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image of From CASP13 to the Nobel Prize: DeepMind’s AlphaFold Journey in Revolutionizing Protein Structure Prediction and Beyond

Abstract

Four years ago, at the 14th Critical Assessment of Structure Prediction (CASP14), John Moult made a historic announcement that the long-standing challenge of Protein Structure Prediction—a problem that had confounded scientists for over five decades—had been “solved” for single protein chains. Supporting this groundbreaking statement was a plot depicting the median Global Distance Test (GDT) across 87 out of 92 domains, where AlphaFold2, developed by DeepMind, achieved an unprecedented score of 92.4. The bar chart not only underscored AlphaFold2’s remarkable performance—standing out prominently among other methods—but also revealed a level of accuracy that exceeded all prior expectations. In the years since this breakthrough, DeepMind's team has made significant strides. The AlphaFold Database now hosts approximately 214 million structures for various model organisms, covering nearly the entire genome. Research continues to explore multiple facets of protein science, including the prediction of multi-chain protein complex structures and the impact of missense mutations on protein function. The open availability of this extensive database and the suite of AlphaFold2 algorithms has catalysed remarkable advancements in protein biology and bioinformatics. This review will begin by revisiting DeepMind's early efforts in CASP13, detailing the architecture and the remarkable progress that led to their breakthrough of AlphaFold2 in CASP14 (2020). It will then delve into two main areas: (1) AlphaFold’s contributions to the scientific community across various fields over the past four years, and (2) the latest improvements, enhancements, and achievements by DeepMind, including AlphaFold3 and the Nobel Prize in Chemistry.

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2025-09-05
2026-01-02

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References

  1. Dill K.A. MacCallum J.L. The protein-folding problem, 50 years on. Science 2012 338 6110 1042 1046 10.1126/science.1219021 23180855
    [Google Scholar]
  2. Anfinsen C.B. Haber E. Sela M. White F.H. The kinetics of formation of native ribonuclease during oxidation of the reduced polypeptide chain. Proc. Natl. Acad. Sci. USA 1961 47 9 1309 1314 10.1073/pnas.47.9.1309 13683522
    [Google Scholar]
  3. Anfinsen C.B. Principles that govern the folding of protein chains. Science 1973 181 4096 223 230 10.1126/science.181.4096.223 4124164
    [Google Scholar]
  4. Karplus M. The Levinthal paradox: Yesterday and today. Fold. Des. 1997 2 4 S69 S75 10.1016/S1359‑0278(97)00067‑9 9269572
    [Google Scholar]
  5. Dill K.A. Chan H.S. From Levinthal to pathways to funnels. Nat. Struct. Mol. Biol. 1997 4 1 10 19 10.1038/nsb0197‑10 8989315
    [Google Scholar]
  6. Dill K.A. Theory for the folding and stability of globular proteins. Biochemistry 1985 24 6 1501 1509 10.1021/bi00327a032 3986190
    [Google Scholar]
  7. Shirts M. Pande V.S. Screen savers of the world unite! Science 2000 290 5498 1903 1904 10.1126/science.290.5498.1903 17742054
    [Google Scholar]
  8. Kleffner R. Flatten J. Leaver-Fay A. Baker D. Siegel J.B. Khatib F. Cooper S. Foldit Standalone: A video game-derived protein structure manipulation interface using Rosetta. Bioinformatics 2017 33 17 2765 2767 10.1093/bioinformatics/btx283 28481970
    [Google Scholar]
  9. AlQuraishi M. End-to-end differentiable learning of protein structure. Cell Syst. 2019 8 4 292 301.e3 10.1016/j.cels.2019.03.006 31005579
    [Google Scholar]
  10. AlQuraishi M. Machine learning in protein structure prediction. Curr. Opin. Chem. Biol. 2021 65 1 8 10.1016/j.cbpa.2021.04.005 34015749
    [Google Scholar]
  11. Moult J. Pedersen J.T. Judson R. Fidelis K. A large‐scale experiment to assess protein structure prediction methods. Proteins 1995 23 3 ii v 10.1002/prot.340230303 8710822
    [Google Scholar]
  12. 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 10.1002/prot.26237 34533838
    [Google Scholar]
  13. Burley S.K. Bhikadiya C. Bi C. Bittrich S. Chen L. Crichlow G.V. Duarte J.M. Dutta S. Fayazi M. Feng Z. Flatt J.W. Ganesan S.J. Goodsell D.S. Ghosh S. Kramer Green R. Guranovic V. Henry J. Hudson B.P. Lawson C.L. Liang Y. Lowe R. Peisach E. Persikova I. Piehl D.W. Rose Y. Sali A. Segura J. Sekharan M. Shao C. Vallat B. Voigt M. Westbrook J.D. Whetstone S. Young J.Y. Zardecki C. RCSB Protein data bank: Celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D. Protein Sci. 2022 31 1 187 208 10.1002/pro.4213 34676613
    [Google Scholar]
  14. Silver D. Huang A. Maddison C.J. Guez A. Sifre L. van den Driessche G. Schrittwieser J. Antonoglou I. Panneershelvam V. Lanctot M. Dieleman S. Grewe D. Nham J. Kalchbrenner N. Sutskever I. Lillicrap T. Leach M. Kavukcuoglu K. Graepel T. Hassabis D. Mastering the game of Go with deep neural networks and tree search. Nature 2016 529 7587 484 489 10.1038/nature16961 26819042
    [Google Scholar]
  15. Chaslot G.M.J.B. Winands M.H.M. Van Den Herik H.J. Parallel Monte-Carlo Tree Search. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2008 5131 60 71 10.1007/978‑3‑540‑87608‑3_6
    [Google Scholar]
  16. Zwanzig R. Szabo A. Bagchi B. Levinthal’s paradox. Proc. Natl. Acad. Sci. USA 1992 89 1 20 22 10.1073/pnas.89.1.20 1729690
    [Google Scholar]
  17. 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 10.1002/prot.25834 31602685
    [Google Scholar]
  18. Abriata L.A. Tamò G.E. Dal Peraro M. A further leap of improvement in tertiary structure prediction in CASP13 prompts new routes for future assessments. Proteins 2019 87 12 1100 1112 10.1002/prot.25787 31344267
    [Google Scholar]
  19. Zemla A. LGA: A method for finding 3D similarities in protein structures. Nucleic Acids Res. 2003 31 13 3370 3374 10.1093/nar/gkg571 12824330
    [Google Scholar]
  20. Pearce R. Zhang Y. Toward the solution of the protein structure prediction problem. J. Biol. Chem. 2021 297 1 100870 10.1016/j.jbc.2021.100870 34119522
    [Google Scholar]
  21. Abbass J. Nebel J.C. Rosetta and the journey to predict proteins’ structures, 20 years on. Curr. Bioinform. 2020 15 6 611 628 10.2174/1574893615999200504103643
    [Google Scholar]
  22. 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 10.1038/s41586‑019‑1923‑7 31942072
    [Google Scholar]
  23. Zhang Y. Skolnick J. TM-align: A protein structure alignment algorithm based on the TM-score. Nucleic Acids Res. 2005 33 7 2302 2309 10.1093/nar/gki524 15849316
    [Google Scholar]
  24. AlQuraishi M. AlphaFold at CASP13. Bioinformatics 2019 35 22 4862 4865 10.1093/bioinformatics/btz422 31116374
    [Google Scholar]
  25. Xu J. Distance-based protein folding powered by deep learning. Proc. Natl. Acad. Sci. USA 2019 116 34 16856 16865 10.1073/pnas.1821309116 31399549
    [Google Scholar]
  26. Zhu J. Wang S. Bu D. Xu J. Protein threading using residue co-variation and deep learning. Bioinformatics 2018 34 13 i263 i273 10.1093/bioinformatics/bty278 29949980
    [Google Scholar]
  27. Zhao F. Xu J. A position-specific distance-dependent statistical potential for protein structure and functional study. Structure 2012 20 6 1118 1126 10.1016/j.str.2012.04.003 22608968
    [Google Scholar]
  28. Kandathil S.M. Greener J.G. Jones D.T. Recent developments in deep learning applied to protein structure prediction. Proteins 2019 87 12 1179 1189 10.1002/prot.25824 31589782
    [Google Scholar]
  29. 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 1005659 10.1371/journal.pcbi.1005659 28746339
    [Google Scholar]
  30. Hornak V. Abel R. Okur A. Strockbine B. Roitberg A. Simmerling C. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 2006 65 3 712 725 10.1002/prot.21123 16981200
    [Google Scholar]
  31. Xie Q. Luong M.T. Hovy E. Le Q.V. Self-training with noisy student improves imagenet classification. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2020 10684 10695 10.1109/CVPR42600.2020.01070
    [Google Scholar]
  32. Mirdita M. von den Driesch L. Galiez C. Martin M.J. Söding J. Steinegger M. Uniclust databases of clustered and deeply annotated protein sequences and alignments. Nucleic Acids Res. 2017 45 D1 D170 D176 10.1093/nar/gkw1081 27899574
    [Google Scholar]
  33. 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 10.1038/s41586‑021‑03819‑2 34265844
    [Google Scholar]
  34. Mariani V. Biasini M. Barbato A. Schwede T. lDDT: A local superposition-free score for comparing protein structures and models using distance difference tests. Bioinformatics 2013 29 21 2722 2728 10.1093/bioinformatics/btt473 23986568
    [Google Scholar]
  35. 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. Silver D. Vinyals O. Senior A.W. Kavukcuoglu K. Kohli P. Hassabis D. Applying and improving AlphaFold at CASP14. Proteins 2021 89 12 1711 1721 10.1002/prot.26257 34599769
    [Google Scholar]
  36. Zhang Y. Skolnick J. Scoring function for automated assessment of protein structure template quality. Proteins 2004 57 4 702 710 10.1002/prot.20264 15476259
    [Google Scholar]
  37. Kryshtafovych A. Moult J. Billings W.M. Della Corte D. Fidelis K. Kwon S. Olechnovič K. Seok C. Venclovas Č. Won J. Modeling SARS‐CoV‐2 proteins in the CASP‐commons experiment. Proteins 2021 89 12 1987 1996 10.1002/prot.26231 34462960
    [Google Scholar]
  38. Lupas A.N. Pereira J. Alva V. Merino F. Coles M. Hartmann M.D. The breakthrough in protein structure prediction. Biochem. J. 2021 478 10 1885 1890 10.1042/BCJ20200963 34029366
    [Google Scholar]
  39. Kinch L.N. Pei J. Kryshtafovych A. Schaeffer R.D. Grishin N.V. Topology evaluation of models for difficult targets in the 14th round of the critical assessment of protein structure prediction (CASP14). Proteins 2021 89 12 1673 1686 10.1002/prot.26172 34240477
    [Google Scholar]
  40. Tunyasuvunakool K. Adler J. Wu Z. Green T. Zielinski M. Žídek A. Bridgland A. Cowie A. Meyer C. Laydon A. Velankar S. Kleywegt G.J. Bateman A. Evans R. Pritzel A. Figurnov M. Ronneberger O. Bates R. Kohl S.A.A. Potapenko A. Ballard A.J. Romera-Paredes B. Nikolov S. Jain R. Clancy E. Reiman D. Petersen S. Senior A.W. Kavukcuoglu K. Birney E. Kohli P. Jumper J. Hassabis D. Highly accurate protein structure prediction for the human proteome. Nature 2021 596 7873 590 596 10.1038/s41586‑021‑03828‑1 34293799
    [Google Scholar]
  41. Venter J.C. Adams M.D. Myers E.W. Li P.W. Mural R.J. Sutton G.G. Smith H.O. Yandell M. Evans C.A. Holt R.A. Gocayne J.D. Amanatides P. Ballew R.M. Huson D.H. Wortman J.R. Zhang Q. Kodira C.D. Zheng X.H. Chen L. Skupski M. Subramanian G. Thomas P.D. Zhang J. Gabor Miklos G.L. Nelson C. Broder S. Clark A.G. Nadeau J. McKusick V.A. Zinder N. Levine A.J. Roberts R.J. Simon M. Slayman C. Hunkapiller M. Bolanos R. Delcher A. Dew I. Fasulo D. Flanigan M. Florea L. Halpern A. Hannenhalli S. Kravitz S. Levy S. Mobarry C. Reinert K. Remington K. Abu-Threideh J. Beasley E. Biddick K. Bonazzi V. Brandon R. Cargill M. Chandramouliswaran I. Charlab R. Chaturvedi K. Deng Z. Francesco V.D. Dunn P. Eilbeck K. Evangelista C. Gabrielian A.E. Gan W. Ge W. Gong F. Gu Z. Guan P. Heiman T.J. Higgins M.E. Ji R.R. Ke Z. Ketchum K.A. Lai Z. Lei Y. Li Z. Li J. Liang Y. Lin X. Lu F. Merkulov G.V. Milshina N. Moore H.M. Naik A.K. Narayan V.A. Neelam B. Nusskern D. Rusch D.B. Salzberg S. Shao W. Shue B. Sun J. Wang Z.Y. Wang A. Wang X. Wang J. Wei M.H. Wides R. Xiao C. Yan C. Yao A. Ye J. Zhan M. Zhang W. Zhang H. Zhao Q. Zheng L. Zhong F. Zhong W. Zhu S.C. Zhao S. Gilbert D. Baumhueter S. Spier G. Carter C. Cravchik A. Woodage T. Ali F. An H. Awe A. Baldwin D. Baden H. Barnstead M. Barrow I. Beeson K. Busam D. Carver A. Center A. Cheng M.L. Curry L. Danaher S. Davenport L. Desilets R. Dietz S. Dodson K. Doup L. Ferriera S. Garg N. Gluecksmann A. Hart B. Haynes J. Haynes C. Heiner C. Hladun S. Hostin D. Houck J. Howland T. Ibegwam C. Johnson J. Kalush F. Kline L. Koduru S. Love A. Mann F. May D. McCawley S. McIntosh T. McMullen I. Moy M. Moy L. Murphy B. Nelson K. Pfannkoch C. Pratts E. Puri V. Qureshi H. Reardon M. Rodriguez R. Rogers Y.H. Romblad D. Ruhfel B. Scott R. Sitter C. Smallwood M. Stewart E. Strong R. Suh E. Thomas R. Tint N.N. Tse S. Vech C. Wang G. Wetter J. Williams S. Williams M. Windsor S. Winn-Deen E. Wolfe K. Zaveri J. Zaveri K. Abril J.F. Guigó R. Campbell M.J. Sjolander K.V. Karlak B. Kejariwal A. Mi H. Lazareva B. Hatton T. Narechania A. Diemer K. Muruganujan A. Guo N. Sato S. Bafna V. Istrail S. Lippert R. Schwartz R. Walenz B. Yooseph S. Allen D. Basu A. Baxendale J. Blick L. Caminha M. Carnes-Stine J. Caulk P. Chiang Y.H. Coyne M. Dahlke C. Mays A.D. Dombroski M. Donnelly M. Ely D. Esparham S. Fosler C. Gire H. Glanowski S. Glasser K. Glodek A. Gorokhov M. Graham K. Gropman B. Harris M. Heil J. Henderson S. Hoover J. Jennings D. Jordan C. Jordan J. Kasha J. Kagan L. Kraft C. Levitsky A. Lewis M. Liu X. Lopez J. Ma D. Majoros W. McDaniel J. Murphy S. Newman M. Nguyen T. Nguyen N. Nodell M. Pan S. Peck J. Peterson M. Rowe W. Sanders R. Scott J. Simpson M. Smith T. Sprague A. Stockwell T. Turner R. Venter E. Wang M. Wen M. Wu D. Wu M. Xia A. Zandieh A. Zhu X. The sequence of the human genome. Science 2001 291 5507 1304 1351 10.1126/science.1058040 11181995
    [Google Scholar]
