. Features of forming a list of accounts in the "work" group from the general list of "friends"are revealed. Modern tools for creating social networks of the subject, corporate social networks of enterprises and organizations are studied. The factors that will influence the formation of a web-PSN are highlighted. The structure of web-CSN, disadvantages and advantages of using it, and technologies for their implementation are studied. Changes in the structures of corporate social networks of educational institutions are highlighted. Scientific novelty. The paper defines a personal social network (PSN), web-PSN, aggregate web-PSN, personificator, and personificant. The paper identifies groups of web-PSN objects, elucidates the mechanisms of web-PSN formation, and provides a formal description of them. Corporate social networks are considered, the definition of web-CSN is given, and the problems that an enterprise should solve before implementing web-CSN are formulated. The factors that influence the formation of a personal social network are also identified. Practical significance. Highlighting a web-PSN is relevant for business tasks, such as identifying potential buyers, identifying bots, fake accounts, and so on. The research results are important for other applied tasks, for example, determining the prospects for network expansion, determining the directions and speed of information dissemination, information perception, the ability and possibility of distortion, transmission prospects, and in general, for predicting the dynamics of communication networks of subjects. The study of the mechanisms of forming social contacts is important for formulating tips and suggestions on the processes of creating and developing a pesonal social network for an ordinary user, to ensure the protection of their account and personal data that it contains.
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Dissertations / Theses on the topic "Protein Structure Networks (PSN)"

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Zhao, Jing. "Protein Structure Prediction Based on Neural Networks." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/23636.

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Proteins are the basic building blocks of biological organisms, and are responsible for a variety of functions within them. Proteins are composed of unique amino acid sequences. Some has only one sequence, while others contain several sequences that are combined together. These combined amino acid sequences fold to form a unique three-dimensional (3D) shape. Although the sequences may fold proteins into different 3D shapes in diverse environments, proteins with similar amino acid sequences typically have similar 3D shapes and functions. Knowledge of the 3D shape of a protein is important in both protein function analysis and drug design, for example when assessing the toxicity reduction associated with a given drug. Due to the complexity of protein 3D shapes and the close relationship between shapes and functions, the prediction of protein 3D shapes has become an important topic in bioinformatics. This research introduces a new approach to predict proteins’ 3D shapes, utilizing a multilayer artificial neural network. Our novel solution allows one to learn and predict the representations of the 3D shape associated with a protein by starting directly from its amino acid sequence descriptors. The input of the artificial neural network is a set of amino acid sequence descriptors we created based on a set of probability density functions. In our algorithm, the probability density functions are calculated by the correlation between the constituent amino acids, according to the substitution matrix. The output layer of the network is formed by 3D shape descriptors provided by an information retrieval system, called CAPRI. This system contains the pose invariant 3D shape descriptors, and retrieves proteins having the closest structures. The network is trained by proteins with known amino acid sequences and 3D shapes. Once the network has been trained, it is able to predict the 3D shape descriptors of the query protein. Based on the predicted 3D shape descriptors, the CAPRI system allows the retrieval of known proteins with 3D shapes closest to the query protein. These retrieved proteins may be verified as to whether they are in the same family as the query protein, since proteins in the same family generally have similar 3D shapes. The search for similar 3D shapes is done against a database of more than 45,000 known proteins. We present the results when evaluating our approach against a number of protein families of various sizes. Further, we consider a number of different neural network architectures and optimization algorithms. When the neural network is trained with proteins that are from large families where the proteins in the same family have similar amino acid sequences, the accuracy for finding proteins from the same family is 100%. When we employ proteins whose family members have dissimilar amino acid sequences, or those from a small protein family, in which case, neural networks with one hidden layer produce more promising results than networks with two hidden layers, and the performance may be improved by increasing the number of hidden nodes when the networks have one hidden layer.
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Zotenko, Elena. "Computational methods in protein structure comparison and analysis of protein interaction networks." College Park, Md.: University of Maryland, 2007. http://hdl.handle.net/1903/7621.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.
Thesis research directed by: Dept. of Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Grochow, Joshua A. "On the structure and evolution of protein interaction networks." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/42053.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 107-114).
The study of protein interactions from the networks point of view has yielded new insights into systems biology [Bar03, MA03, RSM+02, WS98]. In particular, "network motifs" become apparent as a useful and systematic tool for describing and exploring networks [BP06, MKFV06, MSOI+02, SOMMA02, SV06]. Finding motifs has involved either exact counting (e.g. [MSOI+02]) or subgraph sampling (e.g. [BP06, KIMA04a, MZW05]). In this thesis we develop an algorithm to count all instances of a particular subgraph, which can be used to query whether a given subgraph is a significant motif. This method can be used to perform exact counting of network motifs faster and with less memory than previous methods, and can also be combined with subgraph sampling to find larger motifs than ever before -- we have found motifs with up to 15 nodes and explored subgraphs up to 20 nodes. Unlike previous methods, this method can also be used to explore motif clustering and can be combined with network alignment techniques [FNS+06, KSK+03]. We also present new methods of estimating parameters for models of biological network growth, and present a new model based on these parameters and underlying binding domains. Finally, we propose an experiment to explore the effect of the whole genome duplication [KBL04] on the protein-protein interaction network of S. cerevisiae, allowing us to distinguish between cases of subfunctionalization and neofunctionalization.
by Joshua A. Grochow.
M.Eng.
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Valenta, Martin. "Predikce proteinových domén." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236163.

