1932
0

Annual Review of Genomics and Human Genetics

Volume 25, 2024

Integrating Large-Scale Protein Structure Prediction into Human Genetics Research

  • Miguel Correa Marrero1,2, Jürgen Jänes1,2, Delora Baptista3 and Pedro Beltrao1,2,3
  • 1Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland; email: [email protected] 2SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland 3Instituto Gulbenkian de Ciência, Oeiras, Portugal
  • Vol. 25:123-140 (Volume publication date August 2024)
  • First published as a Review in Advance on April 15, 2024
  • Copyright © 2024 by the author(s).
    This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See credit lines of images or other third-party material in this article for license information.

Abstract

The last five years have seen impressive progress in deep learning models applied to protein research. Most notably, sequence-based structure predictions have seen transformative gains in the form of AlphaFold2 and related approaches. Millions of missense protein variants in the human population lack annotations, and these computational methods are a valuable means to prioritize variants for further analysis. Here, we review the recent progress in deep learning models applied to the prediction of protein structure and protein variants, with particular emphasis on their implications for human genetics and health. Improved prediction of protein structures facilitates annotations of the impact of variants on protein stability, protein–protein interaction interfaces, and small-molecule binding pockets. Moreover, it contributes to the study of host–pathogen interactions and the characterization of protein function. As genome sequencing in large cohorts becomes increasingly prevalent, we believe that better integration of state-of-the-art protein informatics technologies into human genetics research is of paramount importance.

Keyword(s): human disease, protein structure, protein structure prediction, variant effect prediction
Loading

Article metrics loading...

/content/journals/10.1146/annurev-genom-120622-020615
2024-08-27
2025-06-23
Loading full text...

Full text loading...

/deliver/fulltext/genom/25/1/annurev-genom-120622-020615.html?itemId=/content/journals/10.1146/annurev-genom-120622-020615&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Abdellaoui A, Yengo L, Verweij KJH, Visscher PM. 2023.. 15 years of GWAS discovery: realizing the promise. . Am. J. Hum. Genet. 110:(2):179–94
     [Crossref] [Google Scholar]
  2. 2.
    Adzhubei I, Jordan DM, Sunyaev SR. 2013.. Predicting functional effect of human missense mutations using PolyPhen-2. . Curr. Protoc. Hum. Genet. 76:(1):7.20.1–41
     [Google Scholar]
  3. 3.
    Ahdritz G, Bouatta N, Floristean C, Kadyan S, Xia Q, et al. 2023.. OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization. . bioRxiv 2022.11.20.517210. https://doi.org/10.1101/2022.11.20.517210
  4. 4.
    Akdel M, Pires DEV, Pardo EP, Jänes J, Zalevsky AO, et al. 2022.. A structural biology community assessment of AlphaFold2 applications. . Nat. Struct. Mol. Biol. 29:(11):1056–67
     [Crossref] [Google Scholar]
  5. 5.
    Albert FW, Kruglyak L. 2015.. The role of regulatory variation in complex traits and disease. . Nat. Rev. Genet. 16:(4):197–212
     [Crossref] [Google Scholar]
  6. 6.
    Alford RF, Leaver-Fay A, Jeliazkov JR, O'Meara MJ, DiMaio FP, et al. 2017.. The Rosetta All-Atom energy function for macromolecular modeling and design. . J. Chem. Theory Comput. 13:(6):3031–48. Correction . 2022.. J. Chem. Theory Comput. 18:(7):4594
     [Google Scholar]
  7. 7.
    Almeida A, Nayfach S, Boland M, Strozzi F, Beracochea M, et al. 2021.. A unified catalog of 204,938 reference genomes from the human gut microbiome. . Nat. Biotechnol. 39:(1):105–14
     [Crossref] [Google Scholar]
  8. 8.
    Aloy P, Russell RB. 2006.. Structural systems biology: modelling protein interactions. . Nat. Rev. Mol. Cell Biol. 7:(3):188–97
     [Crossref] [Google Scholar]
  9. 9.
    AlQuraishi M. 2021.. Machine learning in protein structure prediction. . Curr. Opin. Chem. Biol. 65::1–8
     [Crossref] [Google Scholar]
  10. 10.
    Anishchenko I, Ovchinnikov S, Kamisetty H, Baker D. 2017.. Origins of coevolution between residues distant in protein 3D structures. . PNAS 114:(34):9122–27
     [Crossref] [Google Scholar]
  11. 11.
    Backman JD, Li AH, Marcketta A, Sun D, Mbatchou J, et al. 2021.. Exome sequencing and analysis of 454,787 UK Biobank participants. . Nature 599:(7886):628–34
     [Crossref] [Google Scholar]
  12. 12.
    Barrio-Hernandez I, Yeo J, Jänes J, Mirdita M, Gilchrist CLM, et al. 2023.. Clustering predicted structures at the scale of the known protein universe. . Nature 622:(7983):637–45
     [Crossref] [Google Scholar]
  13. 13.
    Basu S, Wallner B. 2016.. DockQ: a quality measure for protein-protein docking models. . PLOS ONE 11:(8):e0161879
     [Crossref] [Google Scholar]
  14. 14.
    Beltrao P, Kiel C, Serrano L. 2007.. Structures in systems biology. . Curr. Opin. Struct. Biol. 17:(3):378–84
     [Crossref] [Google Scholar]
  15. 15.
    Blaabjerg LM, Kassem MM, Good LL, Jonsson N, Cagiada M, et al. 2023.. Rapid protein stability prediction using deep learning representations. . eLife 12::e82593
     [Crossref] [Google Scholar]
  16. 16.
    Bouatta N, Sorger P, AlQuraishi M. 2021.. Protein structure prediction by AlphaFold2: Are attention and symmetries all you need?. Acta Crystallogr. D 77:(8):982–91
     [Crossref] [Google Scholar]
  17. 17.
    Brandes N, Goldman G, Wang CH, Ye CJ, Ntranos V. 2023.. Genome-wide prediction of disease variant effects with a deep protein language model. . Nat. Genet. 55:(9):1512–22
     [Crossref] [Google Scholar]
  18. 18.
    Bryant P. 2023.. Structure prediction of alternative protein conformations. . bioRxiv 2023.09.25.559256. https://doi.org/10.1101/2023.09.25.559256
  19. 19.
    Bryant P, Pozzati G, Elofsson A. 2022.. Improved prediction of protein-protein interactions using AlphaFold2. . Nat. Commun. 13::1265
     [Crossref] [Google Scholar]
  20. 20.
    Bryant P, Pozzati G, Zhu W, Shenoy A, Kundrotas P, Elofsson A. 2022.. Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search. . Nat. Commun. 13::6028
     [Crossref] [Google Scholar]
  21. 21.
    Burke DF, Bryant P, Barrio-Hernandez I, Memon D, Pozzati G, et al. 2023.. Towards a structurally resolved human protein interaction network. . Nat. Struct. Mol. Biol. 30:(2):216–25
     [Crossref] [Google Scholar]
  22. 22.
    Butler BM, Kazan IC, Kumar A, Ozkan SB. 2018.. Coevolving residues inform protein dynamics profiles and disease susceptibility of nSNVs. . PLOS Comput. Biol. 14:(11):e1006626
     [Crossref] [Google Scholar]
  23. 23.
    Cheng J, Novati G, Pan J, Bycroft C, Žemgulytė A, et al. 2023.. Accurate proteome-wide missense variant effect prediction with AlphaMissense. . Science 381:(6664):eadg7492
     [Crossref] [Google Scholar]
  24. 24.
    Chowdhury R, Bouatta N, Biswas S, Floristean C, Kharkar A, et al. 2022.. Single-sequence protein structure prediction using a language model and deep learning. . Nat. Biotechnol. 40:(11):1617–23
     [Crossref] [Google Scholar]
  25. 25.
    Corso G, Stärk H, Jing B, Barzilay R, Jaakkola T. 2022.. DiffDock: diffusion steps, twists, and turns for molecular docking. . arXiv:2210.01776 [q-bio.BM]
  26. 26.
    Dayhoff MO, Schwartz RM, Orcutt BC. 1978.. A model of evolutionary change in proteins. . In Atlas of Protein Sequence and Structure, Vol. 5, Suppl. 3, ed. MO Dayhoff , pp. 345–52. Washington, DC:: Natl. Biomed. Res. Found.
     [Google Scholar]
  27. 27.
    Edwards AM, Isserlin R, Bader GD, Frye SV, Willson TM, Yu FH. 2011.. Too many roads not taken. . Nature 470:(7333):163–65
     [Crossref] [Google Scholar]
  28. 28.
    Evans R, O'Neill M, Pritzel A, Antropova N, Senior A, et al. 2022.. Protein complex prediction with AlphaFold-Multimer. . bioRxiv 2021.10.04.463034. https://doi.org/10.1101/2021.10.04.463034
  29. 29.
    Flower TG, Hurley JH. 2021.. Crystallographic molecular replacement using an in silico-generated search model of SARS-CoV-2 ORF8. . Protein Sci. 30:(4):728–34
     [Crossref] [Google Scholar]
  30. 30.
    Frazer J, Notin P, Dias M, Gomez A, Min JK, et al. 2021.. Disease variant prediction with deep generative models of evolutionary data. . Nature 599:(7883):91–95
     [Crossref] [Google Scholar]
  31. 31.
    Gerasimavicius L, Livesey BJ, Marsh JA. 2022.. Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure. . Nat. Commun. 13::3895
     [Crossref] [Google Scholar]
  32. 32.
    Gerasimavicius L, Livesey BJ, Marsh JA. 2023.. Correspondence between functional scores from deep mutational scans and predicted effects on protein stability. . Protein Sci. 32:(7):e4688
     [Crossref] [Google Scholar]
  33. 33.
    Gligorijević V, Renfrew PD, Kosciolek T, Leman JK, Berenberg D, et al. 2021.. Structure-based protein function prediction using graph convolutional networks. . Nat. Commun. 12::3168
     [Crossref] [Google Scholar]
  34. 34.
    Gupta M, Azumaya CM, Moritz M, Pourmal S, Diallo A, et al. 2021.. CryoEM and AI reveal a structure of SARS-CoV-2 Nsp2, a multifunctional protein involved in key host processes. . bioRxiv 2021.05.10.443524. https://doi.org/10.1101/2021.05.10.443524
  35. 35.
    Hao A, Suo Y, Lee S-Y. 2024.. Structural insights into the FtsEX-EnvC complex regulation on septal peptidoglycan hydrolysis in Vibrio cholerae. . Structure 32:(2):188–99
     [Crossref] [Google Scholar]
  36. 36.
    Haynes WA, Tomczak A, Khatri P. 2018.. Gene annotation bias impedes biomedical research. . Sci. Rep. 8::1362
     [Crossref] [Google Scholar]
  37. 37.
    Henikoff S, Henikoff JG. 1992.. Amino acid substitution matrices from protein blocks. . PNAS 89:(22):10915–19
     [Crossref] [Google Scholar]
  38. 38.
    Holcomb M, Chang Y-T, Goodsell DS, Forli S. 2023.. Evaluation of AlphaFold2 structures as docking targets. . Protein Sci. 32:(1):e4530
     [Crossref] [Google Scholar]
  39. 39.
    Homma F, Huang J, van der Hoorn RAL. 2023.. AlphaFold-Multimer predicts cross-kingdom interactions at the plant-pathogen interface. . Nat. Commun. 14::6040
     [Crossref] [Google Scholar]
  40. 40.
    Hopf TA, Ingraham JB, Poelwijk FJ, Schärfe CPI, Springer M, et al. 2017.. Mutation effects predicted from sequence co-variation. . Nat. Biotechnol. 35:(2):128–35
     [Crossref] [Google Scholar]
  41. 41.
    Ibrahim T, Khandare V, Mirkin FG, Tumtas Y, Bubeck D, Bozkurt TO. 2023.. AlphaFold2-multimer guided high-accuracy prediction of typical and atypical ATG8-binding motifs. . PLOS Biol. 21:(2):e3001962
     [Crossref] [Google Scholar]
  42. 42.
    Jakubec D, Skoda P, Krivak R, Novotny M, Hoksza D. 2022.. PrankWeb 3: accelerated ligand-binding site predictions for experimental and modelled protein structures. . Nucleic Acids Res. 50:(W1):W593–97
     [Crossref] [Google Scholar]
  43. 43.
    Jumper J, Evans R, Pritzel A, Green T, Figurnov M, et al. 2021.. Highly accurate protein structure prediction with AlphaFold. . Nature 596:(7873):583–89
     [Crossref] [Google Scholar]
  44. 44.
    Kannan SR, Sachdev S, Reddy AS, Kandasamy SL, Byrareddy SN, et al. 2022.. Mutations in the monkeypox virus replication complex: potential contributing factors to the 2022 outbreak. . J. Autoimmun. 133::102928
     [Crossref] [Google Scholar]
  45. 45.
    Karelina M, Noh JJ, Dror RO. 2023.. How accurately can one predict drug binding modes using AlphaFold models?. eLife 12::RP89386
     [Crossref] [Google Scholar]
  46. 46.
    Krishna R, Wang J, Ahern W, Sturmfels P, Venkatesh P, et al. 2023.. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. . bioRxiv 2023.10.09.561603. https://doi.org/10.1101/2023.10.09.561603
  47. 47.
    Książek M, Goulas T, Mizgalska D, Rodríguez-Banqueri A, Eckhard U, et al. 2023.. A unique network of attack, defence and competence on the outer membrane of the periodontitis pathogen Tannerella forsythia. . Chem. Sci. 14:(4):869–88
     [Crossref] [Google Scholar]
  48. 48.
    Kustatscher G, Collins T, Gingras A-C, Guo T, Hermjakob H, et al. 2022.. An open invitation to the Understudied Proteins Initiative. . Nat. Biotechnol. 40:(6):815–17
     [Crossref] [Google Scholar]
  49. 49.
    Lane TJ. 2023.. Protein structure prediction has reached the single-structure frontier. . Nat. Methods 20:(2):170–73
     [Crossref] [Google Scholar]
  50. 50.
    Lee CY, Hubrich D, Varga JK, Schäfer C, Welzel M, et al. 2023.. Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation. . bioRxiv 2023.08.07.552219. https://doi.org/10.1101/2023.08.07.552219
  51. 51.
    Lensink MF, Brysbaert G, Nadzirin N, Velankar S, Chaleil RAG, et al. 2019.. Blind prediction of homo- and hetero-protein complexes: the CASP13-CAPRI experiment. . Proteins 87:(12):1200–21
     [Crossref] [Google Scholar]
  52. 52.
    Li S, Xie T, Liu P, Wang L, Gong X. 2021.. Structural insights into the assembly and substrate selectivity of human SPT-ORMDL3 complex. . Nat. Struct. Mol. Biol. 28:(3):249–57
     [Crossref] [Google Scholar]
  53. 53.
    Lin Z, Akin H, Rao R, Hie B, Zhu Z, et al. 2023.. Evolutionary-scale prediction of atomic-level protein structure with a language model. . Science 379:(6637):1123–30
     [Crossref] [Google Scholar]
  54. 54.
    Livesey BJ, Marsh JA. 2023.. Updated benchmarking of variant effect predictors using deep mutational scanning. . Mol. Syst. Biol. 19:(8):e11474
     [Crossref] [Google Scholar]
  55. 55.
    Madera S, Kistler A, Ranaivoson HC, Ahyong V, Andrianiaina A, et al. 2022.. Discovery and genomic characterization of a novel henipavirus, Angavokely virus, from fruit bats in Madagascar. . J. Virol. 96:(18):e0092122
     [Crossref] [Google Scholar]
  56. 56.
    Maranga M, Szczerbiak P, Bezshapkin V, Gligorijevic V, Chandler C, et al. 2023.. Comprehensive functional annotation of metagenomes and microbial genomes using a deep learning-based method. . mSystems 8:(2):e0117822
     [Crossref] [Google Scholar]
  57. 57.
    Meller A, Bhakat S, Solieva S, Bowman GR. 2023.. Accelerating cryptic pocket discovery using AlphaFold. . J. Chem. Theory Comput. 19:(14):4355–63
     [Crossref] [Google Scholar]
  58. 58.
    Mihalič F, Benz C, Kassa E, Lindqvist R, Simonetti L, et al. 2023.. Identification of motif-based interactions between SARS-CoV-2 protein domains and human peptide ligands pinpoint antiviral targets. . Nat. Commun. 14::5636
     [Crossref] [Google Scholar]
  59. 59.
    Mosca R, Céol A, Aloy P. 2013.. Interactome3D: adding structural details to protein networks. . Nat. Methods 10:(1):47–53
     [Crossref] [Google Scholar]
  60. 60.
    Mukherjee S, Zhang Y. 2009.. MM-align: a quick algorithm for aligning multiple-chain protein complex structures using iterative dynamic programming. . Nucleic Acids Res. 37:(11):e83
     [Crossref] [Google Scholar]
  61. 61.
    Murray D, Petrey D, Honig B. 2021.. Integrating 3D structural information into systems biology. . J. Biol. Chem. 296::100562
     [Crossref] [Google Scholar]
  62. 62.
    Ng PC, Henikoff S. 2003.. SIFT: predicting amino acid changes that affect protein function. . Nucleic Acids Res. 31:(13):3812–14
     [Crossref] [Google Scholar]
  63. 63.
    Oprea TI, Bologa CG, Brunak S, Campbell A, Gan GN, et al. 2018.. Unexplored therapeutic opportunities in the human genome. . Nat. Rev. Drug Discov. 17:(5):317–32
     [Crossref] [Google Scholar]
  64. 64.
    Oughtred R, Rust J, Chang C, Breitkreutz B-J, Stark C, et al. 2021.. The BioGRID database: a comprehensive biomedical resource of curated protein, genetic, and chemical interactions. . Protein Sci. 30:(1):187–200
     [Crossref] [Google Scholar]
  65. 65.
    Ozden B, Kryshtafovych A, Karaca E. 2023.. The impact of AI-based modeling on the accuracy of protein assembly prediction: insights from CASP15. . Proteins 91:(12):1636–57
     [Crossref] [Google Scholar]
  66. 66.
    Perdigão N, Heinrich J, Stolte C, Sabir KS, Buckley MJ, et al. 2015.. Unexpected features of the dark proteome. . PNAS 112:(52):15898–903
     [Crossref] [Google Scholar]
  67. 67.
    Porta-Pardo E, Ruiz-Serra V, Valentini S, Valencia A. 2022.. The structural coverage of the human proteome before and after AlphaFold. . PLOS Comput. Biol. 18:(1):e1009818
     [Crossref] [Google Scholar]
  68. 68.
    Porter KA, Israel D, Kozakov D, Vajda S. 2019.. What method to use for protein-protein docking?. Curr. Opin. Struct. Biol. 55::1–7
     [Crossref] [Google Scholar]
  69. 69.
