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Journal articles on the topic 'Protein structure prediction'

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Author: Grafiati

Published: 4 June 2021

Last updated: 14 March 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Benner, S. A., D. L. Geroff, and J. D. Rozzell. "Protein Structure Prediction." Science 274, no. 5292 (November 29, 1996): 1448b—1449b. http://dx.doi.org/10.1126/science.274.5292.1448b.

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12

Garnier, J. "Protein structure prediction." Biochimie 72, no. 8 (August 1990): 513–24. http://dx.doi.org/10.1016/0300-9084(90)90115-w.

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13

Moult, John. "Protein structure prediction." Journal of Molecular Graphics and Modelling 18, no. 4-5 (2000): 553. http://dx.doi.org/10.1016/s1093-3263(00)80125-4.

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14

Baker, David. "Protein folding, structure prediction and design." Biochemical Society Transactions 42, no. 2 (March 20, 2014): 225–29. http://dx.doi.org/10.1042/bst20130055.

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I describe how experimental studies of protein folding have led to advances in protein structure prediction and protein design. I describe the finding that protein sequences are not optimized for rapid folding, the contact order–protein folding rate correlation, the incorporation of experimental insights into protein folding into the Rosetta protein structure production methodology and the use of this methodology to determine structures from sparse experimental data. I then describe the inverse problem (protein design) and give an overview of recent work on designing proteins with new structures and functions. I also describe the contributions of the general public to these efforts through the Rosetta@home distributed computing project and the FoldIt interactive protein folding and design game.

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15

Liu, Wei-min, and Kou-Chen Chou. "Prediction of protein secondary structure content." Protein Engineering, Design and Selection 12, no. 12 (December 1999): 1041–50. http://dx.doi.org/10.1093/protein/12.12.1041.

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16

PALOPOLI, LUIGI, and GIORGIO TERRACINA. "CooPPS: A SYSTEM FOR THE COOPERATIVE PREDICTION OF PROTEIN STRUCTURES." Journal of Bioinformatics and Computational Biology 02, no. 03 (September 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 prediction tools and combine their results in order to improve the quality of the predictions. However, several problems have to be solved in order to make this a viable possibility. In this paper a framework and a tool is proposed which allows: (i) definition of a common reference applicative domain for different prediction tools; (ii) characterization of prediction tools through evaluating some quality parameters; (iii) characterization of the performances of a team of predictors jointly applied over a prediction problem; (iv) the singling out of the best team for a prediction problem; and (v) the integration of predictor results in the team in order to obtain a unique prediction. A system implementing the various steps of the proposed framework (CooPPS) has been developed and several experiments for testing the effectiveness of the proposed approach have been carried out.

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17

Susanty, Meredita, Tati Erawati Rajab, and Rukman Hertadi. "A Review of Protein Structure Prediction using Deep Learning." BIO Web of Conferences 41 (2021): 04003. http://dx.doi.org/10.1051/bioconf/20214104003.

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Proteins are macromolecules composed of 20 types of amino acids in a specific order. Understanding how proteins fold is vital because its 3-dimensional structure determines the function of a protein. Prediction of protein structure based on amino acid strands and evolutionary information becomes the basis for other studies such as predicting the function, property or behaviour of a protein and modifying or designing new proteins to perform certain desired functions. Machine learning advances, particularly deep learning, are igniting a paradigm shift in scientific study. In this review, we summarize recent work in applying deep learning techniques to tackle problems in protein structural prediction. We discuss various deep learning approaches used to predict protein structure and future achievements and challenges. This review is expected to help provide perspectives on problems in biochemistry that can take advantage of the deep learning approach. Some of the unanswered challenges with current computational approaches are predicting the location and precision orientation of protein side chains, predicting protein interactions with DNA, RNA and other small molecules and predicting the structure of protein complexes.

