Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation information.

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作者： Simona,Cocco ^[1] ; Lorenzo,Posani ^[1] ; Rémi,Monasson ^[1]

作者单位： Laboratory of Physics of the Ecole Normale Supérieure, CNRS UMR8023 and Paris Sciences & Lettres (PSL) Research, Sorbonne Université, 75005 Paris, France. ^[1]

关键词 fitness prediction functional networks inference of sparse graphical Potts models multiple sequence alignments mutational scans

主题词突变(Mutation);蛋白质类(Proteins);上位性, 遗传(Epistasis, Genetic);进化, 分子(Evolution, Molecular);计算生物学(Computational Biology)

DOI 10.1073/pnas.2312335121

PMID 38889151

发布时间 2025-01-04

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Proceedings of the National Academy of Sciences of the United States of America

2024年121卷26期

e2312335121页

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Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation information.

Proceedings of the National Academy of Sciences of the United States of America

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Proceedings of the National Academy of Sciences of the United States of America

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Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation information.

Proceedings of the National Academy of Sciences of the United States of America

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Proceedings of the National Academy of Sciences of the United States of America

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