    Richards AL, Eckhardt M, Krogan NJ. 2021.. Mass spectrometry-based protein-protein interaction networks for the study of human diseases. . Mol. Syst. Biol. 17:(1):e8792
     [Crossref] [Google Scholar]
  70. 70.
    Rocha JJ, Jayaram SA, Stevens TJ, Muschalik N, Shah RD, et al. 2023.. Functional unknomics: systematic screening of conserved genes of unknown function. . PLOS Biol. 21:(8):e3002222
     [Crossref] [Google Scholar]
  71. 71.
    Rodríguez-López M, Bordin N, Lees J, Scholes H, Hassan S, et al. 2023.. Broad functional profiling of fission yeast proteins using phenomics and machine learning. . eLife 12::RP88229
     [Crossref] [Google Scholar]
  72. 72.
    Ruff KM, Pappu RV. 2021.. AlphaFold and implications for intrinsically disordered proteins. . J. Mol. Biol. 433:(20):167208
     [Crossref] [Google Scholar]
  73. 73.
    Sala D, Engelberger F, Mchaourab HS, Meiler J. 2023.. Modeling conformational states of proteins with AlphaFold. . Curr. Opin. Struct. Biol. 81::102645
     [Crossref] [Google Scholar]
  74. 74.
    Schad E, Tompa P, Hegyi H. 2011.. The relationship between proteome size, structural disorder and organism complexity. . Genome Biol. 12:(12):R120
     [Crossref] [Google Scholar]
  75. 75.
    Schymkowitz J, Borg J, Stricher F, Nys R, Rousseau F, Serrano L. 2005.. The FoldX web server: an online force field. . Nucleic Acids Res. 33:(Suppl. 2):W382–88
     [Crossref] [Google Scholar]
  76. 76.
    Sekhar A, Kay LE. 2019.. An NMR view of protein dynamics in health and disease. . Annu. Rev. Biophys. 48::297–319
     [Crossref] [Google Scholar]
  77. 77.
    Seong K, Krasileva KV. 2023.. Prediction of effector protein structures from fungal phytopathogens enables evolutionary analyses. . Nat. Microbiol. 8:(1):174–87
     [Crossref] [Google Scholar]
  78. 78.
    Sim J, Kwon S, Seok C. 2022.. HProteome-BSite: predicted binding sites and ligands in human 3D proteome. . Nucleic Acids Res. 51:(D1):D403–8
     [Crossref] [Google Scholar]
  79. 79.
    Simonovsky M, Meyers J. 2020.. DeeplyTough: learning structural comparison of protein binding sites. . J. Chem. Inf. Model. 60:(4):2356–66
     [Crossref] [Google Scholar]
  80. 80.
    Sinha S, Eisenhaber B, Jensen LJ, Kalbuaji B, Eisenhaber F. 2018.. Darkness in the human gene and protein function space: widely modest or absent illumination by the life science literature and the trend for fewer protein function discoveries since 2000. . Proteomics 18:(21–22):e1800093
     [Crossref] [Google Scholar]
  81. 81.
    Smith LM, Kelleher NL. 2018.. Proteoforms as the next proteomics currency. . Science 359:(6380):1106–7
     [Crossref] [Google Scholar]
  82. 82.
    Sollis E, Mosaku A, Abid A, Buniello A, Cerezo M, et al. 2023.. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. . Nucleic Acids Res. 51:(D1):D977–85
     [Crossref] [Google Scholar]
  83. 83.
    Stoeger T, Gerlach M, Morimoto RI, Nunes Amaral LA. 2018.. Large-scale investigation of the reasons why potentially important genes are ignored. . PLOS Biol. 16:(9):e2006643
     [Crossref] [Google Scholar]
  84. 84.
    Stoeger T, Nunes Amaral LA. 2020.. COVID-19 research risks ignoring important host genes due to pre-established research patterns. . eLife 9::e61981
     [Crossref] [Google Scholar]
  85. 85.
    Superti-Furga A, Unger S. 2011.. CHST3-related skeletal dysplasia. . In GeneReviews, ed. MP Adam, HH Ardinger, RA Pagon, SE Wallace, LJH Bean, et al . Seattle:: Univ. Wash. https://www.ncbi.nlm.nih.gov/books/NBK62112/
    [Google Scholar]
  86. 86.
    Teulet A, Quan C, Evangelisti E, Wanke A, Yang W, Schornack S. 2023.. A pathogen effector FOLD diversified in symbiotic fungi. . New Phytol. 239:(3):1127–39