  42. Lander E.S. Linton L.M. Birren B. Nusbaum C. Zody M.C. Baldwin J. Devon K. Dewar K. Doyle M. FitzHugh W. Funke R. Gage D. Harris K. Heaford A. Howland J. Kann L. Lehoczky J. LeVine R. McEwan P. McKernan K. Meldrim J. Mesirov J.P. Miranda C. Morris W. Naylor J. Raymond C. Rosetti M. Santos R. Sheridan A. Sougnez C. Stange-Thomann N. Stojanovic N. Subramanian A. Wyman D. Rogers J. Sulston J. Ainscough R. Beck S. Bentley D. Burton J. Clee C. Carter N. Coulson A. Deadman R. Deloukas P. Dunham A. Dunham I. Durbin R. French L. Grafham D. Gregory S. Hubbard T. Humphray S. Hunt A. Jones M. Lloyd C. McMurray A. Matthews L. Mercer S. Milne S. Mullikin J.C. Mungall A. Plumb R. Ross M. Shownkeen R. Sims S. Waterston R.H. Wilson R.K. Hillier L.W. McPherson J.D. Marra M.A. Mardis E.R. Fulton L.A. Chinwalla A.T. Pepin K.H. Gish W.R. Chissoe S.L. Wendl M.C. Delehaunty K.D. Miner T.L. Delehaunty A. Kramer J.B. Cook L.L. Fulton R.S. Johnson D.L. Minx P.J. Clifton S.W. Hawkins T. Branscomb E. Predki P. Richardson P. Wenning S. Slezak T. Doggett N. Cheng J.F. Olsen A. Lucas S. Elkin C. Uberbacher E. Frazier M. Gibbs R.A. Muzny D.M. Scherer S.E. Bouck J.B. Sodergren E.J. Worley K.C. Rives C.M. Gorrell J.H. Metzker M.L. Naylor S.L. Kucherlapati R.S. Nelson D.L. Weinstock G.M. Sakaki Y. Fujiyama A. Hattori M. Yada T. Toyoda A. Itoh T. Kawagoe C. Watanabe H. Totoki Y. Taylor T. Weissenbach J. Heilig R. Saurin W. Artiguenave F. Brottier P. Bruls T. Pelletier E. Robert C. Wincker P. Rosenthal A. Platzer M. Nyakatura G. Taudien S. Rump A. Smith D.R. Doucette-Stamm L. Rubenfield M. Weinstock K. Lee H.M. Dubois J.A. Yang H. Yu J. Wang J. Huang G. Gu J. Hood L. Rowen L. Madan A. Qin S. Davis R.W. Federspiel N.A. Abola A.P. Proctor M.J. Roe B.A. Chen F. Pan H. Ramser J. Lehrach H. Reinhardt R. McCombie W.R. de la Bastide M. Dedhia N. Blöcker H. Hornischer K. Nordsiek G. Agarwala R. Aravind L. Bailey J.A. Bateman A. Batzoglou S. Birney E. Bork P. Brown D.G. Burge C.B. Cerutti L. Chen H-C. Church D. Clamp M. Copley R.R. Doerks T. Eddy S.R. Eichler E.E. Furey T.S. Galagan J. Gilbert J.G.R. Harmon C. Hayashizaki Y. Haussler D. Hermjakob H. Hokamp K. Jang W. Johnson L.S. Jones T.A. Kasif S. Kaspryzk A. Kennedy S. Kent W.J. Kitts P. Koonin E.V. Korf I. Kulp D. Lancet D. Lowe T.M. McLysaght A. Mikkelsen T. Moran J.V. Mulder N. Pollara V.J. Ponting C.P. Schuler G. Schultz J. Slater G. Smit A.F.A. Stupka E. Szustakowki J. Thierry-Mieg D. Thierry-Mieg J. Wagner L. Wallis J. Wheeler R. Williams A. Wolf Y.I. Wolfe K.H. Yang S-P. Yeh R-F. Collins F. Guyer M.S. Peterson J. Felsenfeld A. Wetterstrand K.A. Myers R.M. Schmutz J. Dickson M. Grimwood J. Cox D.R. Olson M.V. Kaul R. Raymond C. Shimizu N. Kawasaki K. Minoshima S. Evans G.A. Athanasiou M. Schultz R. Patrinos A. Morgan M.J. de Jong P. Catanese J.J. Osoegawa K. Shizuya H. Choi S. Chen Y.J. Szustakowki J. Initial sequencing and analysis of the human genome. Nature 2001 409 6822 860 921 10.1038/35057062 11237011
    [Google Scholar]
  43. Sehnal D. Bittrich S. Deshpande M. Svobodová R. Berka K. Bazgier V. Velankar S. Burley S.K. Koča J. Rose A.S. Mol* Viewer: Modern web app for 3D visualization and analysis of large biomolecular structures. Nucleic Acids Res. 2021 49 W1 W431 W437 10.1093/nar/gkab314 33956157
    [Google Scholar]
  44. Bhowmick A. Brookes D.H. Yost S.R. Dyson H.J. Forman-Kay J.D. Gunter D. Head-Gordon M. Hura G.L. Pande V.S. Wemmer D.E. Wright P.E. Head-Gordon T. Finding our way in the dark proteome. J. Am. Chem. Soc. 2016 138 31 9730 9742 10.1021/jacs.6b06543 27387657
    [Google Scholar]
  45. Basile W. Salvatore M. Bassot C. Elofsson A. Why do eukaryotic proteins contain more intrinsically disordered regions? PLOS Comput. Biol. 2019 15 7 1007186 10.1371/journal.pcbi.1007186 31329574
    [Google Scholar]
  46. Oates M.E. Romero P. Ishida T. Ghalwash M. Mizianty M.J. Xue B. Dosztányi Z. Uversky V.N. Obradovic Z. Kurgan L. Dunker A.K. Gough J. D2P2: Database of disordered protein predictions. Nucleic Acids Res. 2012 41 D1 D508 D516 10.1093/nar/gks1226 23203878
    [Google Scholar]
  47. Necci M. Piovesan D. Hoque M.T. Walsh I. Iqbal S. Vendruscolo M. Sormanni P. Wang C. Raimondi D. Sharma R. Zhou Y. Litfin T. Galzitskaya O.V. Lobanov M.Y. Vranken W. Wallner B. Mirabello C. Malhis N. Dosztányi Z. Erdős G. Mészáros B. Gao J. Wang K. Hu G. Wu Z. Sharma A. Hanson J. Paliwal K. Callebaut I. Bitard-Feildel T. Orlando G. Peng Z. Xu J. Wang S. Jones D.T. Cozzetto D. Meng F. Yan J. Gsponer J. Cheng J. Wu T. Kurgan L. Promponas V.J. Tamana S. Marino-Buslje C. Martínez-Pérez E. Chasapi A. Ouzounis C. Dunker A.K. Kajava A.V. Leclercq J.Y. Aykac-Fas B. Lambrughi M. Maiani E. Papaleo E. Chemes L.B. Álvarez L. González-Foutel N.S. Iglesias V. Pujols J. Ventura S. Palopoli N. Benítez G.I. Parisi G. Bassot C. Elofsson A. Govindarajan S. Lamb J. Salvatore M. Hatos A. Monzon A.M. Bevilacqua M. Mičetić I. Minervini G. Paladin L. Quaglia F. Leonardi E. Davey N. Horvath T. Kovacs O.P. Murvai N. Pancsa R. Schad E. Szabo B. Tantos A. Macedo-Ribeiro S. Manso J.A. Pereira P.J.B. Davidović R. Veljkovic N. Hajdu-Soltész B. Pajkos M. Szaniszló T. Guharoy M. Lazar T. Macossay-Castillo M. Tompa P. Tosatto S.C.E. Critical assessment of protein intrinsic disorder prediction. Nat. Methods 2021 18 5 472 481 10.1038/s41592‑021‑01117‑3 33875885
    [Google Scholar]
  48. Huang Y.J. Zhang N. Bersch B. Fidelis K. Inouye M. Ishida Y. Kryshtafovych A. Kobayashi N. Kuroda Y. Liu G. LiWang, A.; Swapna, G.V.T.; Wu, N.; Yamazaki, T.; Montelione, G.T. Assessment of prediction methods for protein structures determined by NMR in CASP14: Impact of AlphaFold2. Proteins 2021 89 12 1959 1976 10.1002/prot.26246 34559429
    [Google Scholar]
  49. Varadi M. Anyango S. Deshpande M. Nair S. Natassia C. Yordanova G. Yuan D. Stroe O. Wood G. Laydon A. Žídek A. Green T. Tunyasuvunakool K. Petersen S. Jumper J. Clancy E. Green R. Vora A. Lutfi M. Figurnov M. Cowie A. Hobbs N. Kohli P. Kleywegt G. Birney E. Hassabis D. Velankar S. Alphafold protein structure database: Massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 2022 50 D1 D439 D444 10.1093/nar/gkab1061 34791371
    [Google Scholar]
  50. Varadi M. Bertoni D. Magana P. Paramval U. Pidruchna I. Radhakrishnan M. Tsenkov M. Nair S. Mirdita M. Yeo J. Kovalevskiy O. Tunyasuvunakool K. Laydon A. Žídek A. Tomlinson H. Hariharan D. Abrahamson J. Green T. Jumper J. Birney E. Steinegger M. Hassabis D. Velankar S. Alphafold protein structure database in 2024: Providing structure coverage for over 214 million protein sequences. Nucleic Acids Res. 2024 52 D1 D368 D375 10.1093/nar/gkad1011 37933859
    [Google Scholar]
  51. Bateman A. Martin M-J. Orchard S. Magrane M. Ahmad S. Alpi E. Bowler-Barnett E.H. Britto R. Bye-A-Jee H. Cukura A. Denny P. Dogan T. Ebenezer T.G. Fan J. Garmiri P. da Costa Gonzales L.J. Hatton-Ellis E. Hussein A. Ignatchenko A. Insana G. Ishtiaq R. Joshi V. Jyothi D. Kandasaamy S. Lock A. Luciani A. Lugaric M. Luo J. Lussi Y. MacDougall A. Madeira F. Mahmoudy M. Mishra A. Moulang K. Nightingale A. Pundir S. Qi G. Raj S. Raposo P. Rice D.L. Saidi R. Santos R. Speretta E. Stephenson J. Totoo P. Turner E. Tyagi N. Vasudev P. Warner K. Watkins X. Zaru R. Zellner H. Bridge A.J. Aimo L. Argoud-Puy G. Auchincloss A.H. Axelsen K.B. Bansal P. Baratin D. Batista Neto T.M. Blatter M-C. Bolleman J.T. Boutet E. Breuza L. Gil B.C. Casals-Casas C. Echioukh K.C. Coudert E. Cuche B. de Castro E. Estreicher A. Famiglietti M.L. Feuermann M. Gasteiger E. Gaudet P. Gehant S. Gerritsen V. Gos A. Gruaz N. Hulo C. Hyka-Nouspikel N. Jungo F. Kerhornou A. Le Mercier P. Lieberherr D. Masson P. Morgat A. Muthukrishnan V. Paesano S. Pedruzzi I. Pilbout S. Pourcel L. Poux S. Pozzato M. Pruess M. Redaschi N. Rivoire C. Sigrist C.J.A. Sonesson K. Sundaram S. Wu C.H. Arighi C.N. Arminski L. Chen C. Chen Y. Huang H. Laiho K. McGarvey P. Natale D.A. Ross K. Vinayaka C.R. Wang Q. Wang Y. Zhang J. UniProt: The universal protein knowledgebase in 2023. Nucleic Acids Res. 2023 51 D1 D523 D531 10.1093/nar/gkac1052 36408920
    [Google Scholar]
  52. Camacho C. Coulouris G. Avagyan V. Ma N. Papadopoulos J. Bealer K. Madden T.L. BLAST+: Architecture and applications. BMC Bioinformatics 2009 10 1 421 10.1186/1471‑2105‑10‑421 20003500
    [Google Scholar]
  53. Paysan-Lafosse T. Blum M. Chuguransky S. Grego T. Pinto B.L. Salazar G.A. Bileschi M.L. Bork P. Bridge A. Colwell L. Gough J. Haft D.H. Letunić I. Marchler-Bauer A. Mi H. Natale D.A. Orengo C.A. Pandurangan A.P. Rivoire C. Sigrist C.J.A. Sillitoe I. Thanki N. Thomas P.D. Tosatto S.C.E. Wu C.H. Bateman A. InterPro in 2022. Nucleic Acids Res. 2023 51 D1 D418 D427 10.1093/nar/gkac993 36350672
    [Google Scholar]
  54. Cunningham F. Allen J.E. Allen J. Alvarez-Jarreta J. Amode M.R. Armean I.M. Austine-Orimoloye O. Azov A.G. Barnes I. Bennett R. Berry A. Bhai J. Bignell A. Billis K. Boddu S. Brooks L. Charkhchi M. Cummins C. Da Rin Fioretto L. Davidson C. Dodiya K. Donaldson S. El Houdaigui B. El Naboulsi T. Fatima R. Giron C.G. Genez T. Martinez J.G. Guijarro-Clarke C. Gymer A. Hardy M. Hollis Z. Hourlier T. Hunt T. Juettemann T. Kaikala V. Kay M. Lavidas I. Le T. Lemos D. Marugán J.C. Mohanan S. Mushtaq A. Naven M. Ogeh D.N. Parker A. Parton A. Perry M. Piližota I. Prosovetskaia I. Sakthivel M.P. Salam A.I.A. Schmitt B.M. Schuilenburg H. Sheppard D. Pérez-Silva J.G. Stark W. Steed E. Sutinen K. Sukumaran R. Sumathipala D. Suner M.M. Szpak M. Thormann A. Tricomi F.F. Urbina-Gómez D. Veidenberg A. Walsh T.A. Walts B. Willhoft N. Winterbottom A. Wass E. Chakiachvili M. Flint B. Frankish A. Giorgetti S. Haggerty L. Hunt S.E. IIsley, G.R.; Loveland, J.E.; Martin, F.J.; Moore, B.; Mudge, J.M.; Muffato, M.; Perry, E.; Ruffier, M.; Tate, J.; Thybert, D.; Trevanion, S.J.; Dyer, S.; Harrison, P.W.; Howe, K.L.; Yates, A.D.; Zerbino, D.R.; Flicek, P. Ensembl 2022. Nucleic Acids Res. 2022 50 D1 D988 D995 10.1093/nar/gkab1049 34791404
    [Google Scholar]
  55. Varadi M. Anyango S. Armstrong D. Berrisford J. Choudhary P. Deshpande M. Nadzirin N. Nair S.S. Pravda L. Tanweer A. Al-Lazikani B. Andreini C. Barton G.J. Bednar D. Berka K. Blundell T. Brock K.P. Carazo J.M. Damborsky J. David A. Dey S. Dunbrack R. Recio J.F. Fraternali F. Gibson T. Helmer-Citterich M. Hoksza D. Hopf T. Jakubec D. Kannan N. Krivak R. Kumar M. Levy E.D. London N. Macias J.R. Srivatsan M.M. Marks D.S. Martens L. McGowan S.A. McGreig J.E. Modi V. Parra R.G. Pepe G. Piovesan D. Prilusky J. Putignano V. Radusky L.G. Ramasamy P. Rausch A.O. Reuter N. Rodriguez L.A. Rollins N.J. Rosato A. Rubach P. Serrano L. Singh G. Skoda P. Sorzano C.O.S. Stourac J. Sulkowska J.I. Svobodova R. Tichshenko N. Tosatto S.C.E. Vranken W. Wass M.N. Xue D. Zaidman D. Thornton J. Sternberg M. Orengo C. Velankar S. PDBe-KB: Collaboratively defining the biological context of structural data. Nucleic Acids Res. 2022 50 D1 D534 D542 10.1093/nar/gkab988 34755867
    [Google Scholar]
  56. Crystallography: Protein data bank. Nat. New Biol. 1971 233 42 223 223 10.1038/newbio233223b0
    [Google Scholar]
  57. Bittrich S. Bhikadiya C. Bi C. Chao H. Duarte J.M. Dutta S. Fayazi M. Henry J. Khokhriakov I. Lowe R. Piehl D.W. Segura J. Vallat B. Voigt M. Westbrook J.D. Burley S.K. Rose Y. RCSB protein data bank: Efficient searching and simultaneous access to one million computed structure models alongside the PDB structures enabled by architectural advances. J. Mol. Biol. 2023 435 14 167994 10.1016/j.jmb.2023.167994 36738985
    [Google Scholar]
  58. Burley S.K. Piehl D.W. Vallat B. Zardecki C. RCSB Protein Data Bank: Supporting research and education worldwide through explorations of experimentally determined and computationally predicted atomic level 3D biostructures. IUCrJ 2024 11 279 286 10.1107/S2052252524002604
    [Google Scholar]
  59. 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 10.1126/science.abj8754 34282049
    [Google Scholar]
  60. Vallat B. Tauriello G. Bienert S. Haas J. Webb B.M. Žídek A. Zheng W. Peisach E. Piehl D.W. Anischanka I. Sillitoe I. Tolchard J. Varadi M. Baker D. Orengo C. Zhang Y. Hoch J.C. Kurisu G. Patwardhan A. Velankar S. Burley S.K. Sali A. Schwede T. Berman H.M. Westbrook J.D. ModelCIF: An extension of PDBx/mmCIF data representation for computed structure models. J. Mol. Biol. 2023 435 14 168021 10.1016/j.jmb.2023.168021 36828268
    [Google Scholar]
  61. Shao C. Bittrich S. Wang S. Burley S.K. Assessing PDB macromolecular crystal structure confidence at the individual amino acid residue level. Structure 2022 30 10 1385 1394.e3 10.1016/j.str.2022.08.004 36049478
    [Google Scholar]
  62. Altschul S. Madden T.L. Schäffer A.A. Zhang J. Zhang Z. Miller W. Lipman D.J. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 1997 25 17 3389 3402 10.1093/nar/25.17.3389 9254694
    [Google Scholar]
  63. Sillitoe I. Bordin N. Dawson N. Waman V.P. Ashford P. Scholes H.M. Pang C.S.M. Woodridge L. Rauer C. Sen N. Abbasian M. Le Cornu S. Lam S.D. Berka K. Varekova I.H. Svobodova R. Lees J. Orengo C.A. CATH: Increased structural coverage of functional space. Nucleic Acids Res. 2021 49 D1 D266 D273 10.1093/nar/gkaa1079 33237325
    [Google Scholar]
  64. Lo Conte L. Ailey B. Hubbard T.J. Brenner S.E. Murzin A.G. Chothia C. SCOP: A structural classification of proteins database. Nucleic Acids Res. 2000 28 1 257 259 10.1093/nar/28.1.257 10592240
    [Google Scholar]
  65. Andreeva A. Howorth D. Chothia C. Kulesha E. Murzin A.G. SCOP2 prototype: A new approach to protein structure mining. Nucleic Acids Res. 2014 42 D1 D310 D314 10.1093/nar/gkt1242 24293656
    [Google Scholar]
  66. Andreeva A. Kulesha E. Gough J. Murzin A.G. The SCOP database in 2020: Expanded classification of representative family and superfamily domains of known protein structures. Nucleic Acids Res. 2020 48 D1 D376 D382 10.1093/nar/gkz1064 31724711
    [Google Scholar]
  67. Chandonia J.M. Guan L. Lin S. Yu C. Fox N.K. Brenner S.E. SCOPe: Improvements to the structural classification of proteins – extended database to facilitate variant interpretation and machine learning. Nucleic Acids Res. 2022 50 D1 D553 D559 10.1093/nar/gkab1054 34850923
    [Google Scholar]
  68. Cheng H. Schaeffer R.D. Liao Y. Kinch L.N. Pei J. Shi S. Kim B.H. Grishin N.V. ECOD: An evolutionary classification of protein domains. PLOS Comput. Biol. 2014 10 12 1003926 10.1371/journal.pcbi.1003926 25474468
    [Google Scholar]
  69. Schaeffer R.D. Zhang J. Kinch L.N. Pei J. Cong Q. Grishin N.V. Classification of domains in predicted structures of the human proteome. Proc. Natl. Acad. Sci. USA 2023 120 12 2214069120 10.1073/pnas.2214069120 36917664
    [Google Scholar]
  70. Bordin N. Sillitoe I. Nallapareddy V. Rauer C. Lam S.D. Waman V.P. Sen N. Heinzinger M. Littmann M. Kim S. Velankar S. Steinegger M. Rost B. Orengo C. AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms. Commun. Biol. 2023 6 1 160 10.1038/s42003‑023‑04488‑9 36755055
    [Google Scholar]
  71. Waman V.P. Bordin N. Alcraft R. Vickerstaff R. Rauer C. Chan Q. Sillitoe I. Yamamori H. Orengo C. CATH 2024: CATH-alphaflow doubles the number of structures in CATH and reveals nearly 200 new folds. J. Mol. Biol. 2024 436 17 168551 10.1016/j.jmb.2024.168551 38548261
    [Google Scholar]
  72. Durairaj J. Waterhouse A.M. Mets T. Brodiazhenko T. Abdullah M. Studer G. Tauriello G. Akdel M. Andreeva A. Bateman A. Tenson T. Hauryliuk V. Schwede T. Pereira J. Uncovering new families and folds in the natural protein universe. Nature 2023 622 7983 646 653 10.1038/s41586‑023‑06622‑3 37704037
    [Google Scholar]
  73. Barrio-Hernandez I. Yeo J. Jänes J. Mirdita M. Gilchrist C.L.M. Wein T. Varadi M. Velankar S. Beltrao P. Steinegger M. Clustering predicted structures at the scale of the known protein universe. Nature 2023 622 7983 637 645 10.1038/s41586‑023‑06510‑w 37704730
    [Google Scholar]
  74. Mistry J. Chuguransky S. Williams L. Qureshi M. Salazar G.A. Sonnhammer E.L.L. Tosatto S.C.E. Paladin L. Raj S. Richardson L.J. Finn R.D. Bateman A. Pfam: The protein families database in 2021. Nucleic Acids Res. 2021 49 D1 D412 D419 10.1093/nar/gkaa913 33125078
    [Google Scholar]
  75. Anand N. Achim T. Protein structure and sequence generation with equivariant denoising diffusion probabilistic models arXiv:220515019 2022 1 5 10.48550/arXiv.2205.15019
    [Google Scholar]
  76. Mosalaganti S. Obarska-Kosinska A. Siggel M. Taniguchi R. Turoňová B. Zimmerli C.E. AI-based structure prediction empowers integrative structural analysis of human nuclear pores. Science 1979 2022 376 10.1126/SCIENCE.ABM9506/SUPPL_FILE/SCIENCE.ABM9506_MDAR_REPRODUCIBILITY_CHECKLIST.PDF 35679397
    [Google Scholar]
  77. Kreitz J. Friedrich M.J. Guru A. Lash B. Saito M. Macrae R.K. Zhang F. Programmable protein delivery with a bacterial contractile injection system. Nature 2023 616 7956 357 364 10.1038/s41586‑023‑05870‑7 36991127
    [Google Scholar]
  78. Lim Y. Tamayo-Orrego L. Schmid E. Tarnauskaite Z. Kochenova O.V. Gruar R. In silico protein interaction screening uncovers DONSON’s role in replication initiation. Science 1979 2023 381 10.1126/SCIENCE.ADI3448/SUPPL_FILE/SCIENCE.ADI3448_MDAR_REPRODUCIBILITY_CHECKLIST.PDF
    [Google Scholar]
  79. Yang Z. Zeng X. Zhao Y. Chen R. AlphaFold2 and its applications in the fields of biology and medicine. Signal Transduct. Target. Ther. 2023 8 1 115 10.1038/s41392‑023‑01381‑z 36918529
    [Google Scholar]
  80. Hu L. Salmen W. Sankaran B. Lasanajak Y. Smith D.F. Crawford S.E. Estes M.K. Prasad B.V.V. Novel fold of rotavirus glycan-binding domain predicted by AlphaFold2 and determined by X-ray crystallography. Commun. Biol. 2022 5 1 419 10.1038/s42003‑022‑03357‑1 35513489
    [Google Scholar]
  81. Paul B. Weeratunga S. Tillu V.A. Hariri H. Henne W.M. Collins B.M. Structural predictions of the SNX-RGS proteins suggest they belong to a new class of lipid transfer proteins. Front. Cell Dev. Biol. 2022 10 826688 10.3389/fcell.2022.826688 35223850
    [Google Scholar]
  82. Nagaratnam N. Martin-Garcia J.M. Yang J.H. Goode M.R. Ketawala G. Craciunescu F.M. Zook J.D. Sonowal M. Williams D. Grant T.D. Fromme R. Hansen D.T. Fromme P. Structural and biophysical properties of FopA, a major outer membrane protein of Francisella tularensis. PLoS One 2022 17 8 0267370 10.1371/journal.pone.0267370 35913965
    [Google Scholar]
  83. Chang L. Wang F. Connolly K. Meng H. Su Z. Cvirkaite-Krupovic V. Krupovic M. Egelman E.H. Si D. DeepTracer-ID: De novo protein identification from cryo-EM maps. Biophys. J. 2022 121 15 2840 2848 10.1016/j.bpj.2022.06.025 35769006
    [Google Scholar]
  84. Tai L. Zhu Y. Ren H. Huang X. Zhang C. Sun F. 8 Å structure of the outer rings of the Xenopus laevis nuclear pore complex obtained by cryo-EM and AI. Protein Cell 2022 13 10 760 777 10.1007/s13238‑021‑00895‑y 35015240
    [Google Scholar]
  85. Liu H. Hong X. Xi J. Menne S. Hu J. Wang J.C.Y. Cryo-EM structures of human hepatitis B and woodchuck hepatitis virus small spherical subviral particles. Sci. Adv. 2022 8 31 eabo4184 10.1126/sciadv.abo4184 35930632
    [Google Scholar]
  86. Skalidis I. Kyrilis F.L. Tüting C. Hamdi F. Chojnowski G. Kastritis P.L. Cryo-EM and artificial intelligence visualize endogenous protein community members. Structure 2022 30 4 575 589.e6 10.1016/j.str.2022.01.001 35093201
    [Google Scholar]
  87. Jin Y. Fyfe P.K. Gardner S. Wilmes S. Bubeck D. Moraga I. Structural insights into the assembly and activation of the IL ‐27 signaling complex. EMBO Rep. 2022 23 10 55450 10.15252/embr.202255450 35920255
    [Google Scholar]
  88. Hutin S. Ling W.L. Tarbouriech N. Schoehn G. Grimm C. Fischer U. Burmeister W.P. The vaccinia virus DNA helicase structure from combined single-particle cryo-electron microscopy and alphafold2 prediction. Viruses 2022 14 10 2206 10.3390/v14102206 36298761
    [Google Scholar]
  89. Arantes P.R. Nierzwicki L. Belato H. D’Ordine A.M. Jogl G. Lisi G. Palermo G. Assessing structure and dynamics of AlphaFold2 prediction of GeoCas9. Biophys. J. 2022 121 3 45a 10.1016/j.bpj.2021.11.2474
    [Google Scholar]
  90. Tejero R. Huang Y.J. Ramelot T.A. Montelione G.T. AlphaFold models of small proteins rival the accuracy of solution NMR structures. Front. Mol. Biosci. 2022 9 877000 10.3389/fmolb.2022.877000 35769913
    [Google Scholar]
  91. Friesner R.A. Banks J.L. Murphy R.B. Halgren T.A. Klicic J.J. Mainz D.T. Repasky M.P. Knoll E.H. Shelley M. Perry J.K. Shaw D.E. Francis P. Shenkin P.S. Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 2004 47 7 1739 1749 10.1021/jm0306430 15027865
    [Google Scholar]
  92. Zhang Y. Vass M. Shi D. Abualrous E. Chambers J.M. Chopra N. Higgs C. Kasavajhala K. Li H. Nandekar P. Sato H. Miller E.B. Repasky M.P. Jerome S.V. Benchmarking refined and unrefined alphafold2 structures for hit discovery. J. Chem. Inf. Model. 2023 63 6 1656 1667 10.1021/acs.jcim.2c01219 36897766
    [Google Scholar]
  93. Yang Q. Xia D. Syed A.A.S. Wang Z. Shi Y. Highly accurate protein structure prediction and drug screen of monkeypox virus proteome. J. Infect. 2023 86 1 66 117 10.1016/j.jinf.2022.08.006 35964685
    [Google Scholar]
  94. Park H.M. Park Y. Vankerschaver J. Van Messem A. De Neve W. Shim H. Rethinking protein drug design with highly accurate structure prediction of Anti-CRISPR proteins. Pharmaceuticals 2022 15 3 310 10.3390/ph15030310 35337108
    [Google Scholar]
  95. Yang Q. Jian X. Syed A.A.S. Fahira A. Zheng C. Zhu Z. Wang K. Zhang J. Wen Y. Li Z. Pan D. Lu T. Wang Z. Shi Y. Structural comparison and drug screening of spike proteins of ten SARS-CoV-2 variants. Research 2022 2022 2022/9781758 10.34133/2022/9781758 35198984
    [Google Scholar]
  96. Liu F. Jiang X. Yang J. Tao J. Zhang M. A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia. Brief. Bioinform. 2022 23 5 bbac365 10.1093/bib/bbac365 36088545
    [Google Scholar]
  97. Liang X. Zhang H. Wang Z. Zhang X. Dai Z. Zhang J. Luo P. Zhang L. Hu J. Liu Z. Bi C. Cheng Q. JMJD8 is an M2 macrophage biomarker, and it associates with dna damage repair to facilitate stemness maintenance, chemoresistance, and immunosuppression in pan-cancer. Front. Immunol. 2022 13 875786 10.3389/fimmu.2022.875786 35898493
    [Google Scholar]
  98. Weng Y. Pan C. Shen Z. Chen S. Xu L. Dong X. Chen J. Identification of potential WSB1 inhibitors by alphafold modeling, virtual screening, and molecular dynamics simulation studies. Evid. Based Complement. Alternat. Med. 2022 2022 1 11 10.1155/2022/4629392 35600960
    [Google Scholar]
  99. Ren F. Ding X. Zheng M. Korzinkin M. Cai X. Zhu W. Mantsyzov A. Aliper A. Aladinskiy V. Cao Z. Kong S. Long X. Man Liu B.H. Liu Y. Naumov V. Shneyderman A. Ozerov I.V. Wang J. Pun F.W. Polykovskiy D.A. Sun C. Levitt M. Aspuru-Guzik A. Zhavoronkov A. AlphaFold accelerates artificial intelligence powered drug discovery: Efficient discovery of a novel CDK20 small molecule inhibitor. Chem. Sci. 2023 14 6 1443 1452 10.1039/D2SC05709C 36794205
    [Google Scholar]
  100. Jendrusch M. Korbel J.O. Kashif S. Sadiq S. AlphaDesign: A de novo protein design framework based on AlphaFold. bioRxiv 2021 2021.10.11.463937 10.1101/2021.10.11.463937
    [Google Scholar]
  101. Casadevall G. Duran C. Estévez-Gay M. Osuna S. Estimating conformational heterogeneity of tryptophan synthase with a template‐based Alphafold2 approach. Protein Sci. 2022 31 10 4426 10.1002/pro.4426 36173176
    [Google Scholar]
  102. Listov D. Lipsh-Sokolik R. Rosset S. Yang C. Correia B.E. Fleishman S.J. Assessing and enhancing foldability in designed proteins. Protein Sci. 2022 31 9 4400 10.1002/pro.4400 36040259
    [Google Scholar]
  103. Peñas-Utrilla D. Marcos E. Identifying well-folded de novo proteins in the new era of accurate structure prediction. Front. Mol. Biosci. 2022 9 991380 10.3389/fmolb.2022.991380 36275629
    [Google Scholar]
  104. Goverde C.A. Wolf B. Khakzad H. Rosset S. Correia B.E. De novo protein design by inversion of the AlphaFold structure prediction network. Protein Sci. 2023 32 6 4653 10.1002/pro.4653 37165539
    [Google Scholar]
  105. Wu M. Zhang Y. Combining bioinformatics, network pharmacology and artificial intelligence to predict the mechanism of celastrol in the treatment of type 2 diabetes. Front. Endocrinol. 2022 13 1030278 10.3389/fendo.2022.1030278 36339449
    [Google Scholar]
  106. Hegedűs T. Geisler M. Lukács G.L. Farkas B. Ins and outs of AlphaFold2 transmembrane protein structure predictions. Cell. Mol. Life Sci. 2022 79 1 73 10.1007/s00018‑021‑04112‑1 35034173
    [Google Scholar]
  107. Wu M. Zhang Y. Integrated bioinformatics, network pharmacology, and artificial intelligence to predict the mechanism of celastrol against muscle atrophy caused by colorectal cancer. Front. Genet. 2022 13 1012932 10.3389/fgene.2022.1012932 36419834
    [Google Scholar]
  108. Wang S. Lin H. Huang Z. He Y. Deng X. Xu Y. Pei J. Lai L. CavitySpace: A database of potential ligand binding sites in the human proteome. Biomolecules 2022 12 7 967 10.3390/biom12070967 35883523
    [Google Scholar]
  109. Ma W. Zhang S. Li Z. Jiang M. Wang S. Lu W. Bi X. Jiang H. Zhang H. Wei Z. Enhancing protein function prediction performance by utilizing alphafold-predicted protein structures. J. Chem. Inf. Model. 2022 62 17 4008 4017 10.1021/acs.jcim.2c00885 36006049
    [Google Scholar]
  110. Hu M. Yuan F. Yang K.K. Ju F. Su J. Wang H. Exploring evolution-aware and -free protein language models as protein function predictors. Adv. Neural Inf. Process. Syst. 2022 35 1 6
    [Google Scholar]
  111. Feng Y. Liu X.C. Li L. Gao S.Q. Wen G.B. Lin Y.W. Naturally occurring I81N mutation in human cytochrome c regulates both inherent peroxidase activity and interactions with neuroglobin. ACS Omega 2022 7 13 11510 11518 10.1021/acsomega.2c01256 35415373
    [Google Scholar]
  112. Dawson J.E. Smith I.N. Martin W. Khan K. Cheng F. Eng C. Shape shifting: The multiple conformational substates of the PTEN N‐terminal PIP 2 ‐binding domain. Protein Sci. 2022 31 5 4308 10.1002/pro.4308 35481646
    [Google Scholar]
  113. Bartas M. Slychko K. Brázda V. Červeň J. Beaudoin C.A. Blundell T.L. Pečinka P. Searching for New Z-DNA/Z-RNA binding proteins based on structural similarity to experimentally validated zα domain. Int. J. Mol. Sci. 2022 23 2 768 10.3390/ijms23020768 35054954
    [Google Scholar]
  114. Rappoport D. Jinich A. Enzyme substrate prediction from three-dimensional feature representations using space-filling curves. J. Chem. Inf. Model. 2023 63 5 1637 1648 10.1021/acs.jcim.3c00005 36802628
    [Google Scholar]
  115. Evans R. O’Neill M. Pritzel A. Antropova N. Senior A. Green T. Protein complex prediction with AlphaFold-Multimer. BioRxiv 2022 2021.10.04.463034 10.1101/2021.10.04.463034
    [Google Scholar]
  116. Ivanov Y.D. Taldaev A. Lisitsa A.V. Ponomarenko E.A. Archakov A.I. Prediction of monomeric and dimeric structures of cyp102a1 using alphafold2 and alphafold multimer and assessment of point mutation effect on the efficiency of intra- and interprotein electron transfer. Molecules 2022 27 4 1386 10.3390/molecules27041386 35209175
    [Google Scholar]
  117. Bryant P. Pozzati G. Elofsson A. Improved prediction of protein-protein interactions using AlphaFold2. Nat. Commun. 2022 13 1 1265 10.1038/s41467‑022‑28865‑w 35273146
    [Google Scholar]
  118. 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 10.1038/s41467‑021‑27838‑9 35013344
    [Google Scholar]
  119. Wong F. Krishnan A. Zheng E.J. Stärk H. Manson A.L. Earl A.M. Jaakkola T. Collins J.J. Benchmarking AlphaFold ‐enabled molecular docking predictions for antibiotic discovery. Mol. Syst. Biol. 2022 18 9 11081 10.15252/msb.202211081 36065847
    [Google Scholar]
  120. Kayastha K. Katsyv A. Himmrich C. Welsch S. Schuller J.M. Ermler U. Müller V. Structure-based electron-confurcation mechanism of the Ldh-EtfAB complex. eLife 2022 11 77095 10.7554/eLife.77095 35748623
    [Google Scholar]
  121. Kimura S. Srisuknimit V. McCarty K.L. Dedon P.C. Kranzusch P.J. Waldor M.K. Sequential action of a tRNA base editor in conversion of cytidine to pseudouridine. Nat. Commun. 2022 13 1 5994 10.1038/s41467‑022‑33714‑x 36220828
    [Google Scholar]
  122. Liang M. Chen X. Zhu C. Liang X. Gao Z. Luo S. Identification of a novel substrate motif of yeast separase and deciphering the recognition specificity using AlphaFold2 and molecular dynamics simulation. Biochem. Biophys. Res. Commun. 2022 620 173 179 10.1016/j.bbrc.2022.06.056 35803173
    [Google Scholar]
  123. McMullen P. Fang L. Qiao Q. Shao Q. Jiang S. Impacts of a zwitterionic peptide on its fusion protein. Bioconjug. Chem. 2022 33 8 1485 1493 10.1021/acs.bioconjchem.2c00176 35852436
    [Google Scholar]
  124. Lorenz P. Steinbeck F. Krause L. Thiesen H.J. The KRAB domain of ZNF10 guides the identification of specific amino acids that transform the ancestral KRAB-A-related domain present in human PRDM9 into a canonical modern KRAB-A domain. Int. J. Mol. Sci. 2022 23 3 1072 10.3390/ijms23031072 35162997
    [Google Scholar]
  125. Pinheiro F. Santos J. Ventura S. AlphaFold and the amyloid landscape. J. Mol. Biol. 2021 433 20 167059 10.1016/j.jmb.2021.167059 34023402
    [Google Scholar]
  126. Ries J.I. Heß M. Nouri N. Wichelhaus T.A. Göttig S. Falcone F.H. Kraiczy P. CipA mediates complement resistance of Acinetobacter baumannii by formation of a factor I-dependent quadripartite assemblage. Front. Immunol. 2022 13 942482 10.3389/fimmu.2022.942482 35958553
    [Google Scholar]
  127. Goulet A. Joos R. Lavelle K. Van Sinderen D. Mahony J. Cambillau C. A structural discovery journey of streptococcal phages adhesion devices by AlphaFold2. Front. Mol. Biosci. 2022 9 960325 10.3389/fmolb.2022.960325 36060267