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The work is focused on the area of the proteins and their domains. It also briefly describes gathering methods of the protein´s structure at the various levels of the hierarchy. This is followed by examining of existing tools for protein´s domains prediction and databases consisting of domain´s information. In the next part of the work selected representatives of prediction methods are introduced.  These methods work with the information about the internal structure of the molecule or the amino acid sequence. The appropriate chapter outlines applied procedure of domains´ boundaries prediction. The prediction is derived from the primary structure of the protein, using a neural network  The implemented procedure and its possibility of further development in the related thesis are introduced at the conclusion of this work.
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Tsilo, Lipontseng Cecilia. "Protein secondary structure prediction using neural networks and support vector machines." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1002809.

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Predicting the secondary structure of proteins is important in biochemistry because the 3D structure can be determined from the local folds that are found in secondary structures. Moreover, knowing the tertiary structure of proteins can assist in determining their functions. The objective of this thesis is to compare the performance of Neural Networks (NN) and Support Vector Machines (SVM) in predicting the secondary structure of 62 globular proteins from their primary sequence. For each NN and SVM, we created six binary classifiers to distinguish between the classes’ helices (H) strand (E), and coil (C). For NN we use Resilient Backpropagation training with and without early stopping. We use NN with either no hidden layer or with one hidden layer with 1,2,...,40 hidden neurons. For SVM we use a Gaussian kernel with parameter fixed at = 0.1 and varying cost parameters C in the range [0.1,5]. 10- fold cross-validation is used to obtain overall estimates for the probability of making a correct prediction. Our experiments indicate for NN and SVM that the different binary classifiers have varying accuracies: from 69% correct predictions for coils vs. non-coil up to 80% correct predictions for stand vs. non-strand. It is further demonstrated that NN with no hidden layer or not more than 2 hidden neurons in the hidden layer are sufficient for better predictions. For SVM we show that the estimated accuracies do not depend on the value of the cost parameter. As a major result, we will demonstrate that the accuracy estimates of NN and SVM binary classifiers cannot distinguish. This contradicts a modern belief in bioinformatics that SVM outperforms other predictors.
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Alistair, Chalk. "PREDICTION OF PROTEIN SECONDARY STRUCTURE by Incorporating Biophysical Information into Artificial Neural Networks." Thesis, University of Skövde, Department of Computer Science, 1998. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-235.

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This project applied artificial neural networks to the field of secondary structure prediction of proteins. A NETtalk architecture with a window size 13 was used. Over-fitting was avoided by the use of 3 real numbers to represent amino acids, reducing the number of adjustable weights to 840. Two alternative representations of amino acids that incorporated biophysical data were created and tested. They were tested both separately and in combination on a standard 7-fold cross-validation set of 126 proteins. The best performance was achieved using an average result from two predictions. This was then filtered and gave the following results. Accuracy levels for core structures were: Q3total accuracy of 61.3% consisting of Q3 accuracy’s of 54.0%, 38.1% & 77.0% for Helix, Strand and Coil respectively with Matthew’s correlation’s Ca = 0.34, Cb = 0.26 , Cc = 0.31. The average lengths of structures predicted were 9.8, 4.9 and 11.0, for helix, sheet and coil respectively. These results are lower than those of other methods using single sequences and localist representations. The most likely reason for this is over generalisation caused by using a small number of units.

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Reyaz-Ahmed, Anjum B. "Protein Secondary Structure Prediction Using Support Vector Machines, Nueral Networks and Genetic Algorithms." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_theses/43.

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Bioinformatics techniques to protein secondary structure prediction mostly depend on the information available in amino acid sequence. Support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure prediction. In this study, a new sliding window scheme is introduced with multiple windows to form the protein data for training and testing SVM. Orthogonal encoding scheme coupled with BLOSUM62 matrix is used to make the prediction. First the prediction of binary classifiers using multiple windows is compared with single window scheme, the results shows single window not to be good in all cases. Two new classifiers are introduced for effective tertiary classification. This new classifiers use neural networks and genetic algorithms to optimize the accuracy of the tertiary classifier. The accuracy level of the new architectures are determined and compared with other studies. The tertiary architecture is better than most available techniques.
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Mulnaes, Daniel [Verfasser]. "TopSuite: A meta-suite for protein structure prediction using deep neural networks / Daniel Mulnaes." Düsseldorf : Universitäts- und Landesbibliothek der Heinrich-Heine-Universität Düsseldorf, 2020. http://d-nb.info/1222261634/34.

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Royer, Loic. "Unraveling the Structure and Assessing the Quality of Protein Interaction Networks with Power Graph Analysis." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-62562.

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Molecular biology has entered an era of systematic and automated experimentation. High-throughput techniques have moved biology from small-scale experiments focused on specific genes and proteins to genome and proteome-wide screens. One result of this endeavor is the compilation of complex networks of interacting proteins. Molecular biologists hope to understand life's complex molecular machines by studying these networks. This thesis addresses tree open problems centered upon their analysis and quality assessment. First, we introduce power graph analysis as a novel approach to the representation and visualization of biological networks. Power graphs are a graph theoretic approach to lossless and compact representation of complex networks. It groups edges into cliques and bicliques, and nodes into a neighborhood hierarchy. We demonstrate power graph analysis on five examples, and show its advantages over traditional network representations. Moreover, we evaluate the algorithm performance on a benchmark, test the robustness of the algorithm to noise, and measure its empirical time complexity at O (e1.71)- sub-quadratic in the number of edges e. Second, we tackle the difficult and controversial problem of data quality in protein interaction networks. We propose a novel measure for accuracy and completeness of genome-wide protein interaction networks based on network compressibility. We validate this new measure by i) verifying the detrimental effect of false positives and false negatives, ii) showing that gold standard networks are highly compressible, iii) showing that authors' choice of confidence thresholds is consistent with high network compressibility, iv) presenting evidence that compressibility is correlated with co-expression, co-localization and shared function, v) showing that complete and accurate networks of complex systems in other domains exhibit similar levels of compressibility than current high quality interactomes. Third, we apply power graph analysis to networks derived from text-mining as well to gene expression microarray data. In particular, we present i) the network-based analysis of genome-wide expression profiles of the neuroectodermal conversion of mesenchymal stem cells. ii) the analysis of regulatory modules in a rare mitochondrial cytopathy: emph{Mitochondrial Encephalomyopathy, Lactic acidosis, and Stroke-like episodes} (MELAS), and iii) we investigate the biochemical causes behind the enhanced biocompatibility of tantalum compared with titanium.
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Planas, Iglesias Joan 1980. "On the study of 3D structure of proteins for developing new algorithms to complete the interactome and cell signalling networks." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/104152.

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Proteins are indispensable players in virtually all biological events. The functions of proteins are determined by their three dimensional (3D) structure and coordinated through intricate networks of protein-protein interactions (PPIs). Hence, a deep comprehension of such networks turns out to be crucial for understanding the cellular biology. Computational approaches have become critical tools for analysing PPI networks. In silico methods take advantage of the existing PPI knowledge to both predict new interactions and predict the function of proteins. Regarding the task of predicting PPIs, several methods have been already developed. However, recent findings demonstrate that such methods could take advantage of the knowledge on non-interacting protein pairs (NIPs). On the task of predicting the function of proteins,the Guilt-by-Association (GBA) principle can be exploited to extend the functional annotation of proteins over PPI networks. In this thesis, a new algorithm for PPI prediction and a protocol to complete cell signalling networks are presented. iLoops is a method that uses NIP data and structural information of proteins to predict the binding fate of protein pairs. A novel protocol for completing signalling networks –a task related to predicting the function of a protein, has also been developed. The protocol is based on the application of GBA principle in PPI networks.
Les proteïnes tenen un paper indispensable en virtualment qualsevol procés biològic. Les funcions de les proteïnes estan determinades per la seva estructura tridimensional (3D) i són coordinades per mitjà d’una complexa xarxa d’interaccions protiques (en anglès, protein-protein interactions, PPIs). Axí doncs, una comprensió en profunditat d’aquestes xarxes és fonamental per entendre la biologia cel•lular. Per a l’anàlisi de les xarxes d’interacció de proteïnes, l’ús de tècniques computacionals ha esdevingut fonamental als darrers temps. Els mètodes in silico aprofiten el coneixement actual sobre les interaccions proteiques per fer prediccions de noves interaccions o de les funcions de les proteïnes. Actualment existeixen diferents mètodes per a la predicció de noves interaccions de proteines. De tota manera, resultats recents demostren que aquests mètodes poden beneficiar-se del coneixement sobre parelles de proteïnes no interaccionants (en anglès, non-interacting pairs, NIPs). Per a la tasca de predir la funció de les proteïnes, el principi de “culpable per associació” (en anglès, guilt by association, GBA) és usat per extendre l’anotació de proteïnes de funció coneguda a través de xarxes d’interacció de proteïnes. En aquesta tesi es presenta un nou mètode pre a la predicció d’interaccions proteiques i un nou protocol basat per a completar xarxes de senyalització cel•lular. iLoops és un mètode que utilitza dades de parells no interaccionants i coneixement de l’estructura 3D de les proteïnes per a predir interaccions de proteïnes. També s’ha desenvolupat un nou protocol per a completar xarxes de senyalització cel•lular, una tasca relacionada amb la predicció de les funcions de les proteïnes. Aquest protocol es basa en aplicar el principi GBA a xarxes d’interaccions proteiques.
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Books on the topic "Protein Structure Networks (PSN)"

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Protein interaction networks: Computational analysis. Cambridge: Cambridge University Press, 2009.

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Flapan, Erica. Knots, molecules, and the universe: An introduction to topology. Providence, Rhode Island: American Mathematical Society, 2015.

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Mrozek, Dariusz. High-Performance Computational Solutions in Protein Bioinformatics. Springer London, Limited, 2014.

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High-Performance Computational Solutions in Protein Bioinformatics. Springer International Publishing AG, 2014.

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Newman, Mark. Biological networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0005.

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A discussion of a range of different kinds of biological networks. The chapter starts with a discussion of biochemical networks such metabolic and protein interaction networks and methods for determining their structure, particularly focusing on high-throughput methods such as the yeast two-hybrid screen. Next is a discussion of neural networks and other networks in the brain, along with measurement techniques such as slice electron microscopy, optical microscopy, transsynaptic tracing, functional MRI, and diffusion MRI. Finally, there is a discussion of ecological networks, and particularly food webs.
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Book chapters on the topic "Protein Structure Networks (PSN)"

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Vanhala, J., and E. Clementi. "Protein Structure Prediction and Neural Networks." In Modem Techniques in Computational Chemistry: MOTECC-91, 991–1015. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3032-5_25.

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Punta, Marco, and Burkhard Rost. "Neural Networks Predict Protein Structure and Function." In Methods in Molecular Biology™, 198–225. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-101-1_11.

