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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

Computational Protein Design - Where it goes?

Author(s): Binbin Xu, Yingjun Chen and Weiwei Xue*

Volume 31, Issue 20, 2024

Published on: 01 August, 2023

Page: [2841 - 2854] Pages: 14

DOI: 10.2174/0929867330666230602143700

Price: $65

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Abstract

Proteins have been playing a critical role in the regulation of diverse biological processes related to human life. With the increasing demand, functional proteins are sparse in this immense sequence space. Therefore, protein design has become an important task in various fields, including medicine, food, energy, materials, etc. Directed evolution has recently led to significant achievements. Molecular modification of proteins through directed evolution technology has significantly advanced the fields of enzyme engineering, metabolic engineering, medicine, and beyond. However, it is impossible to identify desirable sequences from a large number of synthetic sequences alone. As a result, computational methods, including data-driven machine learning and physics-based molecular modeling, have been introduced to protein engineering to produce more functional proteins. This review focuses on recent advances in computational protein design, highlighting the applicability of different approaches as well as their limitations.

Keywords: Protein design, machine learning, neural networks, deep learning, molecular modeling, computational protein design.

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[1]
Devries, M.C.; Phillips, S.M. Supplemental protein in support of muscle mass and health: Advantage whey. J. Food Sci., 2015, 80(S1)(Suppl. 1), A8-A15.
[http://dx.doi.org/10.1111/1750-3841.12802] [PMID: 25757896]
[2]
Das, S.; Dawson, N.L.; Orengo, C.A. Diversity in protein domain superfamilies. Curr. Opin. Genet. Dev., 2015, 35, 40-49.
[http://dx.doi.org/10.1016/j.gde.2015.09.005] [PMID: 26451979]
[3]
Boeckmann, B.; Blatter, M.C.; Famiglietti, L.; Hinz, U.; Lane, L.; Roechert, B.; Bairoch, A. Protein variety and functional diversity: Swiss-Prot annotation in its biological context. C. R. Biol., 2005, 328(10-11), 882-899.
[http://dx.doi.org/10.1016/j.crvi.2005.06.001] [PMID: 16286078]
[4]
Cheng, L.; Fan, K.; Huang, Y.; Wang, D.; Leung, K.S. Full characterization of localization diversity in the human protein interactome. J. Proteome Res., 2017, 16(8), 3019-3029.
[http://dx.doi.org/10.1021/acs.jproteome.7b00306] [PMID: 28707887]
[5]
Anfinsen, C.B. Principles that govern the folding of protein chains. Science, 1973, 181(4096), 223-230.
[http://dx.doi.org/10.1126/science.181.4096.223] [PMID: 4124164]
[6]
Kuhlman, B.; Bradley, P. Advances in protein structure prediction and design. Nat. Rev. Mol. Cell Biol., 2019, 20(11), 681-697.
[http://dx.doi.org/10.1038/s41580-019-0163-x] [PMID: 31417196]
[7]
Huang, P.S.; Boyken, S.E.; Baker, D. The coming of age of de novo protein design. Nature, 2016, 537(7620), 320-327.
[http://dx.doi.org/10.1038/nature19946] [PMID: 27629638]
[8]
Jones, D.T.; Singh, T.; Kosciolek, T.; Tetchner, S. MetaPSICOV: Combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins. Bioinformatics, 2015, 31(7), 999-1006.
[http://dx.doi.org/10.1093/bioinformatics/btu791] [PMID: 25431331]
[9]
Mravic, M.; Thomaston, J.L.; Tucker, M.; Solomon, P.E.; Liu, L.; DeGrado, W.F. Packing of apolar side chains enables accurate design of highly stable membrane proteins. Science, 2019, 363(6434), 1418-1423.
[http://dx.doi.org/10.1126/science.aav7541] [PMID: 30923216]
[10]
Silva, D.A.; Yu, S.; Ulge, U.Y.; Spangler, J.B.; Jude, K.M.; Labão-Almeida, C.; Ali, L.R.; Quijano-Rubio, A.; Ruterbusch, M.; Leung, I.; Biary, T.; Crowley, S.J.; Marcos, E.; Walkey, C.D.; Weitzner, B.D.; Pardo-Avila, F.; Castellanos, J.; Carter, L.; Stewart, L.; Riddell, S.R.; Pepper, M.; Bernardes, G.J.L.; Dougan, M.; Garcia, K.C.; Baker, D. De novo design of potent and selective mimics of IL-2 and IL-15. Nature, 2019, 565(7738), 186-191.
[http://dx.doi.org/10.1038/s41586-018-0830-7] [PMID: 30626941]
[11]
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res., 2000, 28(1), 235-242.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235]
[12]
Evans, R.; O’Neill, M.; Pritzel, A.; Antropova, N.; Senior, A.; Green, T.; Žídek, A.; Bates, R.; Blackwell, S.; Yim, J.; Ronneberger, O.; Bodenstein, S.; Zielinski, M.; Bridgland, A.; Potapenko, A.; Cowie, A.; Tunyasuvunakool, K.; Jain, R.; Clancy, E.; Kohli, P.; Jumper, J.; Hassabis, D. Protein complex prediction with AlphaFold-Multimer. bioRxiv, 2021.
[http://dx.doi.org/10.1101/2021.10.04.463034]
[13]
Yang, K.K.; Wu, Z.; Arnold, F.H. Machine-learning-guided directed evolution for protein engineering. Nat. Methods, 2019, 16(8), 687-694.
[http://dx.doi.org/10.1038/s41592-019-0496-6] [PMID: 31308553]
[14]
Khoury, G.A.; Smadbeck, J.; Kieslich, C.A.; Floudas, C.A. Protein folding and de novo protein design for biotechnological applications. Trends Biotechnol., 2014, 32(2), 99-109.
[http://dx.doi.org/10.1016/j.tibtech.2013.10.008] [PMID: 24268901]
[15]
Woolfson, D.N.; Bartlett, G.J.; Burton, A.J.; Heal, J.W.; Niitsu, A.; Thomson, A.R.; Wood, C.W. De novo protein design: How do we expand into the universe of possible protein structures? Curr. Opin. Struct. Biol., 2015, 33, 16-26.
[http://dx.doi.org/10.1016/j.sbi.2015.05.009] [PMID: 26093060]
[16]
Gouw, M.; Michael, S.; Sámano-Sánchez, H.; Kumar, M.; Zeke, A.; Lang, B.; Bely, B.; Chemes, L.B.; Davey, N.E.; Deng, Z.; Diella, F.; Gürth, C.M.; Huber, A.K.; Kleinsorg, S.; Schlegel, L.S.; Palopoli, N.; Roey, K.V.; Altenberg, B.; Reményi, A.; Dinkel, H.; Gibson, T.J. The eukaryotic linear motif resource – 2018 update. Nucleic Acids Res., 2018, 46(D1), D428-D434.
[http://dx.doi.org/10.1093/nar/gkx1077] [PMID: 29136216]
[17]
Yang, J.; Zhang, Z.; Yang, F.; Zhang, H.; Wu, H.; Zhu, F.; Xue, W. Computational design and modeling of nanobodies toward SARS-CoV-2 receptor binding domain. Chem. Biol. Drug Des., 2021, 98(1), 1-18.
[http://dx.doi.org/10.1111/cbdd.13847] [PMID: 33894099]
[18]
Zhang, Y.F.; Ho, M. Humanization of rabbit monoclonal antibodies via grafting combined Kabat/IMGT/Paratome complementarity-determining regions: Rationale and examples. MAbs, 2017, 9(3), 419-429.
[http://dx.doi.org/10.1080/19420862.2017.1289302] [PMID: 28165915]
[19]
Carter, P.; Presta, L.; Gorman, C.M.; Ridgway, J.B.; Henner, D.; Wong, W.L.; Rowland, A.M.; Kotts, C.; Carver, M.E.; Shepard, H.M. Humanization of an anti-p185HER2 antibody for human cancer therapy. Proc. Natl. Acad. Sci. USA, 1992, 89(10), 4285-4289.
[http://dx.doi.org/10.1073/pnas.89.10.4285] [PMID: 1350088]
[20]
Ewert, S.; Honegger, A.; Plückthun, A. Stability improvement of antibodies for extracellular and intracellular applications: CDR grafting to stable frameworks and structure-based framework engineering. Methods, 2004, 34(2), 184-199.
[http://dx.doi.org/10.1016/j.ymeth.2004.04.007] [PMID: 15312672]
[21]
Liu, Y.; Kuhlman, B. RosettaDesign server for protein design. Nucleic Acids Res., 2006, 34(Issue suppl_2), W235-W235.
[http://dx.doi.org/10.1093/nar/gkl163]
[22]
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]
[23]
Anand-Achim, N.; Eguchi, R.R.; Derry, A.; Altman, R.B.; Huang, P-S. Protein sequence design with a learned potential. biorxiv, 2020.
[http://dx.doi.org/10.1101/2020.01.06.895466]
[24]
Voigt, C.A.; Martinez, C.; Wang, Z.G.; Mayo, S.L.; Arnold, F.H. Protein building blocks preserved by recombination. Nat. Struct. Biol., 2002, 9(7), 553-558.
[http://dx.doi.org/10.1038/nsb805] [PMID: 12042875]
[25]
McMahon, C.; Baier, A.S.; Pascolutti, R.; Wegrecki, M.; Zheng, S.; Ong, J.X.; Erlandson, S.C.; Hilger, D.; Rasmussen, S.G.F.; Ring, A.M.; Manglik, A.; Kruse, A.C. Yeast surface display platform for rapid discovery of conformationally selective nanobodies. Nat. Struct. Mol. Biol., 2018, 25(3), 289-296.
[http://dx.doi.org/10.1038/s41594-018-0028-6] [PMID: 29434346]
[26]
Lee, C.V.; Liang, W.C.; Dennis, M.S.; Eigenbrot, C.; Sidhu, S.S.; Fuh, G. High-affinity human antibodies from phage-displayed synthetic Fab libraries with a single framework scaffold. J. Mol. Biol., 2004, 340(5), 1073-1093.
[http://dx.doi.org/10.1016/j.jmb.2004.05.051] [PMID: 15236968]
[27]
Fellouse, F.A.; Esaki, K.; Birtalan, S.; Raptis, D.; Cancasci, V.J.; Koide, A.; Jhurani, P.; Vasser, M.; Wiesmann, C.; Kossiakoff, A.A.; Koide, S.; Sidhu, S.S. High-throughput generation of synthetic antibodies from highly functional minimalist phage-displayed libraries. J. Mol. Biol., 2007, 373(4), 924-940.
[http://dx.doi.org/10.1016/j.jmb.2007.08.005] [PMID: 17825836]
[28]
Fellouse, F.A.; Barthelemy, P.A.; Kelley, R.F.; Sidhu, S.S. Tyrosine plays a dominant functional role in the paratope of a synthetic antibody derived from a four amino acid code. J. Mol. Biol., 2006, 357(1), 100-114.
[http://dx.doi.org/10.1016/j.jmb.2005.11.092] [PMID: 16413576]
[29]
Jäckel, C.; Kast, P.; Hilvert, D. Protein design by directed evolution. Annu. Rev. Biophys., 2008, 37(1), 153-173.
[http://dx.doi.org/10.1146/annurev.biophys.37.032807.125832] [PMID: 18573077]
[30]
Eijsink, V.G.H.; Gåseidnes, S.; Borchert, T.V.; van den Burg, B. Directed evolution of enzyme stability. Biomol. Eng., 2005, 22(1-3), 21-30.
[http://dx.doi.org/10.1016/j.bioeng.2004.12.003] [PMID: 15857780]
[31]
Johannes, T.W.; Zhao, H. Directed evolution of enzymes and biosynthetic pathways. Curr. Opin. Microbiol., 2006, 9(3), 261-267.
[http://dx.doi.org/10.1016/j.mib.2006.03.003] [PMID: 16621678]
[32]
Xu, Y.; Verma, D.; Sheridan, R.P.; Liaw, A.; Ma, J.; Marshall, N.M.; McIntosh, J.; Sherer, E.C.; Svetnik, V.; Johnston, J.M. Deep dive into machine learning models for protein engineering. J. Chem. Inf. Model., 2020, 60(6), 2773-2790.
[http://dx.doi.org/10.1021/acs.jcim.0c00073] [PMID: 32250622]
[33]
Chen, T.R.; Juan, S.H.; Huang, Y.W.; Lin, Y.C.; Lo, W.C. A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction. PLoS One, 2021, 16(7), e0255076.
[http://dx.doi.org/10.1371/journal.pone.0255076] [PMID: 34320027]
[34]
Xiaotong Lin; Xue-Wen Chen; Chen, X.W. On position-specific scoring matrix for protein function prediction. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2011, 8(2), 308-315.
[http://dx.doi.org/10.1109/TCBB.2010.93] [PMID: 20855926]
[35]
Seeger, M. Gaussian processes for machine learning. Int. J. Neural Syst., 2004, 14(2), 69-106.
[http://dx.doi.org/10.1142/S0129065704001899] [PMID: 15112367]
[36]
Romero, P.A.; Krause, A.; Arnold, F.H. Navigating the protein fitness landscape with Gaussian processes. Proc. Natl. Acad. Sci. USA, 2013, 110(3), E193-E201.
[http://dx.doi.org/10.1073/pnas.1215251110] [PMID: 23277561]
[37]
Bedbrook, C.N.; Yang, K.K.; Robinson, J.E.; Mackey, E.D.; Gradinaru, V.; Arnold, F.H. Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics. Nat. Methods, 2019, 16(11), 1176-1184.
[http://dx.doi.org/10.1038/s41592-019-0583-8] [PMID: 31611694]
[38]
Gao, W.; Mahajan, S.P.; Sulam, J.; Gray, J.J. Deep learning in protein structural modeling and design. Patterns, 2020, 1(9), 100142.
[http://dx.doi.org/10.1016/j.patter.2020.100142] [PMID: 33336200]
[39]
Senior, A.W.; Evans, R.; Jumper, J.; Kirkpatrick, J.; Sifre, L.; Green, T.; Qin, C.; Žídek, A.; Nelson, A.W.R.; Bridgland, A.; Penedones, H.; Petersen, S.; Simonyan, K.; Crossan, S.; Kohli, P.; Jones, D.T.; Silver, D.; Kavukcuoglu, K.; Hassabis, D. Improved protein structure prediction using potentials from deep learning. Nature, 2020, 577(7792), 706-710.
[http://dx.doi.org/10.1038/s41586-019-1923-7] [PMID: 31942072]
[40]
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]
[41]
Frappier, V.; Keating, A.E. Data-driven computational protein design. Curr. Opin. Struct. Biol., 2021, 69, 63-69.
[http://dx.doi.org/10.1016/j.sbi.2021.03.009] [PMID: 33910104]
[42]
N., Anand; Huang, P. Generative modeling for protein structures. NIPS'18: Proceedings of the 32nd International Conference on Neural Information Processing Systems., 2018, pp. 7505-7516.
[43]
Senior, A.W.; Evans, R.; Jumper, J.; Kirkpatrick, J.; Sifre, L.; Green, T.; Qin, C.; Žídek, A.; Nelson, A.W.R.; Bridgland, A.; Penedones, H.; Petersen, S.; Simonyan, K.; Crossan, S.; Kohli, P.; Jones, D.T.; Silver, D.; Kavukcuoglu, K.; Hassabis, D. Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13). Proteins, 2019, 87(12), 1141-1148.
[http://dx.doi.org/10.1002/prot.25834] [PMID: 31602685]
[44]
Hornik, K.; Stinchcombe, M.; White, H. Multilayer feedforward networks are universal approximators. Neural Netw., 1989, 2(5), 359-366.
[http://dx.doi.org/10.1016/0893-6080(89)90020-8]
[45]
Anishchenko, I.; Pellock, S.J.; Chidyausiku, T.M.; Ramelot, T.A.; Ovchinnikov, S.; Hao, J.; Bafna, K.; Norn, C.; Kang, A.; Bera, A.K.; DiMaio, F.; Carter, L.; Chow, C.M.; Montelione, G.T.; Baker, D. De novo protein design by deep network hallucination. Nature, 2021, 600(7889), 547-552.
[http://dx.doi.org/10.1038/s41586-021-04184-w] [PMID: 34853475]
[46]
Anand, N.; Eguchi, R.; Mathews, I.I.; Perez, C.P.; Derry, A.; Altman, R.B.; Huang, P.S. Protein sequence design with a learned potential. Nat. Commun., 2022, 13(1), 746.
[http://dx.doi.org/10.1038/s41467-022-28313-9] [PMID: 35136054]
[47]
Strokach, A.; Becerra, D.; Corbi-Verge, C.; Perez-Riba, A.; Kim, P.M. Fast and flexible protein design using deep graph neural networks. Cell Syst., 2020, 11(4), 402-411.e4.
[http://dx.doi.org/10.1016/j.cels.2020.08.016] [PMID: 32971019]
[48]
Xie, J.J.; Xu, B.; Zhang, C. Horizontal and vertical ensemble with deep representation for classfication. eprint arXiv, 2013.
[49]
Dvornik, N.; Mairal, J.; Schmid, C. Diversity with Cooperation: Ensemble Methods for Few-Shot Classification. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 3722-3730.
[50]
Cao, Y.; Geddes, T.A.; Yang, J.Y.H.; Yang, P. Ensemble deep learning in bioinformatics. Nat. Mach. Intell., 2020, 2(9), 500-508.
[http://dx.doi.org/10.1038/s42256-020-0217-y]
[51]
Ju, C.; Bibaut, A.; van der Laan, M. The relative performance of ensemble methods with deep convolutional neural networks for image classification. J. Appl. Stat., 2018, 45(15), 2800-2818.
[http://dx.doi.org/10.1080/02664763.2018.1441383] [PMID: 31631918]
[52]
Madani, A.; Krause, B.; Greene, E.R.; Subramanian, S.; Mohr, B.P.; Holton, J.M.; Olmos, J.L., Jr; Xiong, C.; Sun, Z.Z.; Socher, R.; Fraser, J.S.; Naik, N. Large language models generate functional protein sequences across diverse families. Nat. Biotechnol., 2023, 1-8.
[http://dx.doi.org/10.1038/s41587-022-01618-2] [PMID: 36702895]
[53]
Russ, W.P.; Figliuzzi, M.; Stocker, C.; Barrat-Charlaix, P.; Socolich, M.; Kast, P.; Hilvert, D.; Monasson, R.; Cocco, S.; Weigt, M.; Ranganathan, R. An evolution-based model for designing chorismate mutase enzymes. Science, 2020, 369(6502), 440-445.
[http://dx.doi.org/10.1126/science.aba3304] [PMID: 32703877]
[54]
Endelman, J.B.; Silberg, J.J.; Wang, Z.G.; Arnold, F.H. Site-directed protein recombination as a shortest-path problem. Protein Eng. Des. Sel., 2004, 17(7), 589-594.
[http://dx.doi.org/10.1093/protein/gzh067] [PMID: 15331774]
[55]
Bedbrook, C.N.; Rice, A.J.; Yang, K.K.; Ding, X.; Chen, S.; LeProust, E.M.; Gradinaru, V.; Arnold, F.H. Structure-guided SCHEMA recombination generates diverse chimeric channelrhodopsins. Proc. Natl. Acad. Sci. USA, 2017, 114(13), E2624-E2633.
[http://dx.doi.org/10.1073/pnas.1700269114] [PMID: 28283661]
[56]
Silva, D.A.; Correia, B.E.; Procko, E. Motif-driven design of protein–protein interfaces. Methods Mol. Biol., 2016, 1414, 285-304.
[http://dx.doi.org/10.1007/978-1-4939-3569-7_17] [PMID: 27094298]
[57]
Procko, E.; Berguig, G.Y.; Shen, B.W.; Song, Y.; Frayo, S.; Convertine, A.J.; Margineantu, D.; Booth, G.; Correia, B.E.; Cheng, Y.; Schief, W.R.; Hockenbery, D.M.; Press, O.W.; Stoddard, B.L.; Stayton, P.S.; Baker, D. A computationally designed inhibitor of an Epstein-Barr viral Bcl-2 protein induces apoptosis in infected cells. Cell, 2014, 157(7), 1644-1656.
[http://dx.doi.org/10.1016/j.cell.2014.04.034] [PMID: 24949974]
[58]
Kim, J.W.; Kim, S.; Lee, H.; Cho, G.; Kim, S.C.; Lee, H.; Jin, M.S.; Lee, J.O. Application of antihelix antibodies in protein structure determination. Proc. Natl. Acad. Sci. USA, 2019, 116(36), 17786-17791.
[http://dx.doi.org/10.1073/pnas.1910080116] [PMID: 31371498]
[59]
Yang, C.; Sesterhenn, F.; Bonet, J.; van Aalen, E.A.; Scheller, L.; Abriata, L.A.; Cramer, J.T.; Wen, X.; Rosset, S.; Georgeon, S.; Jardetzky, T.; Krey, T.; Fussenegger, M.; Merkx, M.; Correia, B.E. Bottom-up de novo design of functional proteins with complex structural features. Nat. Chem. Biol., 2021, 17(4), 492-500.
[http://dx.doi.org/10.1038/s41589-020-00699-x] [PMID: 33398169]
[60]
Sesterhenn, F.; Yang, C.; Bonet, J.; Cramer, J.T.; Wen, X.; Wang, Y.; Chiang, C.I.; Abriata, L.A.; Kucharska, I.; Castoro, G.; Vollers, S.S.; Galloux, M.; Dheilly, E.; Rosset, S.; Corthésy, P.; Georgeon, S.; Villard, M.; Richard, C.A.; Descamps, D.; Delgado, T.; Oricchio, E.; Rameix-Welti, M.A.; Más, V.; Ervin, S.; Eléouët, J.F.; Riffault, S.; Bates, J.T.; Julien, J.P.; Li, Y.; Jardetzky, T.; Krey, T.; Correia, B.E. De novo protein design enables the precise induction of RSV-neutralizing antibodies. Science, 2020, 368(6492), eaay5051.
[http://dx.doi.org/10.1126/science.aay5051] [PMID: 32409444]
[61]
Bonet, J.; Wehrle, S.; Schriever, K.; Yang, C.; Billet, A.; Sesterhenn, F.; Scheck, A.; Sverrisson, F.; Veselkova, B.; Vollers, S.; Lourman, R.; Villard, M.; Rosset, S.; Krey, T.; Correia, B.E. Rosetta FunFolDes – A general framework for the computational design of functional proteins. PLOS Comput. Biol., 2018, 14(11), e1006623.
[http://dx.doi.org/10.1371/journal.pcbi.1006623] [PMID: 30452434]
[62]
Huang, P.S.; Ban, Y.E.A.; Richter, F.; Andre, I.; Vernon, R.; Schief, W.R.; Baker, D. RosettaRemodel: A generalized framework for flexible backbone protein design. PLoS One, 2011, 6(8), e24109.
[http://dx.doi.org/10.1371/journal.pone.0024109] [PMID: 21909381]
[63]
Wood, C.W.; Heal, J.W.; Thomson, A.R.; Bartlett, G.J.; Ibarra, A.Á.; Brady, R.L.; Sessions, R.B.; Woolfson, D.N. ISAMBARD: An open-source computational environment for biomolecular analysis, modelling and design. Bioinformatics, 2017, 33(19), 3043-3050.
[http://dx.doi.org/10.1093/bioinformatics/btx352] [PMID: 28582565]
[64]
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]
[65]
Lin, Z.; Akin, H.; Rao, R.; Hie, B.; Zhu, Z.; Lu, W.; Smetanin, N.; Verkuil, R.; Kabeli, O.; Shmueli, Y.; dos Santos Costa, A.; Fazel-Zarandi, M.; Sercu, T.; Candido, S.; Rives, A. Evolutionary-scale prediction of atomic level protein structure with a language model. bioRxiv, 2022.
[http://dx.doi.org/10.1101/2022.07.20.500902]
[66]
Wu, R.; Ding, F.; Wang, R.; Shen, R.; Zhang, X.; Luo, S.; Su, C.; Wu, Z.; Xie, Q.; Berger, B.; Ma, J.; Peng, J. High-resolution de novo structure prediction from primary sequence. bioRxiv, 2022.
[http://dx.doi.org/10.1101/2022.07.21.500999]
[67]
Yang, J.; Anishchenko, I.; Park, H.; Peng, Z.; Ovchinnikov, S.; Baker, D. Improved protein structure prediction using predicted interresidue orientations. Proc. Natl. Acad. Sci. USA, 2020, 117(3), 1496-1503.
[http://dx.doi.org/10.1073/pnas.1914677117] [PMID: 31896580]
[68]
Xue, W.; Wang, P.; Li, B.; Li, Y.; Xu, X.; Yang, F.; Yao, X.; Chen, Y.Z.; Xu, F.; Zhu, F. Identification of the inhibitory mechanism of FDA approved selective serotonin reuptake inhibitors: An insight from molecular dynamics simulation study. Phys. Chem. Chem. Phys., 2016, 18(4), 3260-3271.
[http://dx.doi.org/10.1039/C5CP05771J] [PMID: 26745505]
[69]
Zheng, G.; Xue, W.; Yang, F.; Zhang, Y.; Chen, Y.; Yao, X.; Zhu, F. Revealing vilazodone’s binding mechanism underlying its partial agonism to the 5-HT1A receptor in the treatment of major depressive disorder. Phys. Chem. Chem. Phys., 2017, 19(42), 28885-28896.
[http://dx.doi.org/10.1039/C7CP05688E] [PMID: 29057413]
[70]
Xue, W.; Wang, P.; Tu, G.; Yang, F.; Zheng, G.; Li, X.; Li, X.; Chen, Y.; Yao, X.; Zhu, F. Computational identification of the binding mechanism of a triple reuptake inhibitor amitifadine for the treatment of major depressive disorder. Phys. Chem. Chem. Phys., 2018, 20(9), 6606-6616.
[http://dx.doi.org/10.1039/C7CP07869B] [PMID: 29451287]
[71]
Xue, W.; Yang, F.; Wang, P.; Zheng, G.; Chen, Y.; Yao, X.; Zhu, F. What contributes to serotonin–norepinephrine reuptake inhibitors’ dual-targeting mechanism? the key role of transmembrane domain 6 in human berotonin and norepinephrine transporters revealed by molecular dynamics simulation. ACS Chem. Neurosci., 2018, 9(5), 1128-1140.
[http://dx.doi.org/10.1021/acschemneuro.7b00490] [PMID: 29300091]
[72]
Du, Q.; Qian, Y.; Xue, W. Molecular simulation of oncostatin M and receptor (OSM–OSMR) interaction as a potential therapeutic target for inflammatory bowel disease. Front. Mol. Biosci., 2020, 7, 29.
[http://dx.doi.org/10.3389/fmolb.2020.00029] [PMID: 32195265]
[73]
Xue, W.; Fu, T.; Deng, S.; Yang, F.; Yang, J.; Zhu, F. Molecular mechanism for the allosteric inhibition of the human serotonin transporter by antidepressant escitalopram. ACS Chem. Neurosci., 2022, 13(3), 340-351.
[http://dx.doi.org/10.1021/acschemneuro.1c00694] [PMID: 35041375]
[74]
Filipe, H.A.L.; Loura, L.M.S. Molecular dynamics simulations: Advances and applications. Molecules, 2022, 27(7), 2105.
[http://dx.doi.org/10.3390/molecules27072105] [PMID: 35408504]
[75]
Wang, X.; Li, F.; Qiu, W.; Xu, B.; Li, Y.; Lian, X.; Yu, H.; Zhang, Z.; Wang, J.; Li, Z.; Xue, W.; Zhu, F. SYNBIP: Synthetic binding proteins for research, diagnosis and therapy. Nucleic Acids Res., 2022, 50(D1), D560-D570.
[http://dx.doi.org/10.1093/nar/gkab926] [PMID: 34664670]
[76]
Eastman, P.; Behara, P.K.; Dotson, D.L.; Galvelis, R.; Herr, J.E.; Horton, J.T.; Mao, Y.; Chodera, J.D.; Pritchard, B.P.; Wang, Y.; De Fabritiis, G.; Markland, T.E. SPICE, A dataset of drug-like molecules and peptides for training machine learning potentials. Sci. Data, 2023, 10(1), 11.
[http://dx.doi.org/10.1038/s41597-022-01882-6] [PMID: 36599873]

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