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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Review Article

Structural Biology Meets Biomolecular Networks: The Post-AlphaFold Era

Author(s): Wenying Yan and Guang Hu*

Volume 17, Issue 6, 2022

Published on: 26 April, 2022

Page: [493 - 497] Pages: 5

DOI: 10.2174/1574893617666220211115211

Price: $65

Abstract

Background: Recent progress in protein structure prediction by AlphaFold has opened new avenues to decipher biological functions from the perspective of structural biology based on the proteomics level.

Methods: To meet these challenges, in this perspective, three scales of networks for protein structures, including structural protein-protein networks, protein structural networks, and elastic network models were introduced for high-throughput modeling of protein functional sites and protein dynamics.

Conclusion: In the post-AlphaFold era, it is assumed that the integration of biomolecular networks may be leveraged in the future to develop a modeling framework that addresses protein structure-based functions with the application in drug discovery.

Keywords: Structural systems biology, network biology, structural proteome, protein dynamics, functional sites, biomolecular networks.

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[1]
Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021; 596(7873): 583-9.
[http://dx.doi.org/10.1038/s41586-021-03819-2] [PMID: 34265844]
[2]
Tunyasuvunakool K, Adler J, Wu Z, et al. Highly accurate protein structure prediction for the human proteome. Nature 2021; 596(7873): 590-6.
[http://dx.doi.org/10.1038/s41586-021-03828-1] [PMID: 34293799]
[3]
McCafferty CL, Verbeke EJ, Marcotte EM, Taylor DW. Structural biology in the multi-omics era. J Chem Inf Model 2020; 60(5): 2424-9.
[http://dx.doi.org/10.1021/acs.jcim.9b01164] [PMID: 32129623]
[4]
Liu C, Ma YF, Zhao J, et al. Computational network biology: Data, models, and applications. Phys Rep 2020; 846: 1-66.
[http://dx.doi.org/10.1016/j.physrep.2019.12.004]
[5]
Yadav A, Vidal M, Luck K. Precision medicine - networks to the rescue. Curr Opin Biotechnol 2020; 63: 177-89.
[http://dx.doi.org/10.1016/j.copbio.2020.02.005] [PMID: 32199228]
[6]
Sun WM, Wang L, Peng JX, et al. A Cross-entropy-based method for essential protein identification in yeast protein-protein interaction network. Curr Bioinform 2021; 16: 565-75.
[http://dx.doi.org/10.2174/1574893615999201116210840]
[7]
Liu X, Hong Z, Liu J, et al. Computational methods for identifying the critical nodes in biological networks. Brief Bioinform 2020; 21(2): 486-97.
[http://dx.doi.org/10.1093/bib/bbz011] [PMID: 30753282]
[8]
Qian DC, Li Q, Zhu YS, Li DJ. Comprehensive analysis of key proteins involved in radioresistance of prostate cancer by integrating protein-protein interaction networks. Curr Bioinform 2021; 16: 139-45.
[http://dx.doi.org/10.2174/1574893615999200605143510]
[9]
Uversky VN, Giuliani A. Networks of networks: An essay on multi-level biological organization. Front Genet 2021; 12: 706260.
[http://dx.doi.org/10.3389/fgene.2021.706260] [PMID: 34234818]
[10]
Di Paola L, Giuliani A. Protein contact network topology: a natural language for allostery. Curr Opin Struct Biol 2015; 31: 43-8.
[http://dx.doi.org/10.1016/j.sbi.2015.03.001] [PMID: 25796032]
[11]
Aloy P, Russell RB. Structural systems biology: modelling protein interactions. Nat Rev Mol Cell Biol 2006; 7(3): 188-97.
[http://dx.doi.org/10.1038/nrm1859] [PMID: 16496021]
[12]
