Computational Protein Design - Where it goes?


Cite item

Full Text

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.

About the authors

Binbin Xu

Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University

Email: info@benthamscience.net

Yingjun Chen

Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University

Email: info@benthamscience.net

Weiwei Xue

Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University

Author for correspondence.
Email: info@benthamscience.net

References

  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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 10.1109/TCBB.2010.93 PMID: 20855926
  35. Seeger, M. Gaussian processes for machine learning. Int. J. Neural Syst., 2004, 14(2), 69-106. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 10.1038/s41597-022-01882-6 PMID: 36599873

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2024 Bentham Science Publishers