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Title: Quick and Accurate Estimates of Mutation Effects on Transition-State Stabilization of Enzymes from Molecular Simulations with Restrained Transition States
Data science and machine learning are revolutionizing enzyme engineering; however, high-throughput simulations for screening large libraries of enzyme variants remain a challenge. Here, we present a novel but highly simple approach to comparing enzyme variants with fully atomistic classical molecular dynamics (MD) simulations on a tractable timescale. Our method greatly simplifies the problem by restricting sampling only to the reaction transition state, and we show that the resulting measurements of transition-state stability are well correlated with experimental activity measurements across two highly distinct enzymes, even for mutations with effects too small to resolve with free energy methods. This method will enable atomistic simulations to achieve sampling coverage for enzyme variant prescreening and machine learning model training on a scale that was previously not possible.  more » « less
Award ID(s):
1934292
PAR ID:
10482704
Author(s) / Creator(s):
; ;
Publisher / Repository:
ACS Publications
Date Published:
Journal Name:
The Journal of Physical Chemistry B
Volume:
126
Issue:
48
ISSN:
1520-6106
Page Range / eLocation ID:
9964 to 9970
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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