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

Computational protein design facilitates the discovery of novel proteins with prescribed structure and functionality. Exciting designs were recently reported using novel data-driven methodologies that can be roughly divided into two categories: evolutionary-based and physics-inspired approaches. The...

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Vydáno v:Current opinion in structural biology Ročník 80; s. 102571
Hlavní autoři: Malbranke, Cyril, Bikard, David, Cocco, Simona, Monasson, Rémi, Tubiana, Jérôme
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Elsevier Ltd 01.06.2023
Elsevier
Témata:
Physics
ISSN:0959-440X, 1879-033X, 1879-033X
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Shrnutí:Computational protein design facilitates the discovery of novel proteins with prescribed structure and functionality. Exciting designs were recently reported using novel data-driven methodologies that can be roughly divided into two categories: evolutionary-based and physics-inspired approaches. The former infer characteristic sequence features shared by sets of evolutionary-related proteins, such as conserved or coevolving positions, and recombine them to generate candidates with similar structure and function. The latter approaches estimate key biochemical properties, such as structure free energy, conformational entropy, or binding affinities using machine learning surrogates, and optimize them to yield improved designs. Here, we review recent progress along both tracks, discuss their strengths and weaknesses, and highlight opportunities for synergistic approaches. [Display omitted] •Machine learning for protein design is progressing along two tracks: evolutionary-based and physics-inspired approaches.•Here, we recapitulate the main progresses for both classes and discuss their respective strengths and limitations.•We argue that both methods are highly complementary and discuss current and future synergistic approaches.
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ISSN:0959-440X
1879-033X
1879-033X
DOI:10.1016/j.sbi.2023.102571