Academic literature on the topic 'Protein structure prediction'

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Journal articles on the topic "Protein structure prediction"

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Cheng, Kaihui, Ce Liu, Qingkun Su, et al. "4D Diffusion for Dynamic Protein Structure Prediction with Reference and Motion Guidance." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 93–101. https://doi.org/10.1609/aaai.v39i1.31984.

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Protein structure prediction is pivotal for understanding the structure-function relationship of proteins, advancing biological research, and facilitating pharmaceutical development and experimental design. While deep learning methods and the expanded availability of experimental 3D protein structures have accelerated structure prediction, the dynamic nature of protein structures has received limited attention. This study introduces an innovative 4D diffusion model incorporating molecular dynamics (MD) simulation data to learn dynamic protein structures. Our approach is distinguished by the fo
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Deng, Haiyou, Ya Jia, and Yang Zhang. "Protein structure prediction." International Journal of Modern Physics B 32, no. 18 (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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Dr., Pankaj Malik, Sharma Anmol, Anand Anoushka, Baliyan Anmol, Raj Amisha, and Singh Jasleen. "Enhancing Alpha Fold Predictions with Transfer Learning: A Comprehensive Analysis and Benchmarking." Enhancing Alpha Fold Predictions with Transfer Learning: A Comprehensive Analysis and Benchmarking 8, no. 12 (2024): 7. https://doi.org/10.5281/zenodo.10499711.

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Protein structure prediction is a critical facet of molecular biology, with profound implications for understanding cellular processes and advancing drug discovery. AlphaFold, a state-of-the-art deep learning model, has demonstrated groundbreaking success in predicting protein structures. However, challenges persist, particularly in scenarios with limited data for specific protein families. This research investigates the augmentation of AlphaFold predictions through the application of transfer learning techniques, leveraging knowledge gained from one set of proteins to enhance predictions for
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Kazm, Ammar, Aida Ali, and Haslina Hashim. "Transformer Encoder with Protein Language Model for Protein Secondary Structure Prediction." Engineering, Technology & Applied Science Research 14, no. 2 (2024): 13124–32. http://dx.doi.org/10.48084/etasr.6855.

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In bioinformatics, protein secondary structure prediction plays a significant role in understanding protein function and interactions. This study presents the TE_SS approach, which uses a transformer encoder-based model and the Ankh protein language model to predict protein secondary structures. The research focuses on the prediction of nine classes of structures, according to the Dictionary of Secondary Structure of Proteins (DSSP) version 4. The model's performance was rigorously evaluated using various datasets. Additionally, this study compares the model with the state-of-the-art methods i
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Natalia Garavano, Francisca Sadosky, and Facundo Bulgheroni. "Protein Structure Prediction Tools and Computational Approaches." Fusion of Multidisciplinary Research, An International Journal 4, no. 2 (2023): 498–509. https://doi.org/10.63995/mwcu4408.

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Protein structure prediction is a critical aspect of bioinformatics, aimed at determining the three-dimensional configuration of proteins from their amino acid sequences. With the advent of sophisticated computational approaches, this field has seen significant advancements. Methods like homology modeling, which relies on the similarity between the target protein and known structures, and ab initio modeling, which predicts structures from scratch, have become fundamental tools. Additionally, molecular dynamics simulations and machine learning techniques, such as AlphaFold, have revolutionized
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El Hefnawi, Mahmoud M., Mohamed E. Hasan, Amal Mahmoud, et al. "Prediction and Analysis of Three-Dimensional Structure of the p7- Transactivated Protein1 of Hepatitis C Virus." Infectious Disorders - Drug Targets 19, no. 1 (2019): 55–66. http://dx.doi.org/10.2174/1871526518666171215123214.

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Background:The p7-transactivated protein1 of Hepatitis C virus is a small integral membrane protein of 127 amino acids, which is crucial for assembly and release of infectious virions. Ab initio or comparative modelling, is an essential tool to solve the problem of protein structure prediction and to comprehend the physicochemical fundamental of how proteins fold in nature.Results:Only one domain (1-127) of p7-transactivated protein1 has been predicted using the systematic in silico approach, ThreaDom. I-TASSER was ranked as the best server for full-length 3-D protein structural predictions of
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Haque, Neshatul, Jessica B. Wagenknecht, Brian D. Ratnasinghe, and Michael T. Zimmermann. "Systematic analysis of the relationship between fold-dependent flexibility and artificial intelligence protein structure prediction." PLOS ONE 19, no. 11 (2024): e0313308. http://dx.doi.org/10.1371/journal.pone.0313308.

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Artificial Intelligence (AI)-based deep learning methods for predicting protein structures are reshaping knowledge development and scientific discovery. Recent large-scale application of AI models for protein structure prediction has changed perceptions about complicated biological problems and empowered a new generation of structure-based hypothesis testing. It is well-recognized that proteins have a modular organization according to archetypal folds. However, it is yet to be determined if predicted structures are tuned to one conformation of flexible proteins or if they represent average con
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PALOPOLI, LUIGI, and GIORGIO TERRACINA. "CooPPS: A SYSTEM FOR THE COOPERATIVE PREDICTION OF PROTEIN STRUCTURES." Journal of Bioinformatics and Computational Biology 02, no. 03 (2004): 471–95. http://dx.doi.org/10.1142/s0219720004000697.

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Predicting the three-dimensional structure of proteins is a difficult task. In the last few years several approaches have been proposed for performing this task taking into account different protein chemical and physical properties. As a result, a growing number of protein structure prediction tools is becoming available, some of them specialized to work on either some aspects of the predictions or on some categories of proteins; however, they are still not sufficiently accurate and reliable for predicting all kinds of proteins. In this context, it is useful to jointly apply different predicti
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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 (1996): 1448–49. http://dx.doi.org/10.1126/science.274.5292.1448.b.

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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 pr
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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 mod
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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.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student submitted PDF version of thesis.<br>Includes bibliographical references (p. 109-124).<br>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 biolog
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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, co
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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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Webster, David. Protein Structure Prediction. Humana Press, 2000. http://dx.doi.org/10.1385/1592593682.

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Kihara, Daisuke, ed. Protein Structure Prediction. 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. Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-574-9.

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Kihara, Daisuke, ed. Protein Structure Prediction. 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. IRL Press at Oxford University Press, 1996.

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J, Kay, Lunt George G, and Osguthorpe D. J, eds. Protein structure, prediction, and design. Biochemical Society, 1990.

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

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Mohammed, Zaki, and Bystroff Chris. Protein Structure Prediction, Second Edition. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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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Pallavi, Gundala, R. Prasanna Kumar, and Ir Oviya. "Dual-Attention Protein Secondary Structure Prediction (DAPSS-Pred)." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724749.

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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. ACM, 2020. http://dx.doi.org/10.1145/3388440.3412471.

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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), 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, 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. Defense Technical Information Center, 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), 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), 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), 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), 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), 1996. http://dx.doi.org/10.2172/425317.

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Cheng, Jianlin. Deep Learning Prediction of Protein Complex Structures. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2371241.

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McDermott, Jason, Song Feng, Christine Chang, Darren Schmidt, and Vincent Danna. Structural- and Functional-Informed Machine Learning for Protein Function Prediction. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1988630.

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