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Guermeur, Yann, and Patrick Gallinari. "Combining statistical models for protein secondary structure prediction." In Artificial Neural Networks — ICANN 96, 599–604. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61510-5_102.

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Zhou, Yaoqi, and Eshel Faraggi. "Prediction of One-Dimensional Structural Properties Of Proteins by Integrated Neural Networks." In Introduction to Protein Structure Prediction, 45–74. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470882207.ch4.

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Fang, Yi, Mengtian Sun, Guoxian Dai, and Karthik Ramani. "The Intrinsic Geometric Structure of Protein-Protein Interaction Networks for Protein Interaction Prediction." In Intelligent Computing in Bioinformatics, 487–93. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09330-7_56.

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Wu, Cathy H. "Neural Networks for Molecular Sequence Classification." In The Protein Folding Problem and Tertiary Structure Prediction, 279–305. Boston, MA: Birkhäuser Boston, 1994. http://dx.doi.org/10.1007/978-1-4684-6831-1_9.

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Mason, Oliver, Mark Verwoerd, and Peter Clifford. "Inference of Protein Function from the Structure of Interaction Networks." In Structural Analysis of Complex Networks, 439–61. Boston: Birkhäuser Boston, 2010. http://dx.doi.org/10.1007/978-0-8176-4789-6_18.

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Condori, E. Sejje, J. Soncco Lupa, S. Barrios Cornejo, and V. Machaca Arceda. "ArgosMol: A Web Tool for Protein Structure Prediction and Visualization." In Lecture Notes in Networks and Systems, 604–16. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98012-2_43.

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Shao, Jianlin, Dong Xu, Lanzhou Wang, and Yifei Wang. "Bayesian Neural Networks for Prediction of Protein Secondary Structure." In Advanced Data Mining and Applications, 544–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527503_65.

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Shi, S. Y. M., and P. N. Suganthan. "Feature Analysis and Classification of Protein Secondary Structure Data." In Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003, 1151–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2_137.

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Conference papers on the topic "Protein Structure Networks (PSN)"

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KUCHAIEV, OLEKSII, and NATAŠA PRŽULJ. "LEARNING THE STRUCTURE OF PROTEIN-PROTEIN INTERACTION NETWORKS." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812836939_0005.

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Bohr, Henrik, Jacob Bohr, So̸ren Brunak, Rodney M. J. Cotterill, Henrik Fredholm, Benny Lautrup, and Steffen B. Petersen. "Neural Networks Applied to Protein Structure." In Advances in biomolecular simulations. AIP, 1991. http://dx.doi.org/10.1063/1.41313.

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Singh, Preeti, and Yan-Qing Zhang. "Protein secondary structure prediction using neural networks." In Defense and Security, edited by Belur V. Dasarathy. SPIE, 2004. http://dx.doi.org/10.1117/12.541411.

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Wu, Daniel Duanqing, and Xiaohua Hu. "Mining and analyzing the topological structure of protein-protein interaction networks." In the 2006 ACM symposium. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1141277.1141318.

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Dong Wang, Shiyuan Han, Yuehui Chen, Wenzheng Bao, Kun Ma, and Ajith Abraham. "A new protein structure classification model." In 2014 6th International Conference on Computational Aspects of Social Networks (CASoN). IEEE, 2014. http://dx.doi.org/10.1109/cason.2014.6920419.

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Ratul, Md Aminur Rab, Maryam Tavakol Elahi, M. Hamed Mozaffari, and WonSook Lee. "PS8-Net: A Deep Convolutional Neural Network to Predict the Eight-State Protein Secondary Structure." In 2020 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2020. http://dx.doi.org/10.1109/dicta51227.2020.9363393.

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Babaei, Sepideh, Seyyed A. Seyyedsalehi, and Amir Geranmayeh. "Pruning neural networks for protein secondary structure prediction." In 2008 8th IEEE International Conference on Bioinformatics and BioEngineering (BIBE). IEEE, 2008. http://dx.doi.org/10.1109/bibe.2008.4696702.

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Aydin, Zafer, and Ommu Gulsum Uzut. "Combining classifiers for protein secondary structure prediction." In 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2017. http://dx.doi.org/10.1109/cicn.2017.8319350.

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Dzikovska, Vasilka, Mile Oreskovic, Slobodan Kalajdziski, Kire Trivodaliev, and Danco Davcev. "Protein Secondary Structure Prediction Method Based on Neural Networks." In 2008 2nd International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2008. http://dx.doi.org/10.1109/icbbe.2008.48.

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Zhu, Hongbing, Chengdong Pu, Xiaoli Lin, Jinguang Gu, Shanjun Zhang, and Mengsi Su. "Protein Structure Prediction with EPSO in Toy Model." In 2009 Second International Conference on Intelligent Networks and Intelligent Systems (ICINIS). IEEE, 2009. http://dx.doi.org/10.1109/icinis.2009.172.

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Reports on the topic "Protein Structure Networks (PSN)"

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Kirchhoff, Helmut, and Ziv Reich. Protection of the photosynthetic apparatus during desiccation in resurrection plants. United States Department of Agriculture, February 2014. http://dx.doi.org/10.32747/2014.7699861.bard.

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In this project, we studied the photosynthetic apparatus during dehydration and rehydration of the homoiochlorophyllous resurrection plant Craterostigmapumilum (retains most of the photosynthetic components during desiccation). Resurrection plants have the remarkable capability to withstand desiccation, being able to revive after prolonged severe water deficit in a few days upon rehydration. Homoiochlorophyllous resurrection plants are very efficient in protecting the photosynthetic machinery against damage by reactive oxygen production under drought. The main purpose of this BARD project was to unravel these largely unknown protection strategies for C. pumilum. In detail, the specific objectives were: (1) To determine the distribution and local organization of photosynthetic protein complexes and formation of inverted hexagonal phases within the thylakoid membranes at different dehydration/rehydration states. (2) To determine the 3D structure and characterize the geometry, topology, and mechanics of the thylakoid network at the different states. (3) Generation of molecular models for thylakoids at the different states and study the implications for diffusion within the thylakoid lumen. (4) Characterization of inter-system electron transport, quantum efficiencies, photosystem antenna sizes and distribution, NPQ, and photoinhibition at different hydration states. (5) Measuring the partition of photosynthetic reducing equivalents between the Calvin cycle, photorespiration, and the water-water cycle. At the beginning of the project, we decided to use C. pumilum instead of C. wilmsii because the former species was available from our collaborator Dr. Farrant. In addition to the original two dehydration states (40 relative water content=RWC and 5% RWC), we characterized a third state (15-20%) because some interesting changes occurs at this RWC. Furthermore, it was not possible to detect D1 protein levels by Western blot analysis because antibodies against other higher plants failed to detect D1 in C. pumilum. We developed growth conditions that allow reproducible generation of different dehydration and rehydration states for C. pumilum. Furthermore, advanced spectroscopy and microscopy for C. pumilum were established to obtain a detailed picture of structural and functional changes of the photosynthetic apparatus in different hydrated states. Main findings of our study are: 1. Anthocyan accumulation during desiccation alleviates the light pressure within the leaves (Fig. 1). 2. During desiccation, stomatal closure leads to drastic reductions in CO2 fixation and photorespiration. We could not identify alternative electron sinks as a solution to reduce ROS production. 3. On the supramolecular level, semicrystalline protein arrays were identified in thylakoid membranes in the desiccated state (see Fig. 3). On the electron transport level, a specific series of shut downs occur (summarized in Fig. 2). The main events include: Early shutdown of the ATPase activity, cessation of electron transport between cyt. bf complex and PSI (can reduce ROS formation at PSI); at higher dehydration levels uncoupling of LHCII from PSII and cessation of electron flow from PSII accompanied by crystal formation. The later could severe as a swift PSII reservoir during rehydration. The specific order of events in the course of dehydration and rehydration discovered in this project is indicative for regulated structural transitions specifically realized in resurrection plants. This detailed knowledge can serve as an interesting starting point for rationale genetic engineering of drought-tolerant crops.
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