    [Google Scholar]
  128. Goulet A. Mahony J. Cambillau C. van Sinderen D. Exploring structural diversity among adhesion devices encoded by lactococcal p335 phages with alphafold2. Microorganisms 2022 10 11 2278 10.3390/microorganisms10112278 36422348
    [Google Scholar]
  129. Pasquadibisceglie A. Leccese A. Polticelli F. A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2. Front Chem. 2022 10 1004815 10.3389/fchem.2022.1004815 36204150
    [Google Scholar]
  130. Tao H. Lauterbach L. Bian G. Chen R. Hou A. Mori T. Cheng S. Hu B. Lu L. Mu X. Li M. Adachi N. Kawasaki M. Moriya T. Senda T. Wang X. Deng Z. Abe I. Dickschat J.S. Liu T. Discovery of non-squalene triterpenes. Nature 2022 606 7913 414 419 10.1038/s41586‑022‑04773‑3 35650436
    [Google Scholar]
  131. Zheng L. Liu J. Niu L. Kamran M. Yang A.W.H. Jolma A. Dai Q. Hughes T.R. Patel D.J. Zhang L. Prasanth S.G. Yu Y. Ren A. Lai E.C. Distinct structural bases for sequence-specific DNA binding by mammalian BEN domain proteins. Genes Dev. 2022 36 3-4 225 240 10.1101/gad.348993.121 35144965
    [Google Scholar]
  132. Patel O. Brammananth R. Dai W. Panjikar S. Coppel R.L. Lucet I.S. Crellin P.K. Crystal structure of the putative cell-wall lipoglycan biosynthesis protein LmcA from Mycobacterium smegmatis. Acta Crystallogr. D Struct. Biol. 2022 78 4 494 508 10.1107/S2059798322001772 35362472
    [Google Scholar]
  133. Mehrtash A.B. Hochstrasser M. Elements of the ERAD ubiquitin ligase Doa10 regulating sequential poly-ubiquitylation of its targets. iScience 2022 25 11 105351 10.1016/j.isci.2022.105351 36325070
    [Google Scholar]
  134. Bentaleb C. Hervouet K. Montpellier C. Camuzet C. Ferrié M. Burlaud-Gaillard J. Bressanelli S. Metzger K. Werkmeister E. Ankavay M. Janampa N.L. Marlet J. Roux J. Deffaud C. Goffard A. Rouillé Y. Dubuisson J. Roingeard P. Aliouat-Denis C.M. Cocquerel L. The endocytic recycling compartment serves as a viral factory for hepatitis E virus. Cell. Mol. Life Sci. 2022 79 12 615 10.1007/s00018‑022‑04646‑y 36460928
    [Google Scholar]
  135. Taka J.R.H. Sun Y. Goldstone D.C. Mapping the interaction between Trim28 and the KRAB domain at the center of Trim28 silencing of endogenous retroviruses. Protein Sci. 2022 31 10 4436 10.1002/pro.4436 36173157
    [Google Scholar]
  136. Ding Y.W. Lu C.Z. Zheng Y. Ma H.Z. Jin J. Jia B. Yuan Y.J. Directed evolution of the fusion enzyme for improving astaxanthin biosynthesis in Saccharomyces cerevisiae. Synth. Syst. Biotechnol. 2023 8 1 46 53 10.1016/j.synbio.2022.10.005 36408203
    [Google Scholar]
  137. Zhorov B.S. Dong K. Pyrethroids in an alphafold2 model of the insect sodium channel. Insects 2022 13 8 745 10.3390/insects13080745 36005370
    [Google Scholar]
  138. Darai N. Mahalapbutr P. Wolschann P. Lee V.S. Wolfinger M.T. Rungrotmongkol T. Theoretical studies on RNA recognition by Musashi 1 RNA-binding protein. Sci. Rep. 2022 12 1 12137 10.1038/s41598‑022‑16252‑w 35840700
    [Google Scholar]
  139. Tang Q.Y. Ren W. Wang J. Kaneko K. The statistical trends of protein evolution: A lesson from alphafold database. Mol. Biol. Evol. 2022 39 10 msac197 10.1093/molbev/msac197 36108094
    [Google Scholar]
  140. Alvarez-Carreño C. Penev P.I. Petrov A.S. Williams L.D. Fold evolution before luca: Common ancestry of SH3 domains and OB domains. Mol. Biol. Evol. 2021 38 11 5134 5143 10.1093/molbev/msab240 34383917
    [Google Scholar]
  141. Kolesnik M.V. Fedorova I. Karneyeva K.A. Artamonova D.N. Severinov K.V. Type III CRISPR-cas systems: Deciphering the most complex prokaryotic immune system. Biochemistry 2021 86 10 1301 1314 10.1134/S0006297921100114 34903162
    [Google Scholar]
  142. Burnim A.A. Xu D. Spence M.A. Jackson C.J. Ando N. Analysis of insertions and extensions in the functional evolution of the ribonucleotide reductase family. Protein Sci. 2022 31 12 4483 10.1002/pro.4483 36307939
    [Google Scholar]
  143. Li V. Lee C. Yoo D. Cho S. Kim H. In silico SARS-CoV-2 vaccine development for Omicron strain using reverse vaccinology. Genes Genomics 2022 44 8 937 944 10.1007/s13258‑022‑01255‑8 35665465
    [Google Scholar]
  144. Molini B. Fernandez M.C. Godornes C. Vorobieva A. Lukehart S.A. Giacani L. B-cell epitope mapping of TprC and TprD variants of Treponema pallidum subspecies informs vaccine development for human treponematoses. Front. Immunol. 2022 13 862491 10.3389/fimmu.2022.862491 35422800
    [Google Scholar]
  145. Zeng D. Xin J. Yang K. Guo S. Wang Q. Gao Y. Chen H. Ge J. Lu Z. Zhang L. Chen J. Chen Y. Xia N. A hemagglutinin stem vaccine designed rationally by alphafold2 confers broad protection against influenza b infection. Viruses 2022 14 6 1305 10.3390/v14061305 35746776
    [Google Scholar]
  146. Guan W. Orellana K.G. Stephens R.F. Zhorov B.S. Spafford J.D. A lysine residue from an extracellular turret switches the ion preference in a Cav3 T-Type channel from calcium to sodium ions. J. Biol. Chem. 2022 298 12 102621 10.1016/j.jbc.2022.102621 36272643
    [Google Scholar]
  147. Pan Q. Nguyen T.B. Ascher D.B. Pires D.E.V. Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures. Brief. Bioinform. 2022 23 2 bbac025 10.1093/bib/bbac025 35189634
    [Google Scholar]
  148. Yang Q. Syed A.A.S. Fahira A. Shi Y. Structural analysis of the SARS-CoV-2 omicron variant proteins. Research 2021 2021 2021/9769586 10.34133/2021/9769586 35088054
    [Google Scholar]
  149. Zhu Y. Lu N. Chen J.Y. He C. Huang Z. Lu Z. Deep whole-genome resequencing sheds light on the distribution and effect of amphioxus SNPs. BMC Genom Data 2022 23 1 26 10.1186/s12863‑022‑01038‑w 35395709
    [Google Scholar]
  150. Iqbal S. Ge F. Li F. Akutsu T. Zheng Y. Gasser R.B. Yu D.J. Webb G.I. Song J. PROST: AlphaFold2-aware sequence-based predictor to estimate protein stability changes upon missense mutations. J. Chem. Inf. Model. 2022 62 17 4270 4282 10.1021/acs.jcim.2c00799 35973091
    [Google Scholar]
  151. Akdel M. Pires D.E.V. Pardo E.P. Jänes J. Zalevsky A.O. Mészáros B. Bryant P. Good L.L. Laskowski R.A. Pozzati G. Shenoy A. Zhu W. Kundrotas P. Serra V.R. Rodrigues C.H.M. Dunham A.S. Burke D. Borkakoti N. Velankar S. Frost A. Basquin J. Lindorff-Larsen K. Bateman A. Kajava A.V. Valencia A. Ovchinnikov S. Durairaj J. Ascher D.B. Thornton J.M. Davey N.E. Stein A. Elofsson A. Croll T.I. Beltrao P. A structural biology community assessment of AlphaFold2 applications. Nat. Struct. Mol. Biol. 2022 29 11 1056 1067 10.1038/s41594‑022‑00849‑w 36344848
    [Google Scholar]
  152. Burke D.F. Bryant P. Barrio-Hernandez I. Memon D. Pozzati G. Shenoy A. Zhu W. Dunham A.S. Albanese P. Keller A. Scheltema R.A. Bruce J.E. Leitner A. Kundrotas P. Beltrao P. Elofsson A. Towards a structurally resolved human protein interaction network. Nat. Struct. Mol. Biol. 2023 30 2 216 225 10.1038/s41594‑022‑00910‑8 36690744
    [Google Scholar]
  153. Jeppesen M. André I. Accurate prediction of protein assembly structure by combining AlphaFold and symmetrical docking. Nat. Commun. 2023 14 1 8283 10.1038/s41467‑023‑43681‑6 38092742
    [Google Scholar]
  154. Ghani U. Desta I. Jindal A. Khan O. Jones G. Hashemi N. Improved docking of protein models by a combination of alphafold2 and cluspro. BioRxiv 2022 2021.09.07.459290 10.1101/2021.09.07.459290