     [Crossref] [Google Scholar]
  87. 87.
    Tsang B, Pritišanac I, Scherer SW, Moses AM, Forman-Kay JD. 2020.. Phase separation as a missing mechanism for interpretation of disease mutations. . Cell 183:(7):1742–56
     [Crossref] [Google Scholar]
  88. 88.
    Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, et al. 2018.. SWISS-MODEL: homology modelling of protein structures and complexes. . Nucleic Acids Res. 46:(W1):W296–303
     [Crossref] [Google Scholar]
  89. 89.
    Wayment-Steele HK, Ojoawo A, Otten R, Apitz JM, Pitsawong W, et al. 2024.. Predicting multiple conformations via sequence clustering and AlphaFold2. . Nature 625:(7996):832–39
     [Crossref] [Google Scholar]
  90. 90.
    Weaver GC, Arya R, Schneider CL, Hudson AW, Stern LJ. 2022.. Structural models for roseolovirus U20 and U21: non-classical MHC-I like proteins from HHV-6A, HHV-6B, and HHV-7. . Front. Immunol. 13::864898
     [Crossref] [Google Scholar]
  91. 91.
    Wong F, Krishnan A, Zheng EJ, Stärk H, Manson AL, et al. 2022.. Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery. . Mol. Syst. Biol. 18:(9):e11081
     [Crossref] [Google Scholar]
  92. 92.
    Wood V, Lock A, Harris MA, Rutherford K, Bähler J, Oliver SG. 2019.. Hidden in plain sight: What remains to be discovered in the eukaryotic proteome?. Open Biol. 9:(2):180241
     [Crossref] [Google Scholar]
  93. 93.
    Yang Z, Wang Y, Jin Y, Zhu Y, Wu Y, et al. 2021.. A non-ACE2 competing human single-domain antibody confers broad neutralization against SARS-CoV-2 and circulating variants. . Signal. Transduct. Target Ther. 6::378
     [Crossref] [Google Scholar]
  94. 94.
    Yin R, Feng BY, Varshney A, Pierce BG. 2022.. Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants. . Protein Sci. 31:(8):e4379
     [Crossref] [Google Scholar]
  95. 95.
    Yin R, Pierce BG. 2024.. Evaluation of AlphaFold antibody–antigen modeling with implications for improving predictive accuracy. . Protein Sci. 33:(1):e4865
     [Crossref] [Google Scholar]
  96. 96.
    Yu DS, Outram MA, Smith A, McCombe CL, Khambalkar PB, et al. 2023.. The structural repertoire of Fusarium oxysporum f. sp. lycopersici effectors revealed by experimental and computational studies. . eLife 12::RP89280
     [Crossref] [Google Scholar]
  97. 97.
    Zarin T, Strome B, Nguyen Ba AN, Alberti S, Forman-Kay JD, Moses AM. 2019.. Proteome-wide signatures of function in highly diverged intrinsically disordered regions. . eLife 8::e46883
     [Crossref] [Google Scholar]
  98. 98.
    Zhang T, Tamman H, Coppieters't Wallant K, Kurata T, LeRoux M, et al. 2022.. Direct activation of a bacterial innate immune system by a viral capsid protein. . Nature 612:(7938):132–40
     [Crossref] [Google Scholar]
  99. 99.
    Zhang Z, Chen L, Zhong F, Wang D, Jiang J, et al. 2022.. Graph neural network approaches for drug-target interactions. . Curr. Opin. Struct. Biol. 73::102327
     [Crossref] [Google Scholar]
  100. 100.
    Zheng S, He J, Liu C, Shi Y, Lu Z, et al. 2023.. Towards predicting equilibrium distributions for molecular systems with deep learning. . arXiv:2306.05445 [physics.chem-ph]
/content/journals/10.1146/annurev-genom-120622-020615
Loading
Integrating Large-Scale Protein Structure Prediction into Human Genetics Research
Annual Review of Genomics and Human Genetics 25, 123 (2024); https://doi.org/10.1146/annurev-genom-120622-020615
/content/journals/10.1146/annurev-genom-120622-020615
/content/journals/10.1146/annurev-genom-120622-020615
Loading

Data & Media loading...

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error