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Current Medicinal Chemistry

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ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

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Review Article

Current Computational Methods for Protein-peptide Complex Structure Prediction

Author(s): Chao Yang*, Xianjin Xu and Changcheng Xiang

Volume 31, Issue 26, 2024

Published on: 06 October, 2023

Page: [4058 - 4078] Pages: 21

DOI: 10.2174/0109298673263447230920151524

Price: $65

Abstract

Peptide-mediated protein-protein interactions (PPIs) play an important role in various biological processes. The development of peptide-based drugs to modulate PPIs has attracted increasing attention due to the advantages of high specificity and low toxicity. In the development of peptide-based drugs, one of the most important steps is to determine the interaction details between the peptide and the target protein. In addition to experimental methods, recently developed computational methods provide a cost-effective way for studying protein-peptide interactions. In this article, we carefully reviewed recently developed protein-peptide docking methods, which were classified into three groups: template-based docking, template-free docking, and hybrid method. Then, we presented available benchmarking sets and evaluation metrics for assessing protein-peptide docking performance. Furthermore, we discussed the use of molecular dynamics simulations, as well as deep learning approaches in protein-peptide complex prediction.

« Previous Next »
[1]
Wells, J.A.; McClendon, C.L. Reaching for high-hanging fruit in drug discovery at protein–protein interfaces. Nature, 2007, 450(7172), 1001-1009.
[http://dx.doi.org/10.1038/nature06526] [PMID: 18075579]
[2]
Stelzl, U.; Worm, U.; Lalowski, M.; Haenig, C.; Brembeck, F.H.; Goehler, H.; Stroedicke, M.; Zenkner, M.; Schoenherr, A.; Koeppen, S.; Timm, J.; Mintzlaff, S.; Abraham, C.; Bock, N.; Kietzmann, S.; Goedde, A.; Toksöz, E.; Droege, A.; Krobitsch, S.; Korn, B.; Birchmeier, W.; Lehrach, H.; Wanker, E.E. A human protein-protein interaction network: A resource for annotating the proteome. Cell, 2005, 122(6), 957-968.
[http://dx.doi.org/10.1016/j.cell.2005.08.029] [PMID: 16169070]
[3]
Rual, J.F.; Venkatesan, K.; Hao, T.; Hirozane-Kishikawa, T.; Dricot, A.; Li, N.; Berriz, G.F.; Gibbons, F.D.; Dreze, M.; Ayivi-Guedehoussou, N.; Klitgord, N.; Simon, C.; Boxem, M.; Milstein, S.; Rosenberg, J.; Goldberg, D.S.; Zhang, L.V.; Wong, S.L.; Franklin, G.; Li, S.; Albala, J.S.; Lim, J.; Fraughton, C.; Llamosas, E.; Cevik, S.; Bex, C.; Lamesch, P.; Sikorski, R.S.; Vandenhaute, J.; Zoghbi, H.Y.; Smolyar, A.; Bosak, S.; Sequerra, R.; Doucette-Stamm, L.; Cusick, M.E.; Hill, D.E.; Roth, F.P.; Vidal, M. Towards a proteome-scale map of the human protein–protein interaction network. Nature, 2005, 437(7062), 1173-1178.
[http://dx.doi.org/10.1038/nature04209] [PMID: 16189514]
[4]
Arkin, M.R.; Whitty, A. The road less traveled: Modulating signal transduction enzymes by inhibiting their protein–protein interactions. Curr. Opin. Chem. Biol., 2009, 13(3), 284-290.
[http://dx.doi.org/10.1016/j.cbpa.2009.05.125] [PMID: 19553156]
[5]
Nero, T.L.; Morton, C.J.; Holien, J.K.; Wielens, J.; Parker, M.W. Oncogenic protein interfaces: Small molecules, big challenges. Nat. Rev. Cancer, 2014, 14(4), 248-262.
[http://dx.doi.org/10.1038/nrc3690] [PMID: 24622521]
[6]
Ideker, T.; Sharan, R. Protein networks in disease. Genome Res., 2008, 18(4), 644-652.
[http://dx.doi.org/10.1101/gr.071852.107] [PMID: 18381899]
[7]
Petsalaki, E.; Russell, R.B. Peptide-mediated interactions in biological systems: New discoveries and applications. Curr. Opin. Biotechnol., 2008, 19(4), 344-350.
[http://dx.doi.org/10.1016/j.copbio.2008.06.004] [PMID: 18602004]
[8]
Hershberger, S.; Lee, S.G.; Chmielewski, J. Scaffolds for blocking protein-protein interactions. Curr. Top. Med. Chem., 2007, 7(10), 928-942.
[http://dx.doi.org/10.2174/156802607780906726] [PMID: 17508924]
[9]
Zhou, P.; Wang, C.; Ren, Y.; Yang, C.; Tian, F. Computational peptidology: A new and promising approach to therapeutic peptide design. Curr. Med. Chem., 2013, 20(15), 1985-1996.
[http://dx.doi.org/10.2174/0929867311320150005] [PMID: 23317161]
[10]
Eichler, J. Peptides as protein binding site mimetics. Curr. Opin. Chem. Biol., 2008, 12(6), 707-713.
[http://dx.doi.org/10.1016/j.cbpa.2008.09.023] [PMID: 18935974]
[11]
London, N.; Raveh, B.; Movshovitz-Attias, D.; Schueler-Furman, O. Can self-inhibitory peptides be derived from the interfaces of globular protein-protein interactions? Proteins, 2010, 78(15), 3140-3149.
[http://dx.doi.org/10.1002/prot.22785] [PMID: 20607702]
[12]
Bruzzoni-Giovanelli, H.; Alezra, V.; Wolff, N.; Dong, C.Z.; Tuffery, P.; Rebollo, A. Interfering peptides targeting protein–protein interactions: The next generation of drugs? Drug Discov. Today, 2018, 23(2), 272-285.
[http://dx.doi.org/10.1016/j.drudis.2017.10.016] [PMID: 29097277]
[13]
Dagliyan, O.; Proctor, E.A.; D’Auria, K.M.; Ding, F.; Dokholyan, N.V. Structural and dynamic determinants of protein-peptide recognition. Structure, 2011, 19(12), 1837-1845.
[http://dx.doi.org/10.1016/j.str.2011.09.014] [PMID: 22153506]
[14]
Wójcik, P.; Berlicki, Ł. Peptide-based inhibitors of protein–protein interactions. Bioorg. Med. Chem. Lett., 2016, 26(3), 707-713.
[http://dx.doi.org/10.1016/j.bmcl.2015.12.084] [PMID: 26764190]
[15]
Nevola, L.; Giralt, E. Modulating protein–protein interactions: The potential of peptides. Chem. Commun., 2015, 51(16), 3302-3315.
[http://dx.doi.org/10.1039/C4CC08565E] [PMID: 25578807]
[16]
Cunningham, A.D.; Qvit, N.; Mochly-Rosen, D. Peptides and peptidomimetics as regulators of protein–protein interactions. Curr. Opin. Struct. Biol., 2017, 44, 59-66.
[http://dx.doi.org/10.1016/j.sbi.2016.12.009] [PMID: 28063303]
[17]
Wang, X.; Ni, D.; Liu, Y.; Lu, S. Rational design of peptide-based inhibitors disrupting protein-protein interactions. Front Chem., 2021, 9, 682675.
[http://dx.doi.org/10.3389/fchem.2021.682675] [PMID: 34017824]
[18]
Corbi-Verge, C.; Kim, P.M. Motif mediated protein-protein interactions as drug targets. Cell Commun. Signal., 2016, 14(1), 8.
[http://dx.doi.org/10.1186/s12964-016-0131-4] [PMID: 26936767]
[19]
Sun, H.; Stuckey, J.A.; Nikolovska-Coleska, Z.; Qin, D.; Meagher, J.L.; Qiu, S.; Lu, J.; Yang, C.Y.; Saito, N.G.; Wang, S. Structure-based design, synthesis, evaluation, and crystallographic studies of conformationally constrained Smac mimetics as inhibitors of the X-linked inhibitor of apoptosis protein (XIAP). J. Med. Chem., 2008, 51(22), 7169-7180.
[http://dx.doi.org/10.1021/jm8006849] [PMID: 18954041]
[20]
Stewart-Ornstein, J.; Iwamoto, Y.; Miller, M.A.; Prytyskach, M.A.; Ferretti, S.; Holzer, P.; Kallen, J.; Furet, P.; Jambhekar, A.; Forrester, W.C.; Weissleder, R.; Lahav, G. p53 dynamics vary between tissues and are linked with radiation sensitivity. Nat. Commun., 2021, 12(1), 898.
[http://dx.doi.org/10.1038/s41467-021-21145-z] [PMID: 33563973]
[21]
Furet, P.; Bordas, V.; Le Douget, M.; Salem, B.; Mesrouze, Y.; Imbach-Weese, P.; Sellner, H.; Voegtle, M.; Soldermann, N.; Chapeau, E.; Wartmann, M.; Scheufler, C.; Fernandez, C.; Kallen, J.; Guagnano, V.; Chène, P.; Schmelzle, T. The first class of small molecules potently disrupting the YAP-TEAD interaction by direct competition. ChemMedChem, 2022, 17(19), e202200303.
[http://dx.doi.org/10.1002/cmdc.202200303] [PMID: 35950546]
[22]
Ciemny, M.; Kurcinski, M.; Kamel, K.; Kolinski, A.; Alam, N.; Schueler-Furman, O.; Kmiecik, S. Protein–peptide docking: Opportunities and challenges. Drug Discov. Today, 2018, 23(8), 1530-1537.
[http://dx.doi.org/10.1016/j.drudis.2018.05.006] [PMID: 29733895]
[23]
Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31(2), 455-461.
[PMID: 19499576]
[24]
Verdonk, M.L.; Cole, J.C.; Hartshorn, M.J.; Murray, C.W.; Taylor, R.D. Improved protein-ligand docking using GOLD. Proteins, 2003, 52(4), 609-623.
[http://dx.doi.org/10.1002/prot.10465] [PMID: 12910460]
[25]
Jain, A.N. Surflex-Dock 2.1: Robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search. J. Comput. Aided Mol. Des., 2007, 21(5), 281-306.
[http://dx.doi.org/10.1007/s10822-007-9114-2] [PMID: 17387436]
[26]
Su, M.; Yang, Q.; Du, Y.; Feng, G.; Liu, Z.; Li, Y.; Wang, R. Comparative assessment of scoring functions: The CASF-2016 update. J. Chem. Inf. Model., 2019, 59(2), 895-913.
[http://dx.doi.org/10.1021/acs.jcim.8b00545] [PMID: 30481020]
[27]
Hauser, A.S.; Windshügel, B. LEADS-PEP: A benchmark data set for assessment of peptide docking performance. J. Chem. Inf. Model., 2016, 56(1), 188-200.
[http://dx.doi.org/10.1021/acs.jcim.5b00234] [PMID: 26651532]
[28]
Alam, N.; Goldstein, O.; Xia, B.; Porter, K.A.; Kozakov, D.; Schueler-Furman, O. High-resolution global peptide-protein docking using fragments-based PIPER-FlexPepDock. PLOS Comput. Biol., 2017, 13(12), e1005905.
[http://dx.doi.org/10.1371/journal.pcbi.1005905] [PMID: 29281622]
[29]
Xu, X.; Zou, X. Predicting protein–peptide complex structures by accounting for peptide flexibility and the physicochemical environment. J. Chem. Inf. Model., 2022, 62(1), 27-39.
[http://dx.doi.org/10.1021/acs.jcim.1c00836] [PMID: 34931833]
[30]
Trellet, M.; Melquiond, A.S.J.; Bonvin, A.M.J.J. A unified conformational selection and induced fit approach to protein-peptide docking. PLoS One, 2013, 8(3), e58769.
[http://dx.doi.org/10.1371/journal.pone.0058769] [PMID: 23516555]
[31]
Geng, C.; Narasimhan, S.; Rodrigues, J.P.; Bonvin, A.M. Information-driven, ensemble flexible peptide docking using HADDOCK. Methods. Mol. Biol., 2017, 109-138.
[32]
Trellet, M.; Melquiond, A.S.; Bonvin, A.M. Information- driven modeling of protein-peptide complexes. In: Methods. Mol. Biol; , 2015; pp. 221-239.
[33]
de Vries, S.J.; Rey, J.; Schindler, C.E.M.; Zacharias, M.; Tuffery, P. The pepATTRACT web server for blind, large-scale peptide–protein docking. Nucleic Acids Res., 2017, 45(W1), W361-W364.
[http://dx.doi.org/10.1093/nar/gkx335] [PMID: 28460116]
[34]
Khramushin, A.; Ben-Aharon, Z.; Tsaban, T.; Varga, J.K.; Avraham, O.; Schueler-Furman, O. Matching protein surface structural patches for high-resolution blind peptide docking. Proc. Natl. Acad. Sci., 2022, 119(18), e2121153119.
[http://dx.doi.org/10.1073/pnas.2121153119] [PMID: 35482919]
[35]
Chen, J.N.; Jiang, F.; Wu, Y.D. Accurate prediction for protein–peptide binding based on high-temperature molecular dynamics simulations. J. Chem. Theory Comput., 2022, 18(10), 6386-6395.
[http://dx.doi.org/10.1021/acs.jctc.2c00743] [PMID: 36149394]
[36]
Tsaban, T.; Varga, J.K.; Avraham, O.; Ben-Aharon, Z.; Khramushin, A.; Schueler-Furman, O. Harnessing protein folding neural networks for peptide–protein docking. Nat. Commun., 2022, 13(1), 176.
[http://dx.doi.org/10.1038/s41467-021-27838-9] [PMID: 35013344]
[37]
Evans, R.; O’Neill, M.; Pritzel, A.; Antropova, N.; Senior, A.; Green, T.; Žídek, A.; Bates, R.; Blackwell, S.; Yim, J. Protein complex prediction with AlphaFold-Multimer. BioRxiv, 2021, 2021.2010.
[38]
Shanker, S.; Sanner, M.F. Predicting protein–peptide interactions: Benchmarking deep learning techniques and a comparison with focused docking. J. Chem. Inf. Model., 2023, 63(10), 3158-3170.
[http://dx.doi.org/10.1021/acs.jcim.3c00602] [PMID: 37167566]
[39]
Santos, K.B.; Guedes, I.A.; Karl, A.L.M.; Dardenne, L.E. Highly flexible ligand docking: Benchmarking of the DockThor program on the LEADS-PEP protein–peptide data set. J. Chem. Inf. Model., 2020, 60(2), 667-683.
[http://dx.doi.org/10.1021/acs.jcim.9b00905] [PMID: 31922754]
[40]
Vanhee, P.; van der Sloot, A.M.; Verschueren, E.; Serrano, L.; Rousseau, F.; Schymkowitz, J. Computational design of peptide ligands. Trends Biotechnol., 2011, 29(5), 231-239.
[http://dx.doi.org/10.1016/j.tibtech.2011.01.004] [PMID: 21316780]
[41]
Stein, A.; Pache, R.A.; Bernadó, P.; Pons, M.; Aloy, P. Dynamic interactions of proteins in complex networks: A more structured view. FEBS J., 2009, 276(19), 5390-5405.
[http://dx.doi.org/10.1111/j.1742-4658.2009.07251.x] [PMID: 19712106]
[42]
Lee, H.; Heo, L.; Lee, M.S.; Seok, C. GalaxyPepDock: A protein–peptide docking tool based on interaction similarity and energy optimization. Nucleic Acids Res., 2015, 43(W1), W431-W435.
[http://dx.doi.org/10.1093/nar/gkv495] [PMID: 25969449]
[43]
Johansson-Åkhe, I.; Mirabello, C.; Wallner, B. InterPep2: Global peptide–protein docking using interaction surface templates. Bioinformatics, 2020, 36(8), 2458-2465.
[http://dx.doi.org/10.1093/bioinformatics/btaa005] [PMID: 31917413]
[44]
London, N.; Raveh, B.; Cohen, E.; Fathi, G.; Schueler-Furman, O. Rosetta flexpepdock web server-high resolution modeling of peptide–protein interactions. Nucleic Acids Res., 2011, 39(Web Server issue)(2), W249-W253.
[http://dx.doi.org/10.1093/nar/gkr431] [PMID: 21622962]
[45]
Raveh, B.; London, N.; Zimmerman, L.; Schueler-Furman, O. Rosetta FlexPepDock ab-initio: Simultaneous folding, docking and refinement of peptides onto their receptors. PLoS One, 2011, 6(4), e18934.
[http://dx.doi.org/10.1371/journal.pone.0018934] [PMID: 21572516]
[46]
Donsky, E.; Wolfson, H.J. PepCrawler: A fast RRT-based algorithm for high-resolution refinement and binding affinity estimation of peptide inhibitors. Bioinformatics, 2011, 27(20), 2836-2842.
[http://dx.doi.org/10.1093/bioinformatics/btr498] [PMID: 21880702]
[47]
Zhang, Y.; Sanner, M.F. AutoDock CrankPep: Combining folding and docking to predict protein–peptide complexes. Bioinformatics, 2019, 35(24), 5121-5127.
[http://dx.doi.org/10.1093/bioinformatics/btz459] [PMID: 31161213]
[48]
Yan, C.; Xu, X.; Zou, X. Fully blind docking at the atomic level for protein-peptide complex structure prediction. Structure, 2016, 24(10), 1842-1853.
[http://dx.doi.org/10.1016/j.str.2016.07.021] [PMID: 27642160]
[49]
Xu, X.; Yan, C.; Zou, X. MDockPeP: An ab-initio protein–peptide docking server. J. Comput. Chem., 2018, 39(28), 2409-2413.
[http://dx.doi.org/10.1002/jcc.25555] [PMID: 30368849]
[50]
Zhou, P.; Li, B.; Yan, Y.; Jin, B.; Wang, L.; Huang, S.Y. Hierarchical flexible peptide docking by conformer generation and ensemble docking of peptides. J. Chem. Inf. Model., 2018, 58(6), 1292-1302.
[http://dx.doi.org/10.1021/acs.jcim.8b00142] [PMID: 29738247]
[51]
Zhou, P.; Jin, B.; Li, H.; Huang, S.Y. HPEPDOCK: A web server for blind peptide–protein docking based on a hierarchical algorithm. Nucleic Acids Res., 2018, 46(W1), W443-W450.
[http://dx.doi.org/10.1093/nar/gky357] [PMID: 29746661]
[52]
Yan, Y.; Zhang, D.; Huang, S.Y. Efficient conformational ensemble generation of protein-bound peptides. J. Cheminform., 2017, 9(1), 59.
[http://dx.doi.org/10.1186/s13321-017-0246-7] [PMID: 29168051]
[53]
Kurcinski, M.; Jamroz, M.; Blaszczyk, M.; Kolinski, A.; Kmiecik, S. CABS-dock web server for the flexible docking of peptides to proteins without prior knowledge of the binding site. Nucleic Acids Res., 2015, 43(W1), W419-W424.
[http://dx.doi.org/10.1093/nar/gkv456] [PMID: 25943545]
[54]
Blaszczyk, M.; Kurcinski, M.; Kouza, M.; Wieteska, L.; Debinski, A.; Kolinski, A.; Kmiecik, S. Modeling of protein–peptide interactions using the CABS-dock web server for binding site search and flexible docking. Methods, 2016, 93, 72-83.
[http://dx.doi.org/10.1016/j.ymeth.2015.07.004] [PMID: 26165956]
[55]
Das, A.A.; Sharma, O.P.; Kumar, M.S.; Krishna, R.; Mathur, P.P. PepBind: A comprehensive database and computational tool for analysis of protein-peptide interactions. Genomics Proteomics Bioinformatics, 2013, 11(4), 241-246.
[http://dx.doi.org/10.1016/j.gpb.2013.03.002] [PMID: 23896518]
[56]
Zhang, Y.; Skolnick, J. Scoring function for automated assessment of protein structure template quality. Proteins, 2004, 57(4), 702-710.
[http://dx.doi.org/10.1002/prot.20264] [PMID: 15476259]
[57]
Trabuco, L.G.; Lise, S.; Petsalaki, E.; Russell, R.B. PepSite: Prediction of peptide-binding sites from protein surfaces. Nucleic Acids Res., 2012, 40(W1), W423-W427.
[http://dx.doi.org/10.1093/nar/gks398] [PMID: 22600738]
[58]
Yan, C.; Zou, X. Predicting peptide binding sites on protein surfaces by clustering chemical interactions. J. Comput. Chem., 2015, 36(1), 49-61.
[http://dx.doi.org/10.1002/jcc.23771] [PMID: 25363279]
[59]
Gainza, P.; Sverrisson, F.; Monti, F.; Rodolà, E.; Boscaini, D.; Bronstein, M.M.; Correia, B.E. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nat. Methods, 2020, 17(2), 184-192.
[http://dx.doi.org/10.1038/s41592-019-0666-6] [PMID: 31819266]
[60]
Rooklin, D.; Wang, C.; Katigbak, J.; Arora, P.S.; Zhang, Y. AlphaSpace: Fragment-centric topographical mapping to target protein–protein interaction interfaces. J. Chem. Inf. Model., 2015, 55(8), 1585-1599.
[http://dx.doi.org/10.1021/acs.jcim.5b00103] [PMID: 26225450]
[61]
Gront, D.; Kulp, D.W.; Vernon, R.M.; Strauss, C.E.M.; Baker, D. Generalized fragment picking in Rosetta: Design, protocols and applications. PLoS One, 2011, 6(8), e23294.
[http://dx.doi.org/10.1371/journal.pone.0023294] [PMID: 21887241]
[62]
Andrusier, N.; Nussinov, R.; Wolfson, H.J. FireDock: Fast interaction refinement in molecular docking. Proteins, 2007, 69(1), 139-159.
[http://dx.doi.org/10.1002/prot.21495] [PMID: 17598144]
[63]
Pierce, B.G.; Wiehe, K.; Hwang, H.; Kim, B.H.; Vreven, T.; Weng, Z. ZDOCK server: Interactive docking prediction of protein–protein complexes and symmetric multimers. Bioinformatics, 2014, 30(12), 1771-1773.
[http://dx.doi.org/10.1093/bioinformatics/btu097] [PMID: 24532726]
[64]
Garzon, J.I.; Lopéz-Blanco, J.R.; Pons, C.; Kovacs, J.; Abagyan, R.; Fernandez-Recio, J.; Chacon, P. FRODOCK: A new approach for fast rotational protein–protein docking. Bioinformatics, 2009, 25(19), 2544-2551.
[http://dx.doi.org/10.1093/bioinformatics/btp447] [PMID: 19620099]
[65]
Weng, G.; Wang, E.; Wang, Z.; Liu, H.; Zhu, F.; Li, D.; Hou, T. HawkDock: A web server to predict and analyze the protein–protein complex based on computational docking and MM/GBSA. Nucleic Acids Res., 2019, 47(W1), W322-W330.
[http://dx.doi.org/10.1093/nar/gkz397] [PMID: 31106357]
[66]
Weng, G.; Gao, J.; Wang, Z.; Wang, E.; Hu, X.; Yao, X.; Cao, D.; Hou, T. Comprehensive evaluation of fourteen docking programs on protein–peptide complexes. J. Chem. Theory Comput., 2020, 16(6), 3959-3969.
[http://dx.doi.org/10.1021/acs.jctc.9b01208] [PMID: 32324992]
[67]
McGuffin, L.J.; Bryson, K.; Jones, D.T. The PSIPRED protein structure prediction server. Bioinformatics, 2000, 16(4), 404-405.
[http://dx.doi.org/10.1093/bioinformatics/16.4.404] [PMID: 10869041]
[68]
Kozakov, D.; Brenke, R.; Comeau, S.R.; Vajda, S. PIPER: An FFT-based protein docking program with pairwise potentials. Proteins, 2006, 65(2), 392-406.
[http://dx.doi.org/10.1002/prot.21117] [PMID: 16933295]
[69]
Schindler, C.E.M.; de Vries, S.J.; Zacharias, M. iATTRACT: Simultaneous global and local interface optimization for protein-protein docking refinement. Proteins, 2015, 83(2), 248-258.
[http://dx.doi.org/10.1002/prot.24728] [PMID: 25402278]
[70]
Case, D.A.; Cheatham, T.E., III; Darden, T.; Gohlke, H.; Luo, R.; Merz, K.M., Jr; Onufriev, A.; Simmerling, C.; Wang, B.; Woods, R.J. The Amber biomolecular simulation programs. J. Comput. Chem., 2005, 26(16), 1668-1688.
[http://dx.doi.org/10.1002/jcc.20290] [PMID: 16200636]
[71]
Webb, B.; Sali, A. Comparative protein structure modeling using modeller. Curr. Protoc. Bioinformatics., 2016, 54(1), 5.6. 1-5.6.
[72]
Huang, S.Y.; Zou, X. An iterative knowledge-based scoring function for protein-protein recognition. Proteins, 2008, 72(2), 557-579.
[http://dx.doi.org/10.1002/prot.21949] [PMID: 18247354]
[73]
Chaudhury, S.; Lyskov, S.; Gray, J.J. PyRosetta: A script-based interface for implementing molecular modeling algorithms using Rosetta. Bioinformatics, 2010, 26(5), 689-691.
[http://dx.doi.org/10.1093/bioinformatics/btq007] [PMID: 20061306]
[74]
Zhou, J.; Grigoryan, G. Rapid search for tertiary fragments reveals protein sequence-structure relationships. Protein Sci., 2015, 24(4), 508-524.
[http://dx.doi.org/10.1002/pro.2610] [PMID: 25420575]
[75]
Kuhlman, B.; Dantas, G.; Ireton, G.C.; Varani, G.; Stoddard, B.L.; Baker, D. Design of a novel globular protein fold with atomic-level accuracy. Science, 2003, 302(5649), 1364-1368.
[http://dx.doi.org/10.1126/science.1089427] [PMID: 14631033]
[76]
London, N.; Movshovitz-Attias, D.; Schueler-Furman, O. The structural basis of peptide-protein binding strategies. Structure, 2010, 18(2), 188-199.
[http://dx.doi.org/10.1016/j.str.2009.11.012] [PMID: 20159464]
[77]
Xu, X.; Zou, X. PepPro: A nonredundant structure data set for benchmarking peptide–protein computational docking. J. Comput. Chem., 2020, 41(4), 362-369.
[http://dx.doi.org/10.1002/jcc.26114] [PMID: 31793016]
[78]
Wen, Z.; He, J.; Tao, H.; Huang, S.Y. PepBDB: A comprehensive structural database of biological peptide–protein interactions. Bioinformatics, 2019, 35(1), 175-177.
[http://dx.doi.org/10.1093/bioinformatics/bty579] [PMID: 29982280]
[79]
Martins, P.M.; Santos, L.H.; Mariano, D.; Queiroz, F.C.; Bastos, L.L.; Gomes, I.S.; Fischer, P.H.C.; Rocha, R.E.O.; Silveira, S.A.; de Lima, L.H.F.; de Magalhães, M.T.Q.; Oliveira, M.G.A.; de Melo-Minardi, R.C. Propedia: A database for protein–peptide identification based on a hybrid clustering algorithm. BMC Bioinformatics, 2021, 22(1), 1-20.
[http://dx.doi.org/10.1186/s12859-020-03881-z] [PMID: 33388027]
[80]
Orengo, C.A.; Michie, A.D.; Jones, S.; Jones, D.T.; Swindells, M.B.; Thornton, J.M. CATH: A hierarchic classification of protein domain structures. Structure, 1997, 5(8), 1093-1109.
[http://dx.doi.org/10.1016/S0969-2126(97)00260-8] [PMID: 9309224]
[81]
Liu, Z.; Su, M.; Han, L.; Liu, J.; Yang, Q.; Li, Y.; Wang, R. Forging the basis for developing protein–ligand interaction scoring functions. Acc. Chem. Res., 2017, 50(2), 302-309.
[http://dx.doi.org/10.1021/acs.accounts.6b00491] [PMID: 28182403]
[82]
Burley, S.K.; Berman, H.M.; Kleywegt, G.J.; Markley, J.L.; Nakamura, H.; Velankar, S. Protein Data Bank (PDB): The single global macromolecular structure archive. Prot. Crystall. Meth. Prot., 2017, 627-641.
[83]
Lei, Y.; Li, S.; Liu, Z.; Wan, F.; Tian, T.; Li, S.; Zhao, D.; Zeng, J. A deep-learning framework for multi-level peptide–protein interaction prediction. Nat. Commun., 2021, 12(1), 5465.
[http://dx.doi.org/10.1038/s41467-021-25772-4] [PMID: 34526500]
[84]
Fukunaga, I.; Sawada, R.; Shibata, T.; Kaitoh, K.; Sakai, Y.; Yamanishi, Y. Prediction of the health effects of food peptides and elucidation of the mode-of-action using multi-task graph convolutional neural network. Mol. Inform., 2020, 39(1-2), 1900134.
[http://dx.doi.org/10.1002/minf.201900134] [PMID: 31778042]
[85]
Cock, P.J.A.; Antao, T.; Chang, J.T.; Chapman, B.A.; Cox, C.J.; Dalke, A.; Friedberg, I.; Hamelryck, T.; Kauff, F.; Wilczynski, B.; de Hoon, M.J.L. Biopython: Freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics, 2009, 25(11), 1422-1423.
[http://dx.doi.org/10.1093/bioinformatics/btp163] [PMID: 19304878]
[86]
Méndez, R.; Leplae, R.; De Maria, L.; Wodak, S.J. Assessment of blind predictions of protein-protein interactions: Current status of docking methods. Proteins, 2003, 52(1), 51-67.
[http://dx.doi.org/10.1002/prot.10393] [PMID: 12784368]
[87]
Basu, S.; Wallner, B.; Dock, Q. A quality measure for protein-protein docking models. PLoS One, 2016, 11(8), e0161879.
[http://dx.doi.org/10.1371/journal.pone.0161879] [PMID: 27560519]
[88]
Lensink, M.F.; Nadzirin, N.; Velankar, S.; Wodak, S.J. Modeling protein-protein, protein-peptide, and protein-oligosaccharide complexes: CAPRI 7th edition. Proteins: Struct. Funct. Bioinform., 2020, 88(8), 916-938.
[89]
Lensink, M.F.; Wodak, S.J. Docking, scoring, and affinity prediction in CAPRI. Proteins, 2013, 81(12), 2082-2095.
[http://dx.doi.org/10.1002/prot.24428] [PMID: 24115211]
[90]
Janin, J.; Henrick, K.; Moult, J.; Eyck, L.T.; Sternberg, M.J.E.; Vajda, S.; Vakser, I.; Wodak, S.J. CAPRI: A critical assessment of predicted interactions. Proteins, 2003, 52(1), 2-9.
[http://dx.doi.org/10.1002/prot.10381] [PMID: 12784359]
[91]
Janin, J. Assessing predictions of protein-protein interaction: The CAPRI experiment. Protein Sci., 2005, 14(2), 278-283.
[http://dx.doi.org/10.1110/ps.041081905] [PMID: 15659362]
[92]
Piana, S.; Klepeis, J.L.; Shaw, D.E. Assessing the accuracy of physical models used in protein-folding simulations: Quantitative evidence from long molecular dynamics simulations. Curr. Opin. Struct. Biol., 2014, 24, 98-105.
[http://dx.doi.org/10.1016/j.sbi.2013.12.006] [PMID: 24463371]
[93]
Li, W.; Wang, W.; Takada, S. Energy landscape views for interplays among folding, binding, and allostery of calmodulin domains. Proc. Natl. Acad. Sci., 2014, 111(29), 10550-10555.
[http://dx.doi.org/10.1073/pnas.1402768111] [PMID: 25002491]
[94]
Chen, H.F.; Luo, R. Binding induced folding in p53-MDM2 complex. J. Am. Chem. Soc., 2007, 129(10), 2930-2937.
[http://dx.doi.org/10.1021/ja0678774] [PMID: 17302414]
[95]
Yang, C.; Zhang, S.; Bai, Z.; Hou, S.; Wu, D.; Huang, J.; Zhou, P. A two-step binding mechanism for the self-binding peptide recognition of target domains. Mol. Biosyst., 2016, 12(4), 1201-1213.
[http://dx.doi.org/10.1039/C5MB00800J] [PMID: 26854254]
[96]
Ahmad, M.; Gu, W.; Helms, V. Mechanism of fast peptide recognition by SH3 domains. Angew. Chem. Int. Ed., 2008, 47(40), 7626-7630.
[http://dx.doi.org/10.1002/anie.200801856] [PMID: 18752238]
[97]
Zou, R.; Zhou, Y.; Wang, Y.; Kuang, G.; Ågren, H.; Wu, J.; Tu, Y. Free energy profile and kinetics of coupled folding and binding of the intrinsically disordered protein p53 with MDM2. J. Chem. Inf. Model., 2020, 60(3), 1551-1558.
[http://dx.doi.org/10.1021/acs.jcim.9b00920] [PMID: 32053358]
[98]
Kmiecik, S.; Gront, D.; Kolinski, M.; Wieteska, L.; Dawid, A.E.; Kolinski, A. Coarse-grained protein models and their applications. Chem. Rev., 2016, 116(14), 7898-7936.
[http://dx.doi.org/10.1021/acs.chemrev.6b00163] [PMID: 27333362]
[99]
Stone, J.E.; Phillips, J.C.; Freddolino, P.L.; Hardy, D.J.; Trabuco, L.G.; Schulten, K. Accelerating molecular modeling applications with graphics processors. J. Comput. Chem., 2007, 28(16), 2618-2640.
[http://dx.doi.org/10.1002/jcc.20829] [PMID: 17894371]
[100]
Anderson, J.A.; Lorenz, C.D.; Travesset, A. General purpose molecular dynamics simulations fully implemented on graphics processing units. J. Comput. Phys., 2008, 227(10), 5342-5359.
[http://dx.doi.org/10.1016/j.jcp.2008.01.047]
[101]
Jones, D.; Allen, J.E.; Yang, Y.; Drew Bennett, W.F.; Gokhale, M.; Moshiri, N.; Rosing, T.S. Accelerators for classical molecular dynamics simulations of biomolecules. J. Chem. Theory Comput., 2022, 18(7), 4047-4069.
[http://dx.doi.org/10.1021/acs.jctc.1c01214] [PMID: 35710099]
[102]
Kutzner, C.; Páll, S.; Fechner, M.; Esztermann, A.; de Groot, B.L.; Grubmüller, H. Best bang for your buck: GPU nodes for gromacs biomolecular simulations. Wiley Online Library, 2015.
[103]
Kohnke, B.; Kutzner, C.; Grubmüller, H. A GPU-accelerated fast multipole method for GROMACS: Performance and accuracy. J. Chem. Theory Comput., 2020, 16(11), 6938-6949.
[http://dx.doi.org/10.1021/acs.jctc.0c00744] [PMID: 33084336]
[104]
Kutzner, C.; Kniep, C.; Cherian, A.; Nordstrom, L.; Grubmüller, H.; de Groot, B.L.; Gapsys, V. GROMACS in the cloud: A global supercomputer to speed up alchemical drug design. J. Chem. Inf. Model., 2022, 62(7), 1691-1711.
[http://dx.doi.org/10.1021/acs.jcim.2c00044] [PMID: 35353508]
[105]
Salomon-Ferrer, R.; Case, D.A.; Walker, R.C. An overview of the Amber biomolecular simulation package. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2013, 3(2), 198-210.
[http://dx.doi.org/10.1002/wcms.1121]
[106]
Salomon-Ferrer, R.; Götz, A.W.; Poole, D.; Le Grand, S.; Walker, R.C. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. J. Chem. Theory Comput., 2013, 9(9), 3878-3888.
[http://dx.doi.org/10.1021/ct400314y] [PMID: 26592383]
[107]
Götz, A.W.; Williamson, M.J.; Xu, D.; Poole, D.; Le Grand, S.; Walker, R.C. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. Generalized born. J. Chem. Theory Comput., 2012, 8(5), 1542-1555.
[http://dx.doi.org/10.1021/ct200909j] [PMID: 22582031]
[108]
Phillips, J.C.; Hardy, D.J.; Maia, J.D.C.; Stone, J.E.; Ribeiro, J.V.; Bernardi, R.C.; Buch, R.; Fiorin, G.; Hénin, J.; Jiang, W.; McGreevy, R.; Melo, M.C.R.; Radak, B.K.; Skeel, R.D.; Singharoy, A.; Wang, Y.; Roux, B.; Aksimentiev, A.; Luthey-Schulten, Z.; Kalé, L.V.; Schulten, K.; Chipot, C.; Tajkhorshid, E. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J. Chem. Phys., 2020, 153(4), 044130.
[http://dx.doi.org/10.1063/5.0014475] [PMID: 32752662]
[109]
Eastman, P.; Pande, V.; Open, M.M. A hardware-independent framework for molecular simulations. Comput. Sci. Eng., 2010, 12(4), 34-39.
[http://dx.doi.org/10.1109/MCSE.2010.27] [PMID: 26146490]
[110]
Eastman, P.; Swails, J.; Chodera, J.D.; McGibbon, R.T.; Zhao, Y.; Beauchamp, K.A.; Wang, L.P.; Simmonett, A.C.; Harrigan, M.P.; Stern, C.D.; Wiewiora, R.P.; Brooks, B.R.; Pande, V.S. OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLOS Comput. Biol., 2017, 13(7), e1005659.
[http://dx.doi.org/10.1371/journal.pcbi.1005659] [PMID: 28746339]
[111]
Bernardi, R.C.; Melo, M.C.R.; Schulten, K. Enhanced sampling techniques in molecular dynamics simulations of biological systems. Biochim. Biophys. Acta, Gen. Subj., 2015, 1850(5), 872-877.
[http://dx.doi.org/10.1016/j.bbagen.2014.10.019] [PMID: 25450171]
[112]
Laio, A.; Parrinello, M. Escaping free-energy minima. Proc. Natl. Acad. Sci., 2002, 99(20), 12562-12566.
[http://dx.doi.org/10.1073/pnas.202427399] [PMID: 12271136]
[113]
Barducci, A.; Bonomi, M.; Parrinello, M. Metadynamics. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2011, 1(5), 826-843.
[http://dx.doi.org/10.1002/wcms.31]
[114]
Clayton, J.; Baweja, L.; Wereszczynski, J. In Computational Peptide Science: Methods and protocols; Springer, 2022, pp. 151-167.
[http://dx.doi.org/10.1007/978-1-0716-1855-4_8]
[115]
Buchete, N.V.; Hummer, G. Peptide folding kinetics from replica exchange molecular dynamics. Phys. Rev. E Stat. Nonlin. Soft Matter Phys., 2008, 77(3), 030902.
[http://dx.doi.org/10.1103/PhysRevE.77.030902] [PMID: 18517321]
[116]
Sugita, Y.; Okamoto, Y. Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett., 1999, 314(1-2), 141-151.
[http://dx.doi.org/10.1016/S0009-2614(99)01123-9]
[117]
Torrie, G.M.; Valleau, J.P. Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling. J. Comput. Phys., 1977, 23(2), 187-199.
[http://dx.doi.org/10.1016/0021-9991(77)90121-8]
[118]
Maragliano, L.; Vanden-Eijnden, E. A temperature accelerated method for sampling free energy and determining reaction pathways in rare events simulations. Chem. Phys. Lett., 2006, 426(1-3), 168-175.
[http://dx.doi.org/10.1016/j.cplett.2006.05.062]
[119]
Miao, Y.; Feher, V.A.; McCammon, J.A. Gaussian accelerated molecular dynamics: Unconstrained enhanced sampling and free energy calculation. J. Chem. Theory Comput., 2015, 11(8), 3584-3595.
[http://dx.doi.org/10.1021/acs.jctc.5b00436] [PMID: 26300708]
[120]
Miao, Y. McCammon, J.A. Annual reports in computational chemistry; Elsevier, 2017, 13, pp. 231-278.
[121]
Wang, J.; Miao, Y. Peptide gaussian accelerated molecular dynamics (Pep-GaMD): Enhanced sampling and free energy and kinetics calculations of peptide binding. J. Chem. Phys., 2020, 153(15), 154109.
[http://dx.doi.org/10.1063/5.0021399] [PMID: 33092378]
[122]
Kang, W.; Jiang, F.; Wu, Y.D. Universal implementation of a residue-specific force field based on CMAP potentials and free energy decomposition. J. Chem. Theory Comput., 2018, 14(8), 4474-4486.
[http://dx.doi.org/10.1021/acs.jctc.8b00285] [PMID: 29906395]
[123]
Tang, C.; Iwahara, J.; Clore, G.M. Visualization of transient encounter complexes in protein–protein association. Nature, 2006, 444(7117), 383-386.
[http://dx.doi.org/10.1038/nature05201] [PMID: 17051159]
[124]
Rajamani, D.; Thiel, S.; Vajda, S.; Camacho, C.J. Anchor residues in protein–protein interactions. Proc. Natl. Acad. Sci., 2004, 101(31), 11287-11292.
[http://dx.doi.org/10.1073/pnas.0401942101] [PMID: 15269345]
[125]
Kim, Y.C.; Tang, C.; Clore, G.M.; Hummer, G. Replica exchange simulations of transient encounter complexes in protein–protein association. Proc. Natl. Acad. Sci., 2008, 105(35), 12855-12860.
[http://dx.doi.org/10.1073/pnas.0802460105] [PMID: 18728193]
[126]
Pan, A.C.; Jacobson, D.; Yatsenko, K.; Sritharan, D.; Weinreich, T.M.; Shaw, D.E. Atomic-level characterization of protein–protein association. Proc. Natl. Acad. Sci., 2019, 116(10), 4244-4249.
[http://dx.doi.org/10.1073/pnas.1815431116] [PMID: 30760596]
[127]
Beuming, T.; Farid, R.; Sherman, W. High-energy water sites determine peptide binding affinity and specificity of PDZ domains. Protein Sci., 2009, 18(8), 1609-1619.
[http://dx.doi.org/10.1002/pro.177] [PMID: 19569188]
[128]
Ylilauri, M.; Pentikäinen, O.T. MMGBSA as a tool to understand the binding affinities of filamin-peptide interactions. J. Chem. Inf. Model., 2013, 53(10), 2626-2633.
[http://dx.doi.org/10.1021/ci4002475] [PMID: 23988151]
[129]
Kötter, A.; Mootz, H.D.; Heuer, A. Standard binding free energy of a SIM–SUMO complex. J. Chem. Theory Comput., 2019, 15(11), 6403-6410.
[http://dx.doi.org/10.1021/acs.jctc.9b00428] [PMID: 31525924]
[130]
ElSawy, K.M.; Lane, D.P.; Verma, C.S.; Caves, L.S.D. Recognition dynamics of p53 and MDM2: Implications for peptide design. J. Phys. Chem. B, 2016, 120(2), 320-328.
[http://dx.doi.org/10.1021/acs.jpcb.5b11162] [PMID: 26701330]
[131]
Ayaz, P.; Lyczek, A.; Paung, Y.; Mingione, V.R.; Iacob, R.E.; de Waal, P.W.; Engen, J.R.; Seeliger, M.A.; Shan, Y.; Shaw, D.E. Structural mechanism of a drug-binding process involving a large conformational change of the protein target. Nat. Commun., 2023, 14(1), 1885.
[http://dx.doi.org/10.1038/s41467-023-36956-5] [PMID: 37019905]
[132]
Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; Bridgland, A.; Meyer, C.; Kohl, S.A.A.; Ballard, A.J.; Cowie, A.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Adler, J.; Back, T.; Petersen, S.; Reiman, D.; Clancy, E.; Zielinski, M.; Steinegger, M.; Pacholska, M.; Berghammer, T.; Bodenstein, S.; Silver, D.; Vinyals, O.; Senior, A.W.; Kavukcuoglu, K.; Kohli, P.; Hassabis, D. Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596(7873), 583-589.
[http://dx.doi.org/10.1038/s41586-021-03819-2] [PMID: 34265844]
[133]
Baek, M.; DiMaio, F.; Anishchenko, I.; Dauparas, J.; Ovchinnikov, S.; Lee, G.R.; Wang, J.; Cong, Q.; Kinch, L.N.; Schaeffer, R.D.; Millán, C.; Park, H.; Adams, C.; Glassman, C.R.; DeGiovanni, A.; Pereira, J.H.; Rodrigues, A.V.; van Dijk, A.A.; Ebrecht, A.C.; Opperman, D.J.; Sagmeister, T.; Buhlheller, C.; Pavkov-Keller, T.; Rathinaswamy, M.K.; Dalwadi, U.; Yip, C.K.; Burke, J.E.; Garcia, K.C.; Grishin, N.V.; Adams, P.D.; Read, R.J.; Baker, D. Accurate prediction of protein structures and interactions using a three-track neural network. Science, 2021, 373(6557), 871-876.
[http://dx.doi.org/10.1126/science.abj8754] [PMID: 34282049]
[134]
Lin, Z.; Akin, H.; Rao, R.; Hie, B.; Zhu, Z.; Lu, W.; dos Santos Costa, A.; Fazel-Zarandi, M.; Sercu, T.; Candido, S. Language models of protein sequences at the scale of evolution enable accurate structure prediction. BioRxiv, 2022.
[135]
Baek, M.; Baker, D. Deep learning and protein structure modeling. Nat. Methods, 2022, 19(1), 13-14.
[http://dx.doi.org/10.1038/s41592-021-01360-8] [PMID: 35017724]
[136]
Zhou, X.; Zheng, W.; Li, Y.; Pearce, R.; Zhang, C.; Bell, E.W.; Zhang, G.; Zhang, Y. I-TASSER-MTD: A deep- learning-based platform for multi-domain protein structure and function prediction. Nat. Protoc., 2022, 17(10), 2326-2353.
[http://dx.doi.org/10.1038/s41596-022-00728-0] [PMID: 35931779]
[137]
Wang, J.; Lisanza, S.; Juergens, D.; Tischer, D.; Watson, J.L.; Castro, K.M.; Ragotte, R.; Saragovi, A.; Milles, L.F.; Baek, M.; Anishchenko, I.; Yang, W.; Hicks, D.R.; Expòsit, M.; Schlichthaerle, T.; Chun, J.H.; Dauparas, J.; Bennett, N.; Wicky, B.I.M.; Muenks, A.; DiMaio, F.; Correia, B.; Ovchinnikov, S.; Baker, D. Scaffolding protein functional sites using deep learning. Science, 2022, 377(6604), 387-394.
[http://dx.doi.org/10.1126/science.abn2100] [PMID: 35862514]
[138]
Zeng, X.; Zhu, S.; Lu, W.; Liu, Z.; Huang, J.; Zhou, Y.; Fang, J.; Huang, Y.; Guo, H.; Li, L.; Trapp, B.D.; Nussinov, R.; Eng, C.; Loscalzo, J.; Cheng, F. Target identification among known drugs by deep learning from heterogeneous networks. Chem. Sci., 2020, 11(7), 1775-1797.
[http://dx.doi.org/10.1039/C9SC04336E] [PMID: 34123272]
[139]
Sapoval, N.; Aghazadeh, A.; Nute, M.G.; Antunes, D.A.; Balaji, A.; Baraniuk, R.; Barberan, C.J.; Dannenfelser, R.; Dun, C.; Edrisi, M.; Elworth, R.A.L.; Kille, B.; Kyrillidis, A.; Nakhleh, L.; Wolfe, C.R.; Yan, Z.; Yao, V.; Treangen, T.J. Current progress and open challenges for applying deep learning across the biosciences. Nat. Commun., 2022, 13(1), 1728.
[http://dx.doi.org/10.1038/s41467-022-29268-7] [PMID: 35365602]
[140]
Unsal, S.; Atas, H.; Albayrak, M.; Turhan, K.; Acar, A.C.; Doğan, T. Learning functional properties of proteins with language models. Nat. Mach. Intell., 2022, 4(3), 227-245.
[http://dx.doi.org/10.1038/s42256-022-00457-9]
[141]
Brandes, N.; Ofer, D.; Peleg, Y.; Rappoport, N.; Linial, M. ProteinBERT: A universal deep-learning model of protein sequence and function. Bioinformatics, 2022, 38(8), 2102-2110.
[http://dx.doi.org/10.1093/bioinformatics/btac020] [PMID: 35020807]
[142]
Kryshtafovych, A.; Schwede, T.; Topf, M.; Fidelis, K.; Moult, J. Critical assessment of methods of protein structure prediction (CASP)-Round XIV. Proteins, 2021, 89(12), 1607-1617.
[http://dx.doi.org/10.1002/prot.26237] [PMID: 34533838]
[143]
Bennett, N.R.; Coventry, B.; Goreshnik, I.; Huang, B.; Allen, A.; Vafeados, D.; Peng, Y.P.; Dauparas, J.; Baek, M.; Stewart, L.; DiMaio, F.; De Munck, S.; Savvides, S.N.; Baker, D. Improving de novo protein binder design with deep learning. Nat. Commun., 2023, 14(1), 2625.
[http://dx.doi.org/10.1038/s41467-023-38328-5] [PMID: 37149653]
[144]
Torres, S.V.; Leung, P.J.; Lutz, I.D.; Venkatesh, P.; Watson, J.L.; Hink, F.; Huynh, H-H.; Yeh, A.H-W.; Juergens, D.; Bennett, N.R. De novo design of high-affinity protein binders to bioactive helical peptides. Biorxiv, 2022, 519862.
[145]
Johansson-Åkhe, I.; Wallner, B. Improving peptide-protein docking with AlphaFold-Multimer using forced sampling. Front. Bioinform., 2022, 2, 959160.
[http://dx.doi.org/10.3389/fbinf.2022.959160] [PMID: 36304330]
[146]
Gainza, P.; Wehrle, S.; Van Hall-Beauvais, A.; Marchand, A.; Scheck, A.; Harteveld, Z.; Buckley, S.; Ni, D.; Tan, S.; Sverrisson, F.; Goverde, C.; Turelli, P.; Raclot, C.; Teslenko, A.; Pacesa, M.; Rosset, S.; Georgeon, S.; Marsden, J.; Petruzzella, A.; Liu, K.; Xu, Z.; Chai, Y.; Han, P.; Gao, G.F.; Oricchio, E.; Fierz, B.; Trono, D.; Stahlberg, H.; Bronstein, M.; Correia, B.E. De novo design of protein interactions with learned surface fingerprints. Nature, 2023, 617(7959), 176-184.
[http://dx.doi.org/10.1038/s41586-023-05993-x] [PMID: 37100904]
[147]
Jiang, Y.; Wang, R.; Feng, J.; Jin, J.; Liang, S.; Li, Z.; Yu, Y.; Ma, A.; Su, R.; Zou, Q.; Ma, Q.; Wei, L. Explainable deep hypergraph learning modeling the peptide secondary structure prediction. Adv. Sci., 2023, 10(11), 2206151.
[http://dx.doi.org/10.1002/advs.202206151] [PMID: 36794291]
[148]
Cao, X.; He, W.; Chen, Z.; Li, Y.; Wang, K.; Zhang, H.; Wei, L.; Cui, L.; Su, R.; Wei, L. PSSP-MVIRT: Peptide secondary structure prediction based on a multi-view deep learning architecture. Brief. Bioinform., 2021, 22(6), bbab203.
[http://dx.doi.org/10.1093/bib/bbab203] [PMID: 34117740]
[149]
Yu, H.; Zhou, P.; Deng, M.; Shang, Z. Indirect readout in protein-peptide recognition: A different story from classical biomolecular recognition. J. Chem. Inf. Model., 2014, 54(7), 2022-2032.
[http://dx.doi.org/10.1021/ci5000246] [PMID: 24999015]

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