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18

El Hefnawi, Mahmoud M., Mohamed E. Hasan, Amal Mahmoud, Yehia A. Khidr, Wessam H. El Behaidy, El-sayed A. El-absawy, and Alaa A. Hemeida. "Prediction and Analysis of Three-Dimensional Structure of the p7- Transactivated Protein1 of Hepatitis C Virus." Infectious Disorders - Drug Targets 19, no. 1 (February 4, 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 p7-transactivated protein1 where the benchmarked scoring system such as C-score, TM-score, RMSD and Z-score are used to obtain quantitative assessments of the I-TASSER models. Scanning protein motif databases, along with secondary and surface accessibility predictions integrated with post translational modification sites (PTMs) prediction revealed functional and protein binding motifs. Three protein binding motifs (two Asp/Glutamnse, CTNNB1- bd_N) with high sequence conservation and two PTMs prediction: Camp_phospho_site and Myristyl site were predicted using BLOCKS and PROSITE scan. These motifs and PTMs were related to the function of p7-transactivated protein1 protein in inducing ion channel/pore and release of infectious virions. Using SCOP, only one hit matched protein sequence at 71-120 was classified as small proteins and FYVE/PHD zinc finger superfamily.Conclusion:Integrating this information about the p7-transactivated protein1 with SCOP and CATH annotations of the templates facilitates the assignment of structure–function/ evolution relationships to the known and the newly determined protein structures.

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19

Haas, Jürgen, Alessandro Barbato, Tobias Schmidt, Steven Roth, Andrew Waterhouse, Stefan Bienert, Konstantin Arnold, Lorenza Bordoli, and Torsten Schwede. "Expanding our knowledge of the protein universe: Modelling of protein structures." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C491. http://dx.doi.org/10.1107/s2053273314095084.

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Computational modeling and prediction of three-dimensional macromolecular structures and complexes from their sequence has been a long standing goal in structural biology. Over the last two decades, a paradigm shift has occurred: starting from a large "knowledge gap" between the huge number of protein sequences compared to a small number of experimentally known structures, today, some form of structural information – either experimental or computational – is available for the majority of amino acids encoded by common model organism genomes. Methods for structure modeling and prediction have made substantial progress of the last decades, and template based homology modeling techniques have matured to a point where they are now routinely used to complement experimental techniques. However, computational modeling and prediction techniques often fall short in accuracy compared to high-resolution experimental structures, and it is often difficult to convey the expected accuracy and structural variability of a specific model. Retrospectively assessing the quality of blind structure prediction in comparison to experimental reference structures allows benchmarking the state-of-the-art in structure prediction and identifying areas which need further development. The Critical Assessment of Structure Prediction (CASP) experiment has for the last 20 years assessed the progress in the field of protein structure modeling based on predictions for ca. 100 blind prediction targets per experiment which are carefully evaluated by human experts. The "Continuous Model EvaluatiOn" (CAMEO) project aims to provide a fully automated blind assessment for prediction servers based on weekly pre-released sequences of the Protein Data Bank PDB. CAMEO has been made possible by the development of novel scoring methods such as lDDT, which are robust against domain movements to allow for automated continuous structure comparison without human intervention.

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20

Jumper, John, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, et al. "Highly accurate protein structure prediction with AlphaFold." Nature 596, no. 7873 (July 15, 2021): 583–89. http://dx.doi.org/10.1038/s41586-021-03819-2.

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AbstractProteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.

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21

Yang, Jianyi, Ivan Anishchenko, Hahnbeom Park, Zhenling Peng, Sergey Ovchinnikov, and David Baker. "Improved protein structure prediction using predicted interresidue orientations." Proceedings of the National Academy of Sciences 117, no. 3 (January 2, 2020): 1496–503. http://dx.doi.org/10.1073/pnas.1914677117.

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The prediction of interresidue contacts and distances from coevolutionary data using deep learning has considerably advanced protein structure prediction. Here, we build on these advances by developing a deep residual network for predicting interresidue orientations, in addition to distances, and a Rosetta-constrained energy-minimization protocol for rapidly and accurately generating structure models guided by these restraints. In benchmark tests on 13th Community-Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP13)- and Continuous Automated Model Evaluation (CAMEO)-derived sets, the method outperforms all previously described structure-prediction methods. Although trained entirely on native proteins, the network consistently assigns higher probability to de novo-designed proteins, identifying the key fold-determining residues and providing an independent quantitative measure of the “ideality” of a protein structure. The method promises to be useful for a broad range of protein structure prediction and design problems.

