Machine learning-assisted amidase-catalytic enantioselectivity prediction and rational design of variants for improving enantioselectivity.

ABSTRACT: Biocatalysis is an attractive approach for the synthesis of chiral pharmaceuticals and fine chemicals, but assessing and/or improving the enantioselectivity of biocatalyst towards target substrates is often time and resource intensive. Although machine learning has been used to reveal the underlying relationship between protein sequences and biocatalytic enantioselectivity, the establishment of substrate fitness space is usually disregarded by chemists and is still a challenge. Using 240 datasets collected in our previous works, we adopt chemistry and geometry descriptors and build random forest classification models for predicting the enantioselectivity of amidase towards new substrates. We further propose a heuristic strategy based on these models, by which the rational protein engineeri

SUBMITTER: Li ZL 

PROVIDER: S-EPMC11467325 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature