Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies.

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作者： Cyril,Malbranke ^[1] ; David,Bikard ^[2] ; Simona,Cocco ^[3] ; Rémi,Monasson ^[3] ; Jérôme,Tubiana ^[4]

作者单位： Laboratory of Physics of the Ecole Normale Supérieure, PSL Research, CNRS UMR 8023, Sorbonne Université, Université de Paris, Paris, France; Institut Pasteur, Université Paris Cité, CNRS UMR 6047, Synthetic Biology, 75015 Paris, France. Electronic address: cyril.malbranke@phys.ens.fr. ^[1] Institut Pasteur, Université Paris Cité, CNRS UMR 6047, Synthetic Biology, 75015 Paris, France. ^[2] Laboratory of Physics of the Ecole Normale Supérieure, PSL Research, CNRS UMR 8023, Sorbonne Université, Université de Paris, Paris, France. ^[3] Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel. Electronic address: jertubiana@gmail.com. ^[4]

主题词蛋白质类(Proteins);物理学(Physics);数据库, 蛋白质(Databases, Protein)

DOI 10.1016/j.sbi.2023.102571

PMID 36947951

发布时间 2023-06-19

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Current opinion in structural biology

2023年80卷

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Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies.

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Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies.

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