Mapping the glycosyltransferase fold landscape using interpretable deep learning

Rahil Taujale, Zhongliang Zhou, Wayland Yeung, Kelley W. Moremen, Sheng Li and Natarajan Kannan ()
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Rahil Taujale: Institute of Bioinformatics, University of Georgia
Zhongliang Zhou: Department of Computer Science, University of Georgia
Wayland Yeung: Institute of Bioinformatics, University of Georgia
Kelley W. Moremen: Complex Carbohydrate Research Center, University of Georgia
Sheng Li: Department of Computer Science, University of Georgia
Natarajan Kannan: Institute of Bioinformatics, University of Georgia

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: 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 delineates sequence and structural features characteristic of individual fold types, while classifying them into distinct clusters that group evolutionarily divergent families based on shared secondary structural features. We further extend our model to classify GT families of unknown folds and variants of known folds. By identifying families that are likely to adopt novel folds such as GT91, GT96 and GT97, our studies expand the GT fold landscape and prioritize targets for future structural studies.

Date: 2021
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DOI: 10.1038/s41467-021-25975-9

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