ArticleNature communications2021
Mapping the glycosyltransferase fold landscape using interpretable deep learning.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
36 citing papers in PubMed, 51 citations in OpenAlex.
- Donor-induced conformational gating and substrate-assisted catalysis in α-1,3-galactosyltransferase.Protein science : a publication of the Protein Society · 2026Article
- Ngt-1: A glycosyltransferase that confers resistance to three distinct antibiotic classes.The Journal of biological chemistry · 2026Article
- Discovery and engineering of UDP-glycosyltransferase and its applications in natural sweetener glycosylation.World journal of microbiology & biotechnology · 2026Review
- Article
- Crystal structure of epidermal growth factor domain-specificPNAS nexus · 2026Article
- Rhizospheric glycosyltransferase repertoires as a resource for enabling sustainable bioprocessing and green biocatalyst discovery.Scientific reports · 2026Article
- Evolutionary analyses of the animal glycosyltransferase family 54 reveals two β1,4-N-acetylglucosaminyltransferase families.iScience · 2025Article
- Identification and classification of ion-channels across the tree of life provide functional insights into understudied CALHM channels.bioRxiv : the preprint server for biology · 2025Article
- Structure and membrane interactions of Arabidopsis thaliana DGD2, a glycosyltransferase in the chloroplast membrane.The Journal of biological chemistry · 2025Article
- Glycosyltransferases: glycoengineers in human milk oligosaccharide synthesis and manufacturing.Frontiers in molecular biosciences · 2025Review
- Expanding the repertoire of imine reductases by mining divergent biosynthetic pathways for promiscuous reactivity.Chem catalysis · 2024Article
- Functional and Genomic Insights into the Biotechnological Potential of Vibrio spp. Isolated from Deeply Polluted and Pristine Environments.Current microbiology · 2024Article
- Polysaccharide sulfotransferases: the identification of putative sequences and respective functional characterisation.Essays in biochemistry · 2024Review
- Applying interpretable machine learning in computational biology-pitfalls, recommendations and opportunities for new developments.Nature methods · 2024Review
- Architecture, Function, Regulation, and Evolution of α-Glucans Metabolic Enzymes in Prokaryotes.Chemical reviews · 2024Review
- Insights into the inner workings of transformer models for protein function prediction.Bioinformatics (Oxford, England) · 2024Article
- HarnessingFrontiers in plant science · 2024Article
- Specificity determinants revealed by the structure of glycosyltransferase Campylobacter concisus PglA.Protein science : a publication of the Protein Society · 2024Article
- Glycosyltransferase 8 domain-containing protein 1 (GLT8D1) is a UDP-dependent galactosyltransferase.Scientific reports · 2023Article
- Structural Characterization and Ligand-Induced Conformational Changes of SenB, a Se-Glycosyltransferase Involved in Selenoneine Biosynthesis.Biochemistry · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.