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22

Vangaveti, Sweta, Thom Vreven, Yang Zhang, and Zhiping Weng. "Integrating ab initio and template-based algorithms for protein–protein complex structure prediction." Bioinformatics 36, no. 3 (August 8, 2019): 751–57. http://dx.doi.org/10.1093/bioinformatics/btz623.

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Abstract Motivation Template-based and template-free methods have both been widely used in predicting the structures of protein–protein complexes. Template-based modeling is effective when a reliable template is available, while template-free methods are required for predicting the binding modes or interfaces that have not been previously observed. Our goal is to combine the two methods to improve computational protein–protein complex structure prediction. Results Here, we present a method to identify and combine high-confidence predictions of a template-based method (SPRING) with a template-free method (ZDOCK). Cross-validated using the protein–protein docking benchmark version 5.0, our method (ZING) achieved a success rate of 68.2%, outperforming SPRING and ZDOCK, with success rates of 52.1% and 35.9% respectively, when the top 10 predictions were considered per test case. In conclusion, a statistics-based method that evaluates and integrates predictions from template-based and template-free methods is more successful than either method independently. Availability and implementation ZING is available for download as a Github repository (https://github.com/weng-lab/ZING.git). Supplementary information Supplementary data are available at Bioinformatics online.

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23

Bernhofer, Michael, Christian Dallago, Tim Karl, Venkata Satagopam, Michael Heinzinger, Maria Littmann, Tobias Olenyi, et al. "PredictProtein - Predicting Protein Structure and Function for 29 Years." Nucleic Acids Research 49, W1 (May 17, 2021): W535—W540. http://dx.doi.org/10.1093/nar/gkab354.

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Abstract Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.

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24

Rychlewski, L., and A. Godzik. "Secondary structure prediction using segment similarity." Protein Engineering Design and Selection 10, no. 10 (October 1, 1997): 1143–53. http://dx.doi.org/10.1093/protein/10.10.1143.

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25

Tunyasuvunakool, Kathryn, Jonas Adler, Zachary Wu, Tim Green, Michal Zielinski, Augustin Žídek, Alex Bridgland, et al. "Highly accurate protein structure prediction for the human proteome." Nature 596, no. 7873 (July 22, 2021): 590–96. http://dx.doi.org/10.1038/s41586-021-03828-1.

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AbstractProtein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure1. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold2, at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective.

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26

AlQuraishi, Mohammed. "Protein-structure prediction revolutionized." Nature 596, no. 7873 (August 23, 2021): 487–88. http://dx.doi.org/10.1038/d41586-021-02265-4.

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27

Shortle, David. "Prediction of protein structure." Current Biology 10, no. 2 (January 2000): R49—R51. http://dx.doi.org/10.1016/s0960-9822(00)00290-6.

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28

Bowie, James U., and David Eisenberg. "Inverted protein structure prediction." Current Opinion in Structural Biology 3, no. 3 (June 1993): 437–44. http://dx.doi.org/10.1016/s0959-440x(05)80118-6.

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29

Marti-Renom, Marc A., Bozidar Yerkovich, and Andrej Sali. "Comparative Protein Structure Prediction." Current Protocols in Protein Science 28, no. 1 (June 2002): 2.9.1–2.9.22. http://dx.doi.org/10.1002/0471140864.ps0209s28.

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30

von Heijne, Gunnar. "Membrane protein structure prediction." Journal of Molecular Biology 225, no. 2 (May 1992): 487–94. http://dx.doi.org/10.1016/0022-2836(92)90934-c.

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31

Barton, Geoffrey J. "Protein secondary structure prediction." Current Opinion in Structural Biology 5, no. 3 (June 1995): 372–76. http://dx.doi.org/10.1016/0959-440x(95)80099-9.

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32

Deléage, G., and B. Roux. "An algorithm for protein secondary structure prediction based on class prediction." "Protein Engineering, Design and Selection" 1, no. 4 (1987): 289–94. http://dx.doi.org/10.1093/protein/1.4.289.

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33

Outeiral, Carlos, Daniel A. Nissley, and Charlotte M. Deane. "Current structure predictors are not learning the physics of protein folding." Bioinformatics 38, no. 7 (January 31, 2022): 1881–87. http://dx.doi.org/10.1093/bioinformatics/btab881.

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Abstract Summary Motivation. Predicting the native state of a protein has long been considered a gateway problem for understanding protein folding. Recent advances in structural modeling driven by deep learning have achieved unprecedented success at predicting a protein’s crystal structure, but it is not clear if these models are learning the physics of how proteins dynamically fold into their equilibrium structure or are just accurate knowledge-based predictors of the final state. Results. In this work, we compare the pathways generated by state-of-the-art protein structure prediction methods to experimental data about protein folding pathways. The methods considered were AlphaFold 2, RoseTTAFold, trRosetta, RaptorX, DMPfold, EVfold, SAINT2 and Rosetta. We find evidence that their simulated dynamics capture some information about the folding pathway, but their predictive ability is worse than a trivial classifier using sequence-agnostic features like chain length. The folding trajectories produced are also uncorrelated with experimental observables such as intermediate structures and the folding rate constant. These results suggest that recent advances in structure prediction do not yet provide an enhanced understanding of protein folding. Availability. The data underlying this article are available in GitHub at https://github.com/oxpig/structure-vs-folding/ Supplementary information Supplementary data are available at Bioinformatics online.

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34

Wheeler, Richard John. "A resource for improved predictions of Trypanosoma and Leishmania protein three-dimensional structure." PLOS ONE 16, no. 11 (November 11, 2021): e0259871. http://dx.doi.org/10.1371/journal.pone.0259871.

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AlphaFold2 and RoseTTAfold represent a transformative advance for predicting protein structure. They are able to make very high-quality predictions given a high-quality alignment of the protein sequence with related proteins. These predictions are now readily available via the AlphaFold database of predicted structures and AlphaFold or RoseTTAfold Colaboratory notebooks for custom predictions. However, predictions for some species tend to be lower confidence than model organisms. Problematic species include Trypanosoma cruzi and Leishmania infantum: important unicellular eukaryotic human parasites in an early-branching eukaryotic lineage. The cause appears to be due to poor sampling of this branch of life (Discoba) in the protein sequences databases used for the AlphaFold database and ColabFold. Here, by comprehensively gathering openly available protein sequence data for Discoba species, significant improvements to AlphaFold2 protein structure prediction over the AlphaFold database and ColabFold are demonstrated. This is made available as an easy-to-use tool for the parasitology community in the form of Colaboratory notebooks for generating multiple sequence alignments and AlphaFold2 predictions of protein structure for Trypanosoma, Leishmania and related species.

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35

Cretin, Gabriel, Tatiana Galochkina, Alexandre G. de Brevern, and Jean-Christophe Gelly. "PYTHIA: Deep Learning Approach for Local Protein Conformation Prediction." International Journal of Molecular Sciences 22, no. 16 (August 17, 2021): 8831. http://dx.doi.org/10.3390/ijms22168831.

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Protein Blocks (PBs) are a widely used structural alphabet describing local protein backbone conformation in terms of 16 possible conformational states, adopted by five consecutive amino acids. The representation of complex protein 3D structures as 1D PB sequences was previously successfully applied to protein structure alignment and protein structure prediction. In the current study, we present a new model, PYTHIA (predicting any conformation at high accuracy), for the prediction of the protein local conformations in terms of PBs directly from the amino acid sequence. PYTHIA is based on a deep residual inception-inside-inception neural network with convolutional block attention modules, predicting 1 of 16 PB classes from evolutionary information combined to physicochemical properties of individual amino acids. PYTHIA clearly outperforms the LOCUSTRA reference method for all PB classes and demonstrates great performance for PB prediction on particularly challenging proteins from the CASP14 free modelling category.

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Feng, Yonge, and Liaofu Luo. "Using long-range contact number information for protein secondary structure prediction." International Journal of Biomathematics 07, no. 05 (August 20, 2014): 1450052. http://dx.doi.org/10.1142/s1793524514500521.

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In this paper, we first combine tetra-peptide structural words with contact number for protein secondary structure prediction. We used the method of increment of diversity combined with quadratic discriminant analysis to predict the structure of central residue for a sequence fragment. The method is used tetra-peptide structural words and long-range contact number as information resources. The accuracy of Q3 is over 83% in 194 proteins. The accuracies of predicted secondary structures for 20 amino acid residues are ranged from 81% to 88%. Moreover, we have introduced the residue long-range contact, which directly indicates the separation of contacting residue in terms of the position in the sequence, and examined the negative influence of long-range residue interactions on predicting secondary structure in a protein. The method is also compared with existing prediction methods. The results show that our method is more effective in protein secondary structures prediction.

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37

Lupas, Andrei N., Joana Pereira, Vikram Alva, Felipe Merino, Murray Coles, and Marcus D. Hartmann. "The breakthrough in protein structure prediction." Biochemical Journal 478, no. 10 (May 24, 2021): 1885–90. http://dx.doi.org/10.1042/bcj20200963.

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Proteins are the essential agents of all living systems. Even though they are synthesized as linear chains of amino acids, they must assume specific three-dimensional structures in order to manifest their biological activity. These structures are fully specified in their amino acid sequences — and therefore in the nucleotide sequences of their genes. However, the relationship between sequence and structure, known as the protein folding problem, has remained elusive for half a century, despite sustained efforts. To measure progress on this problem, a series of doubly blind, biennial experiments called CASP (critical assessment of structure prediction) were established in 1994. We were part of the assessment team for the most recent CASP experiment, CASP14, where we witnessed an astonishing breakthrough by DeepMind, the leading artificial intelligence laboratory of Alphabet Inc. The models filed by DeepMind's structure prediction team using the program AlphaFold2 were often essentially indistinguishable from experimental structures, leading to a consensus in the community that the structure prediction problem for single protein chains has been solved. Here, we will review the path to CASP14, outline the method employed by AlphaFold2 to the extent revealed, and discuss the implications of this breakthrough for the life sciences.

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38

Palopoli, Luigi, Simona E. Rombo, Giorgio Terracina, Giuseppe Tradigo, and Pierangelo Veltri. "Improving protein secondary structure predictions by prediction fusion." Information Fusion 10, no. 3 (July 2009): 217–32. http://dx.doi.org/10.1016/j.inffus.2008.11.004.

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39

Bouatta, Nazim, Peter Sorger, and Mohammed AlQuraishi. "Protein structure prediction by AlphaFold2: are attention and symmetries all you need?" Acta Crystallographica Section D Structural Biology 77, no. 8 (July 29, 2021): 982–91. http://dx.doi.org/10.1107/s2059798321007531.

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The functions of most proteins result from their 3D structures, but determining their structures experimentally remains a challenge, despite steady advances in crystallography, NMR and single-particle cryoEM. Computationally predicting the structure of a protein from its primary sequence has long been a grand challenge in bioinformatics, intimately connected with understanding protein chemistry and dynamics. Recent advances in deep learning, combined with the availability of genomic data for inferring co-evolutionary patterns, provide a new approach to protein structure prediction that is complementary to longstanding physics-based approaches. The outstanding performance of AlphaFold2 in the recent Critical Assessment of protein Structure Prediction (CASP14) experiment demonstrates the remarkable power of deep learning in structure prediction. In this perspective, we focus on the key features of AlphaFold2, including its use of (i) attention mechanisms and Transformers to capture long-range dependencies, (ii) symmetry principles to facilitate reasoning over protein structures in three dimensions and (iii) end-to-end differentiability as a unifying framework for learning from protein data. The rules of protein folding are ultimately encoded in the physical principles that underpin it; to conclude, the implications of having a powerful computational model for structure prediction that does not explicitly rely on those principles are discussed.

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De Meutter, Joëlle, and Erik Goormaghtigh. "Evaluation of protein secondary structure from FTIR spectra improved after partial deuteration." European Biophysics Journal 50, no. 3-4 (February 3, 2021): 613–28. http://dx.doi.org/10.1007/s00249-021-01502-y.

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AbstractFTIR spectroscopy has become a major tool to determine protein secondary structure. One of the identified obstacle for reaching better predictions is the strong overlap of bands assigned to different secondary structures. Yet, while for instance disordered structures and α-helical structures absorb almost at the same wavenumber, the absorbance bands are differentially shifted upon deuteration, in part because exchange is much faster for disordered structures. We recorded the FTIR spectra of 85 proteins at different stages of hydrogen/deuterium exchange process using protein microarrays and infrared imaging for high throughput measurements. Several methods were used to relate spectral shape to secondary structure content. While in absolute terms, β-sheet is always better predicted than α-helix content, results consistently indicate an improvement of secondary structure predictions essentially for the α-helix and the category called “Others” (grouping random, turns, bends, etc.) after 15 min of exchange. On the contrary, the β-sheet fraction is better predicted in non-deuterated conditions. Using partial least square regression, the error of prediction for the α-helix content is reduced after 15-min deuteration. Further deuteration degrades the prediction. Error on the prediction for the “Others” structures also decreases after 15-min deuteration. Cross-validation or a single 25-protein test set result in the same overall conclusions.

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Boscott, P. E., G. J. Barton, and W. G. Richards. "Secondary structure prediction for modelling by homology." "Protein Engineering, Design and Selection" 6, no. 3 (1993): 261–66. http://dx.doi.org/10.1093/protein/6.3.261.

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Gromiha, M. M., and S. Selvaraj. "Protein secondary structure prediction in different structural classes." Protein Engineering Design and Selection 11, no. 4 (April 1, 1998): 249–51. http://dx.doi.org/10.1093/protein/11.4.249.

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Tung, Chi-Hua, Chi-Wei Chen, Ren-Chao Guo, Hui-Fuang Ng, and Yen-Wei Chu. "QuaBingo: A Prediction System for Protein Quaternary Structure Attributes Using Block Composition." BioMed Research International 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/9480276.

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Background. Quaternary structures of proteins are closely relevant to gene regulation, signal transduction, and many other biological functions of proteins. In the current study, a new method based on protein-conserved motif composition in block format for feature extraction is proposed, which is termed block composition.Results. The protein quaternary assembly states prediction system which combines blocks with functional domain composition, called QuaBingo, is constructed by three layers of classifiers that can categorize quaternary structural attributes of monomer, homooligomer, and heterooligomer. The building of the first layer classifier uses support vector machines (SVM) based on blocks and functional domains of proteins, and the second layer SVM was utilized to process the outputs of the first layer. Finally, the result is determined by the Random Forest of the third layer. We compared the effectiveness of the combination of block composition, functional domain composition, and pseudoamino acid composition of the model. In the 11 kinds of functional protein families, QuaBingo is 23% of Matthews Correlation Coefficient (MCC) higher than the existing prediction system. The results also revealed the biological characterization of the top five block compositions.Conclusions. QuaBingo provides better predictive ability for predicting the quaternary structural attributes of proteins.

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Tian, Wei, Meishan Lin, Ke Tang, Jie Liang, and Hammad Naveed. "High-resolution structure prediction ofβ-barrel membrane proteins." Proceedings of the National Academy of Sciences 115, no. 7 (January 29, 2018): 1511–16. http://dx.doi.org/10.1073/pnas.1716817115.

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β-Barrel membrane proteins (βMPs) play important roles, but knowledge of their structures is limited. We have developed a method to predict their 3D structures. We predict strand registers and construct transmembrane (TM) domains of βMPs accurately, including proteins for which no prediction has been attempted before. Our method also accurately predicts structures from protein families with a limited number of sequences and proteins with novel folds. An average main-chain rmsd of 3.48 Å is achieved between predicted and experimentally resolved structures of TM domains, which is a significant improvement (>3 Å) over a recent study. For βMPs with NMR structures, the deviation between predictions and experimentally solved structures is similar to the difference among the NMR structures, indicating excellent prediction accuracy. Moreover, we can now accurately model the extended β-barrels and loops in non-TM domains, increasing the overall coverage of structure prediction by>30%. Our method is general and can be applied to genome-wide structural prediction of βMPs.

