Super High-Throughput Screening of Enzyme Variants by Spectral Graph Convolutional Neural Networks.

ABSTRACT: Finding new enzyme variants with the desired substrate scope requires screening through a large number of potential variants. In a typical in silico enzyme engineering workflow, it is possible to scan a few thousands of variants, and gather several candidates for further screening or experimental verification. In this work, we show that a Graph Convolutional Neural Network (GCN) can be trained to predict the binding energy of combinatorial libraries of enzyme complexes using only sequence information. The GCN model uses a stack of message-passing and graph pooling layers to extract information from the protein input graph and yield a prediction. The GCN model is agnostic to the identity of the ligand, which is kept constant within the mutant libraries. Using a miniscule subset of th

SUBMITTER: Ramirez-Palacios C 

PROVIDER: S-EPMC10373491 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature