Open AccessEnhancing computational enzyme design by a maximum entropy strategy
Author(s) -
Wen Jun Xie,
Mojgan Asadi,
Arieh Warshel
Publication year - 2022
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2122355119
Subject(s) - enzyme , entropy (arrow of time) , principle of maximum entropy , stability (learning theory) , computational biology , chemistry , computer science , biology , biological system , biochemistry , physics , artificial intelligence , thermodynamics , machine learning
Significance Designing efficient enzymes could contribute to a sustainable future. Current computational approaches, including physics-based and machine learning–based design, have not led to a robust enzyme design. Predicting enzyme catalytic power is the crucial step for enzyme design. Here, we found that the properties of enzymes are correlated in a nontrivial way with their evolutionary information. For the active site region and the more distant region, the statistical energy obtained from the maximum entropy model for enzyme homologs is strongly correlated with enzyme catalytic power and stability, respectively. The findings here could be used to understand enzyme catalysis and evolution. Combining the present approach with physics-based computer modeling can provide a potent tool for enzyme design.