Mapping the glycosyltransferase fold landscape using interpretable deep learning.

ABSTRACT: Glycosyltransferases (GTs) play fundamental roles in nearly all cellular processes through the biosynthesis of complex carbohydrates and glycosylation of diverse protein and small molecule substrates. The extensive structural and functional diversification of GTs presents a major challenge in mapping the relationships connecting sequence, structure, fold and function using traditional bioinformatics approaches. Here, we present a convolutional neural network with attention (CNN-attention) based deep learning model that leverages simple secondary structure representations generated from primary sequences to provide GT fold prediction with high accuracy. The model learns distinguishing secondary structure features free of primary sequence alignment constraints and is highly interpretable. It

SUBMITTER: Taujale R 

PROVIDER: S-EPMC8476585 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature