Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation information.

ABSTRACT: Predicting the effects of one or more mutations to the in vivo or in vitro properties of a wild-type protein is a major computational challenge, due to the presence of epistasis, that is, of interactions between amino acids in the sequence. We introduce a computationally efficient procedure to build minimal epistatic models to predict mutational effects by combining evolutionary (homologous sequence) and few mutational-scan data. Mutagenesis measurements guide the selection of links in a sparse graphical model, while the parameters on the nodes and the edges are inferred from sequence data. We show, on 10 mutational scans, that our pipeline exhibits performances comparable to state-of-the-art deep networks trained on many more data, while requiring much less parameters and being hence more

SUBMITTER: Cocco S 

PROVIDER: S-EPMC11214004 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature