Treffer: Generative prediction of real-world prevalent SARS-CoV-2 mutation with in silico virus evolution.
Title:
Generative prediction of real-world prevalent SARS-CoV-2 mutation with in silico virus evolution.
Authors:
Liu X; School of Electronic and Computer Engineering, Peking University, Shenzhen, China., Nie Z; School of Electronic and Computer Engineering, Peking University, Shenzhen, China.; Pengcheng Laboratory, Shenzhen, China., Si H; Guangzhou Medical University, Guangzhou, China.; State Key Laboratory of Respiratory Disease, Guangzhou, China.; Guangzhou National Laboratory, Guangzhou, China., Shen X; Guangzhou National Laboratory, Guangzhou, China., Liu Y; Pengcheng Laboratory, Shenzhen, China.; School of Computer Science, Peking University, Beijing, China., Huang X; Pengcheng Laboratory, Shenzhen, China., Dong T; Guangzhou National Laboratory, Guangzhou, China.; State Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, China.; University of Chinese Academy of Sciences, Beijing, China., Xu F; Pengcheng Laboratory, Shenzhen, China., Ren Z; Pengcheng Laboratory, Shenzhen, China., Zhou P; Guangzhou Medical University, Guangzhou, China.; Guangzhou National Laboratory, Guangzhou, China., Chen J; School of Electronic and Computer Engineering, Peking University, Shenzhen, China.; Pengcheng Laboratory, Shenzhen, China.
Source:
Briefings in bioinformatics [Brief Bioinform] 2025 May 01; Vol. 26 (3).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN: 1477-4054 (Electronic) Linking ISSN: 14675463 NLM ISO Abbreviation: Brief Bioinform Subsets: MEDLINE
Imprint Name(s):
Publication: Oxford : Oxford University Press
Original Publication: London ; Birmingham, AL : H. Stewart Publications, [2000-
Original Publication: London ; Birmingham, AL : H. Stewart Publications, [2000-
MeSH Terms:
References:
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Nature. 2022 Apr;604(7906):546-552. (PMID: 35228716)
J Chem Inf Model. 2021 Sep 27;61(9):4687-4700. (PMID: 34468141)
Nat Commun. 2023 Jun 13;14(1):3478. (PMID: 37311849)
Nature. 2020 Mar;579(7798):270-273. (PMID: 32015507)
Nat Microbiol. 2023 Nov;8(11):1952-1959. (PMID: 37845314)
Bioinform Adv. 2023 Jan 02;3(1):vbac103. (PMID: 36698760)
Viruses. 2022 Mar 19;14(3):. (PMID: 35337047)
Nature. 2022 Feb;602(7897):487-495. (PMID: 34942634)
Science. 2023 Mar 17;379(6637):1123-1130. (PMID: 36927031)
Science. 2021 Jan 15;371(6526):284-288. (PMID: 33446556)
Science. 2020 Aug 14;369(6505):846-849. (PMID: 32576668)
Nature. 2023 Feb;614(7948):521-529. (PMID: 36535326)
Elife. 2019 Apr 30;8:. (PMID: 31038123)
Nat Med. 2022 Jul;28(7):1501-1508. (PMID: 35725921)
Science. 2020 May 8;368(6491):. (PMID: 32234805)
Cell. 2022 Feb 3;185(3):467-484.e15. (PMID: 35081335)
Nature. 2022 Mar;603(7902):706-714. (PMID: 35104837)
Nat Rev Microbiol. 2008 Jun;6(6):477-87. (PMID: 18533288)
Cell Syst. 2022 Apr 20;13(4):274-285.e6. (PMID: 35120643)
PLoS Pathog. 2022 Nov 18;18(11):e1010951. (PMID: 36399443)
Nature. 2023 Oct;622(7984):818-825. (PMID: 37821700)
Lancet. 2020 Nov 21;396(10263):1614-1616. (PMID: 33159850)
Trends Biotechnol. 2011 Sep;29(9):435-42. (PMID: 21561674)
Elife. 2023 Feb 21;12:. (PMID: 36803543)
Proc Natl Acad Sci U S A. 2021 Oct 19;118(42):. (PMID: 34588290)
Nature. 2021 Sep;597(7874):97-102. (PMID: 34261126)
Nat Biotechnol. 2022 Nov;40(11):1617-1623. (PMID: 36192636)
Trends Immunol. 2020 Dec;41(12):1100-1115. (PMID: 33132005)
Elife. 2018 Mar 28;7:. (PMID: 29590010)
J Virol. 2019 Nov 13;93(23):. (PMID: 31511387)
Lancet Infect Dis. 2022 Oct;22(10):1422-1423. (PMID: 36084664)
Immunogenetics. 2002 Nov;54(8):527-42. (PMID: 12439615)
Cell Rep. 2023 Oct 31;42(10):113356. (PMID: 37851571)
N Engl J Med. 2023 Jan 5;388(1):89-91. (PMID: 36476720)
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Nat Rev Microbiol. 2023 Mar;21(3):178-194. (PMID: 36631691)
Cell. 2020 Sep 3;182(5):1295-1310.e20. (PMID: 32841599)
Genome Res. 2004 Jun;14(6):1188-90. (PMID: 15173120)
Sci Rep. 2014 May 13;4:4942. (PMID: 24820965)
IEEE Trans Pattern Anal Mach Intell. 2022 Oct;44(10):7112-7127. (PMID: 34232869)
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Annu Rev Virol. 2015 Nov;2(1):161-79. (PMID: 26958911)
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Cell. 2020 Aug 20;182(4):794-795. (PMID: 32697970)
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Signal Transduct Target Ther. 2022 Jan 5;7(1):8. (PMID: 34987150)
Nat Commun. 2021 Jul 7;12(1):4196. (PMID: 34234131)
Signal Transduct Target Ther. 2022 Apr 28;7(1):141. (PMID: 35484110)
Nat Genet. 2023 Sep;55(9):1512-1522. (PMID: 37563329)
BMC Genomics. 2022 Feb 11;23(1):121. (PMID: 35148677)
Grant Information:
B2302037 Shenzhen Medical Research Funds in China; 61972217 Natural Science Foundation of China; 32071459 Natural Science Foundation of China; 62176249 Natural Science Foundation of China; 62006133 Natural Science Foundation of China; 62271465 Natural Science Foundation of China; SRPG22-001 Self-Supporting Program of Guangzhou Laboratory; QNPG23-07 Young Scientists Program of Guangzhou Laboratory; 2024B0101010003 Guangdong S&T Program; AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, China
Contributed Indexing:
Keywords: generative deep learning; in silico virus evolution; mutation prediction; protein language model
Entry Date(s):
Date Created: 20250618 Date Completed: 20250618 Latest Revision: 20250629
Update Code:
20250630
PubMed Central ID:
PMC12204194
DOI:
10.1093/bib/bbaf276
PMID:
40532108
Database:
MEDLINE
Weitere Informationen
Predicting the mutation prevalence trends of emerging viruses in the real world is an efficient means to update vaccines or drugs in advance. It is crucial to develop a computational method for the prediction of real-world prevalent SARS-CoV-2 mutations considering the impact of multiple selective pressures within and between hosts. Here, a deep-learning generative framework for real-world prevalent SARS-CoV-2 mutation prediction, named ViralForesight, is developed on top of protein language models and in silico virus evolution. Through the paradigm of host-to-herd in silico virus evolution, ViralForesight reproduced previous real-world prevalent SARS-CoV-2 mutations for multiple lineages with superior performance. More importantly, ViralForesight correctly predicted the future prevalent mutations that dominated the COVID-19 pandemic in the real world more than half a year in advance with in vitro experimental validation. Overall, ViralForesight demonstrates a proactive approach to the prevention of emerging viral infections, accelerating the process of discovering future prevalent mutations with the power of generative deep learning.
(© The Author(s) 2025. Published by Oxford University Press.)