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Relevant bibliographies by topics / Protein structure prediction

Academic literature on the topic 'Protein structure prediction'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 March 2023

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Journal articles on the topic "Protein structure prediction"

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Deng, Haiyou, Ya Jia, and Yang Zhang. "Protein structure prediction." International Journal of Modern Physics B 32, no. 18 (July 15, 2018): 1840009. http://dx.doi.org/10.1142/s021797921840009x.

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Predicting 3D structure of protein from its amino acid sequence is one of the most important unsolved problems in biophysics and computational biology. This paper attempts to give a comprehensive introduction of the most recent effort and progress on protein structure prediction. Following the general flowchart of structure prediction, related concepts and methods are presented and discussed. Moreover, brief introductions are made to several widely-used prediction methods and the community-wide critical assessment of protein structure prediction (CASP) experiments.

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Jin, Shikai, Vinicius G. Contessoto, Mingchen Chen, Nicholas P. Schafer, Wei Lu, Xun Chen, Carlos Bueno, et al. "AWSEM-Suite: a protein structure prediction server based on template-guided, coevolutionary-enhanced optimized folding landscapes." Nucleic Acids Research 48, W1 (May 8, 2020): W25—W30. http://dx.doi.org/10.1093/nar/gkaa356.

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Abstract The accurate and reliable prediction of the 3D structures of proteins and their assemblies remains difficult even though the number of solved structures soars and prediction techniques improve. In this study, a free and open access web server, AWSEM-Suite, whose goal is to predict monomeric protein tertiary structures from sequence is described. The model underlying the server’s predictions is a coarse-grained protein force field which has its roots in neural network ideas that has been optimized using energy landscape theory. Employing physically motivated potentials and knowledge-based local structure biasing terms, the addition of homologous template and co-evolutionary restraints to AWSEM-Suite greatly improves the predictive power of pure AWSEM structure prediction. From the independent evaluation metrics released in the CASP13 experiment, AWSEM-Suite proves to be a reasonably accurate algorithm for free modeling, standing at the eighth position in the free modeling category of CASP13. The AWSEM-Suite server also features a front end with a user-friendly interface. The AWSEM-Suite server is a powerful tool for predicting monomeric protein tertiary structures that is most useful when a suitable structure template is not available. The AWSEM-Suite server is freely available at: https://awsem.rice.edu.

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Deng Hai-You, Jia Ya, and Zhang Yang. "Protein structure prediction." Acta Physica Sinica 65, no. 17 (2016): 178701. http://dx.doi.org/10.7498/aps.65.178701.

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Benner, Steven A., Dietlind L. Geroff, and J. David Rozzell. "Protein Structure Prediction." Science 274, no. 5292 (November 29, 1996): 1448–49. http://dx.doi.org/10.1126/science.274.5292.1448.b.

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Benner, Steven A., Dietlind L. Geroff, and J. David Rozzell. "Protein Structure Prediction." Science 274, no. 5292 (November 29, 1996): 1448–49. http://dx.doi.org/10.1126/science.274.5292.1448-b.

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Barton, Geoffrey J., and Robert B. Russell. "Protein structure prediction." Nature 361, no. 6412 (February 1993): 505–6. http://dx.doi.org/10.1038/361505b0.

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Robson, Barry, and Jean Gamier. "Protein structure prediction." Nature 361, no. 6412 (February 1993): 506. http://dx.doi.org/10.1038/361506a0.

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Al-Lazikani, Bissan, Joon Jung, Zhexin Xiang, and Barry Honig. "Protein structure prediction." Current Opinion in Chemical Biology 5, no. 1 (February 2001): 51–56. http://dx.doi.org/10.1016/s1367-5931(00)00164-2.

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Westhead, David R., and Janet M. Thornton. "Protein structure prediction." Current Opinion in Biotechnology 9, no. 4 (August 1998): 383–89. http://dx.doi.org/10.1016/s0958-1669(98)80012-8.

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Benner, S. A., D. L. Geroff, and J. David Rozzell. "Protein Structure Prediction." Science 274, no. 5292 (November 29, 1996): 1447b—1451. http://dx.doi.org/10.1126/science.274.5292.1447b.

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Dissertations / Theses on the topic "Protein structure prediction"

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Cuff, James Andrew. "Protein structure prediction." Thesis, University of Oxford, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365685.

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Wood, Matthew J. "Protein secondary structure prediction." Thesis, University of Nottingham, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430525.

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Choi, Yoonjoo. "Protein loop structure prediction." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:bd5c1b9b-89ba-4225-bc17-85d3f5067e58.

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This dissertation concerns the study and prediction of loops in protein structures. Proteins perform crucial functions in living organisms. Despite their importance, we are currently unable to predict their three dimensional structure accurately. Loops are segments that connect regular secondary structures of proteins. They tend to be located on the surface of proteins and often interact with other biological agents. As loops are generally subject to more frequent mutations than the rest of the protein, their sequences and structural conformations can vary significantly even within the same protein family. Although homology modelling is the most accurate computational method for protein structure prediction, difficulties still arise in predicting protein loops. Protein loop structure prediction is therefore a bottleneck in solving the protein structure prediction problem. Reflecting on the success of homology modelling, I implement an improved version of a database search method, FREAD. I show how sequence similarity as quantified by environment specific substitution scores can be used to significantly improve loop prediction. FREAD performs appreciably better for an identifiable subset of loops (two thirds of shorter loops and half of the longer loops tested) than ab initio methods; FREAD's predictive ability is length independent. In general, it produces results within 2Å root mean square deviation (RMSD) from the native conformations, compared to an average of over 10Å for loop length 20 for any of the other tested ab initio methods. I then examine FREAD’s predictive ability on a specific type of loops called complementarity determining regions (CDRs) in antibodies. CDRs consist of six hypervariable loops and form the majority of the antigen binding site. I examine CDR loop structure prediction as a general case of loop structure prediction problem. FREAD achieves accuracy similar to specific CDR predictors. However, it fails to accurately predict CDR-H3, which is known to be the most challenging CDR. Various FREAD versions including FREAD with contact information (ConFREAD) are examined. The FREAD variants improve predictions for CDR-H3 on homology models and docked structures. Lastly, I focus on the local properties of protein loops and demonstrate that the protein loop structure prediction problem is a local protein folding problem. The end-to-end distance of loops (loop span) follows a distinctive frequency distribution, regardless of secondary structure elements connected or the number of residues in the loop. I show that the loop span distribution follows a Maxwell-Boltzmann distribution. Based on my research, I propose future directions in protein loop structure prediction including estimating experimentally undetermined local structures using FREAD, multiple loop structure prediction using contact information and a novel ab initio method which makes use of loop stretch.

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Son, Hyeon S. "Prediction of membrane protein structure." Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337775.

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Offman, Marc Nathan. "Protein structure prediction and refinement." Thesis, University College London (University of London), 2008. http://discovery.ucl.ac.uk/16775/.

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Over the last few years it has been shown that protein modelling techniques, especially template based modelling, are now accurate enough for qualitative analysis and decision-making in support of a wide range of experimental work. Automatic protein modelling pipelines are becoming ever more accurate; however, this has come hand in hand with an increasingly complicated interplay between all components involved. Despite all progress, still important problems remain and so far computational methods cannot routinely meet the accuracy of experimentally determined protein structures. In protein modelling pipelines, several important steps dictate a model's quality. Selecting a good template and aligning the query sequence correctly, backbone completion, model refinement and final model selection are considered the main steps. As a first step to approach protein refinement, a genetic algorithm (GA) for protein model recombination and optimization is presented in this work. This algorithm has the potential, to drive models away from the template towards the native structure. Furthermore, a complete and novel modelling pipeline, incorporating this GA is presented. In this context, a new scoring scheme, backbone repair algorithm and several other findings are reported and presented: We introduce the novel concept of Alternating Evolutionary Pressure, i.e. intermediate rounds within the GA simulation, where unrestrained linear growth of the model population is allowed. This approach improves the structural sampling and thereby facilitates energy-based model selection. Finally, the GA in combination with molecular dynamics simulations is used in the context of protein engineering. Several mutants were identified to stabilise and increase the activity of the cancer drug L-Asparaginase, a complex enzyme. The successful prediction of these mutations stresses the importance of protein molecular modelling for cell biology and in a clinical context.

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Munro, Robin Edward James. "Protein structure prediction and modelling." Thesis, University College London (University of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313827.

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

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Hosur, Raghavendra. "Structure-based algorithms for protein-protein interaction prediction." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/75843.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2012.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 109-124).
Protein-protein interactions (PPIs) play a central role in all biological processes. Akin to the complete sequencing of genomes, complete descriptions of interactomes is a fundamental step towards a deeper understanding of biological processes, and has a vast potential to impact systems biology, genomics, molecular biology and therapeutics. PPIs are critical in maintenance of cellular integrity, metabolism, transcription/ translation, and cell-cell communication. This thesis develops new methods that significantly advance our efforts at structure- based approaches to predict PPIs and boost confidence in emerging high-throughput (HTP) data. The aims of this thesis are, 1) to utilize physicochemical properties of protein interfaces to better predict the putative interacting regions and increase coverage of PPI prediction, 2) increase confidence in HTP datasets by identifying likely experimental errors, and 3) provide residue-level information that gives us insights into structure-function relationships in PPIs. Taken together, these methods will vastly expand our understanding of macromolecular networks. In this thesis, I introduce two computational approaches for structure-based proteinprotein interaction prediction: iWRAP and Coev2Net. iWRAP is an interface threading approach that utilizes biophysical properties specific to protein interfaces to improve PPI prediction. Unlike previous structure-based approaches that use single structures to make predictions, iWRAP first builds profiles that characterize the hydrophobic, electrostatic and structural properties specific to protein interfaces from multiple interface alignments. Compatibility with these profiles is used to predict the putative interface region between the two proteins. In addition to improved interface prediction, iWRAP provides better accuracy and close to 50% increase in coverage on genome-scale PPI prediction tasks. As an application, we effectively combine iWRAP with genomic data to identify novel cancer related genes involved in chromatin remodeling, nucleosome organization and ribonuclear complex assembly - processes known to be critical in cancer. Coev2Net addresses some of the limitations of iWRAP, and provides techniques to increase coverage and accuracy even further. Unlike earlier sequence and structure profiles, Coev2Net explicitly models long-distance correlations at protein interfaces. By formulating interface co-evolution as a high-dimensional sampling problem, we enrich sequence/structure profiles with artificial interacting homologus sequences for families which do not have known multiple interacting homologs. We build a spanning-tree based graphical model induced by the simulated sequences as our interface profile. Cross-validation results indicate that this approach is as good as previous methods at PPI prediction. We show that Coev2Net's predictions correlate with experimental observations and experimentally validate some of the high-confidence predictions. Furthermore, we demonstrate how analysis of the predicted interfaces together with human genomic variation data can help us understand the role of these mutations in disease and normal cells.
by Raghavendra Hosur.
Ph.D.

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Shatabda, Swakkhar. "Local Search Heuristics for Protein Structure Prediction." Thesis, Griffith University, 2014. http://hdl.handle.net/10072/365446.

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This thesis presents our research on protein structure prediction on discrete lattices. Given a protein’s amino acid sequence, the protein structure prediction problem is to find its three dimensional native structure that has the minimum free energy. Knowledge about the native protein structures and their respective folding process is a key to understand protein functionalities and consequently the basics of life. Protein structure prediction problem is one of the most challenging problems in molecular biology. In-vitro laboratory methods applied to this problem are very time-consuming, cost- expensive and failure-prone. Also, the search based optimization methods used are com- putationally very expensive. To tackle these, researchers have used various simplified models, such as low resolution energy functions and lattice-based structures, and applied incomplete local search methods on them. The simplified models help obtain back-bone structures first and then hierarchically work out the details. Local search methods can normally quickly find solutions although they suffer from re-visitation and stagnancy, and require good heuristics. In the literature, researchers have mostly used primitive ap- proaches based on random decisions at various choice points. Consequently, these methods are applicable to small-sized proteins only. In this thesis, we present a number of techniques to improve the performance of lo- cal search methods applied to protein structure prediction problem using discrete lattices. Firstly, we propose a memory based local search framework that maintains a set of already explored solutions for avoiding re-visitation and stores previously unexplored but promi- nent solutions for restarting to handle stagnation. A novel encoding scheme for protein structures is proposed to handle symmetry present in the search space. We also propose an approximate matching strategy that results in reducing redundancy in the search space.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Institute for Integrated and Intelligent Systems
Science, Environment, Engineering and Technology
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Copley, Richard Robertson. "Analysis and prediction of protein structure." Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361954.

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Books on the topic "Protein structure prediction"

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Protein structure prediction. 3rd ed. New York: Humana Press, 2014.

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Webster, David. Protein Structure Prediction. New Jersey: Humana Press, 2000. http://dx.doi.org/10.1385/1592593682.

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Kihara, Daisuke, ed. Protein Structure Prediction. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0708-4.

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Zaki, Mohammed J., and Christopher Bystroff, eds. Protein Structure Prediction. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-574-9.

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Kihara, Daisuke, ed. Protein Structure Prediction. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0366-5.

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E, Sternberg Michael J., ed. Protein structure prediction: A practical approach. Oxford: IRL Press at Oxford University Press, 1996.

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1950-, Tsigelny Igor F., ed. Protein structure prediction: Bioinformatic approach. La Jolla, Calif: International University Line, 2002.

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Mohammed, Zaki, and Bystroff Chris. Protein Structure Prediction, Second Edition. New Jersey: Humana Press, 2007. http://dx.doi.org/10.1385/1597455741.

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Zhou, Yaoqi, Andrzej Kloczkowski, Eshel Faraggi, and Yuedong Yang, eds. Prediction of Protein Secondary Structure. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6406-2.

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Rangwala, Huzefa, and George Karypis, eds. Introduction to Protein Structure Prediction. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470882207.

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Book chapters on the topic "Protein structure prediction"

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Wiehe, Kevin, Matthew W. Peterson, Brian Pierce, Julian Mintseris, and Zhiping Weng. "Protein–Protein Docking: Overview and Performance Analysis." In Protein Structure Prediction, 283–314. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-574-9_11.

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Guo, Jun-tao, Kyle Ellrott, and Ying Xu. "A Historical Perspective of Template-Based Protein Structure Prediction." In Protein Structure Prediction, 3–42. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-574-9_1.

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Ngan, Shing-Chung, Ling-Hong Hung, Tianyun Liu, and Ram Samudrala. "Scoring Functions for De Novo Protein Structure Prediction Revisited." In Protein Structure Prediction, 243–81. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-574-9_10.

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Garcia, Angel E. "Molecular Dynamics Simulations of Protein Folding." In Protein Structure Prediction, 315–30. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-574-9_12.

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Tramontano, Anna, Domenico Cozzetto, Alejandro Giorgetti, and Domenico Raimondo. "The Assessment of Methods for Protein Structure Prediction." In Protein Structure Prediction, 43–57. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-574-9_2.

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McGuffin, Liam James. "Aligning Sequences to Structures." In Protein Structure Prediction, 61–90. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-574-9_3.

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Xu, Jinbo, Feng Jiao, and Libo Yu. "Protein Structure Prediction Using Threading." In Protein Structure Prediction, 91–121. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-574-9_4.

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Shatsky, Maxim, Ruth Nussinov, and Haim J. Wolfson. "Algorithms for Multiple Protein Structure Alignment and Structure-Derived Multiple Sequence Alignment." In Protein Structure Prediction, 125–46. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-574-9_5.

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Gao, Feng, and Mohammed J. Zaki. "Indexing Protein Structures Using Suffix Trees." In Protein Structure Prediction, 147–69. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-574-9_6.

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Bystroff, Christopher, and Anders Krogh. "Hidden Markov Models for Prediction of Protein Features." In Protein Structure Prediction, 173–98. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-574-9_7.

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Conference papers on the topic "Protein structure prediction"

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Rokde, Chandrayani N., and Manali Kshirsagar. "Bioinformatics: Protein structure prediction." In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT). IEEE, 2013. http://dx.doi.org/10.1109/icccnt.2013.6726753.

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Wang, Dong, Wenzheng Bao, Shiyuan Han, Yuehui Chen, Likai Dong, and Jin Zhou. "Prediction of protein structure classes." In 2015 International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS). IEEE, 2015. http://dx.doi.org/10.1109/iccss.2015.7281154.

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SINGH, ROHIT, JINBO XU, and BONNIE BERGER. "STRUCT2NET: INTEGRATING STRUCTURE INTO PROTEIN-PROTEIN INTERACTION PREDICTION." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2005. http://dx.doi.org/10.1142/9789812701626_0037.

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Muhamud, Ahmed I., M. B. Abdelhalim, and Mai S. Mabrouk. "Extraction of prediction rules: Protein secondary structure prediction." In 2014 10th International Computer Engineering Conference (ICENCO). IEEE, 2014. http://dx.doi.org/10.1109/icenco.2014.7050426.

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Muggleton, S., R. D. King, and M. J. E. Sternberg. "Using logic for protein structure prediction." In Proceedings of the Twenty-Fifth Hawaii International Conference on System Sciences. IEEE, 1992. http://dx.doi.org/10.1109/hicss.1992.183221.

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Kehyayan, Christine, Nashat Mansour, and Hassan Khachfe. "Evolutionary Algorithm for Protein Structure Prediction." In 2008 International Conference on Advanced Computer Theory and Engineering (ICACTE). IEEE, 2008. http://dx.doi.org/10.1109/icacte.2008.130.

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Winter, Pawel, and Rasmus Fonseca. "Alpha Complexes in Protein Structure Prediction." In International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005251401780182.

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Ahmed, Walaa Fathy, and Walid Gomaa. "Approaches to prediction of protein structure." In 2011 9th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA). IEEE, 2011. http://dx.doi.org/10.1109/aiccsa.2011.6126616.

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Alam, Fardina Fathmiul, and Amarda Shehu. "Variational Autoencoders for Protein Structure Prediction." In BCB '20: 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3388440.3412471.

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Mansour, Nashat, Fatima Kanj, and Hassan Khachfe. "Evolutionary algorithm for protein structure prediction." In 2010 Sixth International Conference on Natural Computation (ICNC). IEEE, 2010. http://dx.doi.org/10.1109/icnc.2010.5584796.

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Reports on the topic "Protein structure prediction"

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Moult, J. Infrastructure for Collaborative Protein Structure Prediction. Office of Scientific and Technical Information (OSTI), April 2006. http://dx.doi.org/10.2172/895661.

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Avdjieva, Irena, Ivan Terziyski, Gergana Zahmanova, Anelia Nisheva, and Dimitar Vassilev. Fusion Protein Design with Computational Homologybased Structure Prediction. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, July 2021. http://dx.doi.org/10.7546/crabs.2021.07.07.

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DeRonne, Kevin W., and George Karypis. Effective Optimization Algorithms for Fragment-Assembly Based Protein Structure Prediction. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada444732.

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Honig, Barry. Protein structure and function prediction from physical chemical principles and database analysis. Office of Scientific and Technical Information (OSTI), September 2002. http://dx.doi.org/10.2172/804719.

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Gregurick, S. K. AB Initio Protein Tertiary Structure Prediction: Comparative-Genetic Algorithm with Graph Theoretical Methods. Office of Scientific and Technical Information (OSTI), April 2001. http://dx.doi.org/10.2172/834523.

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Geist, GA. Report on three Genomes to Life Workshops: Data Infrastructure, Modeling and Simulation, and Protein Structure Prediction. Office of Scientific and Technical Information (OSTI), September 2003. http://dx.doi.org/10.2172/885580.

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Phillips, C. A. Final report for LDRD project {open_quotes}A new approach to protein function and structure prediction{close_quotes}. Office of Scientific and Technical Information (OSTI), March 1997. http://dx.doi.org/10.2172/461264.

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Hart, W. E., and S. Istrail. Lattice and off-lattice side chain models of protein folding: Linear time structure prediction better than 86% of optimal. Office of Scientific and Technical Information (OSTI), August 1996. http://dx.doi.org/10.2172/425317.

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Fleming, Karen G. Energetics and Structure Prediction of the Network of Homo- and Hetero-Oligomers Formed by the Transmembrane Domains of the ErbReceptor Family of Proteins. Fort Belvoir, VA: Defense Technical Information Center, June 2006. http://dx.doi.org/10.21236/ada456142.

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Minz, Dror, Stefan J. Green, Noa Sela, Yitzhak Hadar, Janet Jansson, and Steven Lindow. Soil and rhizosphere microbiome response to treated waste water irrigation. United States Department of Agriculture, January 2013. http://dx.doi.org/10.32747/2013.7598153.bard.

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Research objectives : Identify genetic potential and community structure of soil and rhizosphere microbial community structure as affected by treated wastewater (TWW) irrigation. This objective was achieved through the examination soil and rhizosphere microbial communities of plants irrigated with fresh water (FW) and TWW. Genomic DNA extracted from soil and rhizosphere samples (Minz laboratory) was processed for DNA-based shotgun metagenome sequencing (Green laboratory). High-throughput bioinformatics was performed to compare both taxonomic and functional gene (and pathway) differences between sample types (treatment and location). Identify metabolic pathways induced or repressed by TWW irrigation. To accomplish this objective, shotgun metatranscriptome (RNA-based) sequencing was performed. Expressed genes and pathways were compared to identify significantly differentially expressed features between rhizosphere communities of plants irrigated with FW and TWW. Identify microbial gene functions and pathways affected by TWW irrigation*. To accomplish this objective, we will perform a metaproteome comparison between rhizosphere communities of plants irrigated with FW and TWW and selected soil microbial activities. Integration and evaluation of microbial community function in relation to its structure and genetic potential, and to infer the in situ physiology and function of microbial communities in soil and rhizospere under FW and TWW irrigation regimes. This objective is ongoing due to the need for extensive bioinformatics analysis. As a result of the capabilities of the new PI, we have also been characterizing the transcriptome of the plant roots as affected by the TWW irrigation and comparing the function of the plants to that of the microbiome. *This original objective was not achieved in the course of this study due to technical issues, especially the need to replace the American PIs during the project. However, the fact we were able to analyze more than one plant system as a result of the abilities of the new American PI strengthened the power of the conclusions derived from studies for the 1ˢᵗ and 2ⁿᵈ objectives. Background: As the world population grows, more urban waste is discharged to the environment, and fresh water sources are being polluted. Developing and industrial countries are increasing the use of wastewater and treated wastewater (TWW) for agriculture practice, thus turning the waste product into a valuable resource. Wastewater supplies a year- round reliable source of nutrient-rich water. Despite continuing enhancements in TWW quality, TWW irrigation can still result in unexplained and undesirable effects on crops. In part, these undesirable effects may be attributed to, among other factors, to the effects of TWW on the plant microbiome. Previous studies, including our own, have presented the TWW effect on soil microbial activity and community composition. To the best of our knowledge, however, no comprehensive study yet has been conducted on the microbial population associated BARD Report - Project 4662 Page 2 of 16 BARD Report - Project 4662 Page 3 of 16 with plant roots irrigated with TWW – a critical information gap. In this work, we characterize the effect of TWW irrigation on root-associated microbial community structure and function by using the most innovative tools available in analyzing bacterial community- a combination of microbial marker gene amplicon sequencing, microbial shotunmetagenomics (DNA-based total community and gene content characterization), microbial metatranscriptomics (RNA-based total community and gene content characterization), and plant host transcriptome response. At the core of this research, a mesocosm experiment was conducted to study and characterize the effect of TWW irrigation on tomato and lettuce plants. A focus of this study was on the plant roots, their associated microbial communities, and on the functional activities of plant root-associated microbial communities. We have found that TWW irrigation changes both the soil and root microbial community composition, and that the shift in the plant root microbiome associated with different irrigation was as significant as the changes caused by the plant host or soil type. The change in microbial community structure was accompanied by changes in the microbial community-wide functional potential (i.e., gene content of the entire microbial community, as determined through shotgun metagenome sequencing). The relative abundance of many genes was significantly different in TWW irrigated root microbiome relative to FW-irrigated root microbial communities. For example, the relative abundance of genes encoding for transporters increased in TWW-irrigated roots increased relative to FW-irrigated roots. Similarly, the relative abundance of genes linked to potassium efflux, respiratory systems and nitrogen metabolism were elevated in TWW irrigated roots when compared to FW-irrigated roots. The increased relative abundance of denitrifying genes in TWW systems relative FW systems, suggests that TWW-irrigated roots are more anaerobic compare to FW irrigated root. These gene functional data are consistent with geochemical measurements made from these systems. Specifically, the TWW irrigated soils had higher pH, total organic compound (TOC), sodium, potassium and electric conductivity values in comparison to FW soils. Thus, the root microbiome genetic functional potential can be correlated with pH, TOC and EC values and these factors must take part in the shaping the root microbiome. The expressed functions, as found by the metatranscriptome analysis, revealed many genes that increase in TWW-irrigated plant root microbial population relative to those in the FW-irrigated plants. The most substantial (and significant) were sodium-proton antiporters and Na(+)-translocatingNADH-quinoneoxidoreductase (NQR). The latter protein uses the cell respiratory machinery to harness redox force and convert the energy for efflux of sodium. As the roots and their microbiomes are exposed to the same environmental conditions, it was previously hypothesized that understanding the soil and rhizospheremicrobiome response will shed light on natural processes in these niches. This study demonstrate how newly available tools can better define complex processes and their downstream consequences, such as irrigation with water from different qualities, and to identify primary cues sensed by the plant host irrigated with TWW. From an agricultural perspective, many common practices are complicated processes with many ‘moving parts’, and are hard to characterize and predict. Multiple edaphic and microbial factors are involved, and these can react to many environmental cues. These complex systems are in turn affected by plant growth and exudation, and associated features such as irrigation, fertilization and use of pesticides. However, the combination of shotgun metagenomics, microbial shotgun metatranscriptomics, plant transcriptomics, and physical measurement of soil characteristics provides a mechanism for integrating data from highly complex agricultural systems to eventually provide for plant physiological response prediction and monitoring. BARD Report

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