Guven-Maiorov E, Keskin O, Gursoy A, Nussinov R. A structural view of negative regulation of the toll-like receptor-mediated inflammatory pathway. Biophys J 2015; 109(6): 1214-26.
[http://dx.doi.org/10.1016/j.bpj.2015.06.048] [PMID: 26276688]
[13]
Brunk E, Mih N, Monk J, et al. Systems biology of the structural proteome. BMC Syst Biol 2016; 10: 26.
[http://dx.doi.org/10.1186/s12918-016-0271-6] [PMID: 26969117]
[14]
Yan W, Zhang D, Shen C, Liang Z, Hu G. Recent advances on the network models in target-based drug discovery. Curr Top Med Chem 2018; 18(13): 1031-43.
[http://dx.doi.org/10.2174/1568026618666180719152258] [PMID: 30027851]
[15]
Liang Z, Verkhivker GM, Hu G. Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications. Brief Bioinform 2020; 21(3): 815-35.
[http://dx.doi.org/10.1093/bib/bbz029] [PMID: 30911759]
[16]
Murray D, Petrey D, Honig B. Integrating 3D structural information into systems biology. J Biol Chem 2021; 296: 100562.
[http://dx.doi.org/10.1016/j.jbc.2021.100562] [PMID: 33744294]
[17]
Wang F, Han S, Yang J, Yan W, Hu G. Knowledge-guided “community network” analysis reveals the functional modules and candidate targets in non-small-cell lung cancer. Cells 2021; 10(2): 10.
[http://dx.doi.org/10.3390/cells10020402] [PMID: 33669233]
[18]
Vázquez M, Valencia A, Pons T. Structure-PPi: a module for the annotation of cancer-related single-nucleotide variants at protein-protein interfaces. Bioinformatics 2015; 31(14): 2397-9.
[http://dx.doi.org/10.1093/bioinformatics/btv142] [PMID: 25765346]
[19]
Lu HC, Herrera Braga J, Fraternali F. PinSnps: structural and functional analysis of SNPs in the context of protein interaction networks. Bioinformatics 2016; 32(16): 2534-6.
[http://dx.doi.org/10.1093/bioinformatics/btw153] [PMID: 27153707]
[20]
Yan WY, Hu G, Shen BR. Network analysis of protein structures: The comparison of three topologies. Curr Bioinform 2016; 11: 480-9.
[http://dx.doi.org/10.2174/1574893611666160602124707]
[21]
Gao XM, Ding YR. Using the residue interaction network improve the classification of thermophilic and mesophilic proteins. Curr Bioinform 2017; 12: 249-57.
[http://dx.doi.org/10.2174/1574893611666160502122132]
[22]
Kumari N, Verma A. Analysis of oncogene protein structure using small world network concept. Curr Bioinform 2020; 15: 732-40.
[http://dx.doi.org/10.2174/1574893614666191113143840]
[23]
Yan W, Hu G, Liang Z, et al. Node-weighted amino acid network strategy for characterization and identification of protein functional residues. J Chem Inf Model 2018; 58(9): 2024-32.
[http://dx.doi.org/10.1021/acs.jcim.8b00146] [PMID: 30107728]
[24]
Ponzoni L, Bahar I. Structural dynamics is a determinant of the functional significance of missense variants. Proc Natl Acad Sci USA 2018; 115(16): 4164-9.
[http://dx.doi.org/10.1073/pnas.1715896115] [PMID: 29610305]
[25]
Li H, Chang YY, Lee JY, Bahar I, Yang LW. DynOmics: dynamics of structural proteome and beyond. Nucleic Acids Res 2017; 45(W1): W374-80.
[http://dx.doi.org/10.1093/nar/gkx385] [PMID: 28472330]
[26]
Ruiz C, Zitnik M, Leskovec J. Identification of disease treatment mechanisms through the multiscale interactome. Nat Commun 2021; 12(1): 1796.
[http://dx.doi.org/10.1038/s41467-021-21770-8] [PMID: 33741907]
[27]
Baek M, DiMaio F, Anishchenko I, et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 2021; 373(6557): 871-6.
[http://dx.doi.org/10.1126/science.abj8754] [PMID: 34282049]
[28]
Jin S, Zeng X, Xia F, Huang W, Liu X. Application of deep learning methods in biological networks. Brief Bioinform 2021; 22(2): 1902-17.
[http://dx.doi.org/10.1093/bib/bbaa043] [PMID: 32363401]

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