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Yi, Wenjing, Ao Sun, Manman Liu, Xiaoqing Liu, Wei Zhang, and Qi Dai. "Comparative Study on Feature Selection in Protein Structure and Function Prediction." Computational and Mathematical Methods in Medicine 2022 (October 11, 2022): 1–13. http://dx.doi.org/10.1155/2022/1650693.

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Many effective methods extract and fuse different protein features to study the relationship between protein sequence, structure, and function, but different methods have preferences in solving the research of protein structure and function, which requires selecting valuable and contributing features to design more effective prediction methods. This work mainly focused on the feature selection methods in the study of protein structure and function, and systematically compared and analyzed the efficiency of different feature selection methods in the prediction of protein structures, protein disorders, protein molecular chaperones, and protein solubility. The results show that the feature selection method based on nonlinear SVM performs best in protein structure prediction, protein solubility prediction, protein molecular chaperone prediction, and protein solubility prediction. After selection, the accuracy of features is improved by 13.16% ~71%, especially the Kmer features and PSSM features of proteins.

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Perez, Alberto, Joseph A. Morrone, Emiliano Brini, Justin L. MacCallum, and Ken A. Dill. "Blind protein structure prediction using accelerated free-energy simulations." Science Advances 2, no. 11 (November 2016): e1601274. http://dx.doi.org/10.1126/sciadv.1601274.

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We report a key proof of principle of a new acceleration method [Modeling Employing Limited Data (MELD)] for predicting protein structures by molecular dynamics simulation. It shows that such Boltzmann-satisfying techniques are now sufficiently fast and accurate to predict native protein structures in a limited test within the Critical Assessment of Structure Prediction (CASP) community-wide blind competition.

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DOVIDCHENKO, NIKITA V., NATALYA S. BOGATYREVA, and OXANA V. GALZITSKAYA. "PREDICTION OF LOOP REGIONS IN PROTEIN SEQUENCE." Journal of Bioinformatics and Computational Biology 06, no. 05 (October 2008): 1035–47. http://dx.doi.org/10.1142/s0219720008003758.

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We suggest an algorithm that inputs a protein sequence and outputs a decomposition of the protein chain into a regular part including secondary structures and a nonregular part corresponding to loop regions. We have analyzed loop regions in a protein dataset of 3,769 globular domains and defined the optimal parameters for this prediction: the threshold between regular and nonregular regions and the optimal window size for averaging procedures using the scale of the expected number of contacts in a globular state and entropy scale as the number of degrees of freedom for the angles φ, ψ, and χ for each amino acid. Comparison with known methods demonstrates that our method gives the same results as the well-known ALB method based on physical properties of amino acids (the percentage of true predictions is 64% against 66%), and worse prediction for regular and nonregular regions than PSIPRED (Protein Structure Prediction Server) without alignment of homologous proteins (the percentage of true predictions is 73%). The potential advantage of the suggested approach is that the predicted set of loops can be used to find patterns of rigid and flexible loops as possible candidates to play a structure/function role as well as a role of antigenic determinants.

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Zaman, Ahmed Bin, and Amarda Shehu. "Building maps of protein structure spaces in template-free protein structure prediction." Journal of Bioinformatics and Computational Biology 17, no. 06 (December 2019): 1940013. http://dx.doi.org/10.1142/s0219720019400134.

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An important goal in template-free protein structure prediction is how to control the quality of computed tertiary structures of a target amino-acid sequence. Despite great advances in algorithmic research, given the size, dimensionality, and inherent characteristics of the protein structure space, this task remains exceptionally challenging. It is current practice to aim to generate as many structures as can be afforded so as to increase the likelihood that some of them will reside near the sought but unknown biologically-active/native structure. When operating within a given computational budget, this is impractical and uninformed by any metrics of interest. In this paper, we propose instead to equip algorithms that generate tertiary structures, also known as decoy generation algorithms, with memory of the protein structure space that they explore. Specifically, we propose an evolving, granularity-controllable map of the protein structure space that makes use of low-dimensional representations of protein structures. Evaluations on diverse target sequences that include recent hard CASP targets show that drastic reductions in storage can be made without sacrificing decoy quality. The presented results make the case that integrating a map of the protein structure space is a promising mechanism to enhance decoy generation algorithms in template-free protein structure prediction.

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Whisstock, James C., and Arthur M. Lesk. "Prediction of protein function from protein sequence and structure." Quarterly Reviews of Biophysics 36, no. 3 (August 2003): 307–40. http://dx.doi.org/10.1017/s0033583503003901.

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1. Introduction 3082. Plan of this article 3123. Natural mechanisms of development of novel protein functions 3133.1 Divergence 3133.2 Recruitment 3163.3 ‘Mixing and matching’ of domains, including duplication/oligomerization, and domain swapping or fusion 3164. Classification schemes for protein functions 3174.1 General schemes 3174.2 The EC classification 3184.3 Combined classification schemes 3194.4 The Gene Ontology Consortium 3215. Methods for assigning protein function 3215.1 Detection of protein homology from sequence, and its application to function assignment 3215.2 Detection of structural similarity, protein structure classifications, and structure/function correlations 3265.3 Function prediction from amino-acid sequence 3275.3.1 Databases of single motifs 3285.3.2 Databases of profiles 3295.3.3 Databases of multiple motifs 3305.3.4 Precompiled families 3315.3.5 Function identification from sequence by feature extraction 3315.4 Methods making use of structural data 3326. Applications of full-organism information: inferences from genomic context and protein interaction patterns 3347. Conclusions 3358. Acknowledgements 3359. References 335The sequence of a genome contains the plans of the possible life of an organism, but implementation of genetic information depends on the functions of the proteins and nucleic acids that it encodes. Many individual proteins of known sequence and structure present challenges to the understanding of their function. In particular, a number of genes responsible for diseases have been identified but their specific functions are unknown. Whole-genome sequencing projects are a major source of proteins of unknown function. Annotation of a genome involves assignment of functions to gene products, in most cases on the basis of amino-acid sequence alone. 3D structure can aid the assignment of function, motivating the challenge of structural genomics projects to make structural information available for novel uncharacterized proteins. Structure-based identification of homologues often succeeds where sequence-alone-based methods fail, because in many cases evolution retains the folding pattern long after sequence similarity becomes undetectable. Nevertheless, prediction of protein function from sequence and structure is a difficult problem, because homologous proteins often have different functions. Many methods of function prediction rely on identifying similarity in sequence and/or structure between a protein of unknown function and one or more well-understood proteins. Alternative methods include inferring conservation patterns in members of a functionally uncharacterized family for which many sequences and structures are known. However, these inferences are tenuous. Such methods provide reasonable guesses at function, but are far from foolproof. It is therefore fortunate that the development of whole-organism approaches and comparative genomics permits other approaches to function prediction when the data are available. These include the use of protein–protein interaction patterns, and correlations between occurrences of related proteins in different organisms, as indicators of functional properties. Even if it is possible to ascribe a particular function to a gene product, the protein may have multiple functions. A fundamental problem is that function is in many cases an ill-defined concept. In this article we review the state of the art in function prediction and describe some of the underlying difficulties and successes.

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Skorupka, Katarzyna, Seong Kyu Han, Hyun-Jun Nam, Sanguk Kim, and Salem Faham. "Protein design by fusion: implications for protein structure prediction and evolution." Acta Crystallographica Section D Biological Crystallography 69, no. 12 (November 19, 2013): 2451–60. http://dx.doi.org/10.1107/s0907444913022701.

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Domain fusion is a useful tool in protein design. Here, the structure of a fusion of the heterodimeric flagella-assembly proteins FliS and FliC is reported. Although the ability of the fusion protein to maintain the structure of the heterodimer may be apparent, threading-based structural predictions do not properly fuse the heterodimer. Additional examples of naturally occurring heterodimers that are homologous to full-length proteins were identified. These examples highlight that the designed protein was engineered by the same tools as used in the natural evolution of proteins and that heterodimeric structures contain a wealth of information, currently unused, that can improve structural predictions.

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