Sampling of structure and sequence space of small protein folds.

Sampling of structure and sequence space of small protein folds.
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DOI:
10.1038/s41467-022-34937-8
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发表时间:
2022-11-22
影响因子:
16.6
通讯作者:
Strauch, Eva-Maria
Strauch, Eva-Maria
中科院分区:
综合性期刊1区
文献类型:
--
作者:
Linsky, Thomas W.;Noble, Kyle;Tobin, Autumn R.;Crow, Rachel;Carter, Lauren;Urbauer, Jeffrey L.;Baker, David;Strauch, Eva-Maria

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Nature only samples a small fraction of the sequence space that can fold into stable proteins. Furthermore, small structural variations in a single fold, sometimes only a few amino acids, can define a protein’s molecular function. Hence, to design proteins with novel functionalities, such as molecular recognition, methods to control and sample shape diversity are necessary. To explore this space, we developed and experimentally validated a computational platform that can design a wide variety of small protein folds while sampling shape diversity. We designed and evaluated stability of about 30,000 de novo protein designs of eight different folds. Among these designs, about 6,200 stable proteins were identified, including some predicted to have a first-of-its-kind minimalized thioredoxin fold. Obtained data revealed protein folding rules for structural features such as helix-connecting loops. Beyond serving as a resource for protein engineering, this massive and diverse dataset also provides training data for machine learning. We developed an accurate classifier to predict the stability of our designed proteins. The methods and the wide range of protein shapes provide a basis for designing new protein functions without compromising stability. In this work the authors provide a computational workflow for the parallel, from scratch, design of proteins to rapidly explore the shape diversity of protein folds.
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