    [Google Scholar]
  155. Zhu W. Shenoy A. Kundrotas P. Elofsson A. Evaluation of AlphaFold-Multimer prediction on multi-chain protein complexes. Bioinformatics 2023 39 7 btad424 10.1093/bioinformatics/btad424 37405868
    [Google Scholar]
  156. van Kempen M. Kim S.S. Tumescheit C. Mirdita M. Lee J. Gilchrist C.L.M. Fast and accurate protein structure search with Foldseek. Nat. Biotechnol. 2023 2023 1 4 10.1038/s41587‑023‑01773‑0 37156916
    [Google Scholar]
  157. Liu J. Guo Z. Wu T. Roy R.S. Quadir F. Chen C. Cheng J. Enhancing alphafold-multimer-based protein complex structure prediction with MULTICOM in CASP15. Commun. Biol. 2023 6 1 1140 10.1038/s42003‑023‑05525‑3 37949999
    [Google Scholar]
  158. Bryant P. Noé F. Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile. PLOS Comput. Biol. 2024 20 7 1012253 10.1371/journal.pcbi.1012253 39052676
    [Google Scholar]
  159. Jones W.D. Dafou D. McEntagart M. Woollard W.J. Elmslie F.V. Holder-Espinasse M. Irving M. Saggar A.K. Smithson S. Trembath R.C. Deshpande C. Simpson M.A. De novo mutations in MLL cause Wiedemann-Steiner syndrome. Am. J. Hum. Genet. 2012 91 2 358 364 10.1016/j.ajhg.2012.06.008 22795537
    [Google Scholar]
  160. Reynisdottir T. Anderson K.J. Boukas L. Bjornsson H.T. Missense variants causing Wiedemann-Steiner syndrome preferentially occur in the KMT2A-CXXC domain and are accurately classified using AlphaFold2. PLoS Genet. 2022 18 6 1010278 10.1371/journal.pgen.1010278 35727845
    [Google Scholar]
  161. McBride J.M. Polev K. Abdirasulov A. Reinharz V. Grzybowski B.A. Tlusty T. Alphafold2 can predict single-mutation effects. Phys. Rev. Lett. 2023 131 21 218401 10.1103/PhysRevLett.131.218401 38072605
    [Google Scholar]
  162. Pak M.A. Markhieva K.A. Novikova M.S. Petrov D.S. Vorobyev I.S. Maksimova E.S. Kondrashov F.A. Ivankov D.N. Using AlphaFold to predict the impact of single mutations on protein stability and function. PLoS One 2023 18 3 0282689 10.1371/journal.pone.0282689 36928239
    [Google Scholar]
  163. Buel G.R. Walters K.J. Can AlphaFold2 predict the impact of missense mutations on structure? Nat. Struct. Mol. Biol. 2022 29 1 1 2 10.1038/s41594‑021‑00714‑2 35046575
    [Google Scholar]
  164. Cheng J. Novati G. Pan J. Bycroft C. Žemgulyte A. Applebaum T. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 1979 2023 381 10.1126/SCIENCE.ADG7492/SUPPL_FILE/SCIENCE.ADG7492_DATA_S1_TO_S9.ZIP 37733863
    [Google Scholar]
  165. Abramson J. Adler J. Dunger J. Evans R. Green T. Pritzel A. Ronneberger O. Willmore L. Ballard A.J. Bambrick J. Bodenstein S.W. Evans D.A. Hung C.C. O’Neill M. Reiman D. Tunyasuvunakool K. Wu Z. Žemgulytė A. Arvaniti E. Beattie C. Bertolli O. Bridgland A. Cherepanov A. Congreve M. Cowen-Rivers A.I. Cowie A. Figurnov M. Fuchs F.B. Gladman H. Jain R. Khan Y.A. Low C.M.R. Perlin K. Potapenko A. Savy P. Singh S. Stecula A. Thillaisundaram A. Tong C. Yakneen S. Zhong E.D. Zielinski M. Žídek A. Bapst V. Kohli P. Jaderberg M. Hassabis D. Jumper J.M. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 2024 630 8016 493 500 10.1038/s41586‑024‑07487‑w 38718835
    [Google Scholar]
  166. Burley S.K. Bhikadiya C. Bi C. Bittrich S. Chao H. Chen L. Craig P.A. Crichlow G.V. Dalenberg K. Duarte J.M. Dutta S. Fayazi M. Feng Z. Flatt J.W. Ganesan S. Ghosh S. Goodsell D.S. Green R.K. Guranovic V. Henry J. Hudson B.P. Khokhriakov I. Lawson C.L. Liang Y. Lowe R. Peisach E. Persikova I. Piehl D.W. Rose Y. Sali A. Segura J. Sekharan M. Shao C. Vallat B. Voigt M. Webb B. Westbrook J.D. Whetstone S. Young J.Y. Zalevsky A. Zardecki C. RCSB Protein Data Bank (RCSB.org): Delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning. Nucleic Acids Res. 2023 51 D1 D488 D508 10.1093/nar/gkac1077 36420884
    [Google Scholar]
  167. de Brevern A.G. Should we expect a second wave of AlphaFold misuse after the nobel prize? Bio Med. Informat 2024 4 4 2306 2308 10.3390/biomedinformatics4040124
    [Google Scholar]
  168. Thornton J.M. Laskowski R.A. Borkakoti N. AlphaFold heralds a data-driven revolution in biology and medicine. Nat. Med. 2021 27 10 1666 1669 10.1038/s41591‑021‑01533‑0 34642488
    [Google Scholar]
  169. Wang L. The geometric properties of the molecular surfaces of protein crystal structures and AlphaFold predicted models. BioRxiv 2025 2024.11.17.624000 10.1101/2024.11.17.624000
    [Google Scholar]
  170. He X. You C. Jiang H. Jiang Y. Xu H.E. Cheng X. AlphaFold2 versus experimental structures: Evaluation on G protein-coupled receptors. Acta Pharmacol. Sin. 2023 44 1 1 7 10.1038/s41401‑022‑00938‑y
    [Google Scholar]
  171. Chakravarty D. Porter L.L. AlphaFold2 fails to predict protein fold switching. Protein Sci. 2022 31 6 4353 10.1002/pro.4353 35634782
    [Google Scholar]
  172. Yang X. Wang X. Xu Z. Wu C. Zhou Y. Wang Y. Lin G. Li K. Wu M. Xia A. Liu J. Cheng L. Zou J. Yan W. Shao Z. Yang S. Molecular mechanism of allosteric modulation for the cannabinoid receptor CB1. Nat. Chem. Biol. 2022 18 8 831 840 10.1038/s41589‑022‑01038‑y 35637350
    [Google Scholar]
  173. Tikhonov D. Kulikova L. Rudnev V. Kopylov A.T. Taldaev A. Stepanov A. Malsagova K. Izotov A. Enikeev D. Potoldykova N. Kaysheva A. Changes in protein structural motifs upon post‐translational modification in kidney cancer. Diagnostics 2021 11 10 1836 10.3390/diagnostics11101836 34679534
    [Google Scholar]
  174. Bowman G.R. AlphaFold and protein folding: Not dead yet! the frontier is conformational ensembles. Annu. Rev. Biomed. Data Sci. 2024 7 1 51 57 10.1146/annurev‑biodatasci‑102423‑011435 38603560
    [Google Scholar]
  175. Woolfson D.N. Colwell L.J. Chen Z. Vorobieva A.A. Polizzi N.F. Stein A. Liu H. Parmeggiani F. Peacock A. Singh R. King N. Zitnik M. Chica R.A. How do you anticipate computational protein design will change biotechnology and therapeutic development? Cell Syst. 2024 15 11 994 999 10.1016/j.cels.2024.10.012 39571532
    [Google Scholar]
  176. Chowdhury R. Bouatta N. Biswas S. Floristean C. Kharkar A. Roy K. Rochereau C. Ahdritz G. Zhang J. Church G.M. Sorger P.K. AlQuraishi M. Single-sequence protein structure prediction using a language model and deep learning. Nat. Biotechnol. 2022 40 11 1617 1623 10.1038/s41587‑022‑01432‑w 36192636
    [Google Scholar]
  177. 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. Science 2023 379 6637 1123 1130 10.1126/science.ade2574 36927031
    [Google Scholar]
  178. The nobel prize in chemistry. 2020 Available from: https://www.nobelprize.org/prizes/chemistry/
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From CASP13 to the Nobel Prize: DeepMind’s AlphaFold Journey in Revolutionizing Protein Structure Prediction and Beyond
Bentham Science Publishers ; https://doi.org/10.2174/0113892037374986250711152300
/content/journals/cpps/10.2174/0113892037374986250711152300
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  • Article Type:     Review Article
Keywords: protein structure prediction ; multi-chain protein complex structures ; AlphaFold ; DeepMind ; protein science ; CASP ; AlphaFold2 algorithms

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