Decoding substrate specificity determining factors in glycosyltransferase-B enzymes – insights from machine learning models
Open accessPublished version of a preprint
Abstract
This study develops ML models trained on both sequence and structural data to investigate substrate specificity of GT-B glycosyltransferases. Despite good predictive accuracies, the models highlight areas for improving interpretability.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
A general model to predict small molecule substrates of enzymes based on machine and deep learning
Kroll, A., Ranjan, S., Engqvist, M. K. M., & Lercher, M. J. (2023) · Nature Communications · doi:10.1038/s41467-023-38347-2 · Cited by 193 · see citing works
Machine learning modeling of family wide enzyme-substrate specificity screens
Goldman, S., Das, R., Yang, K. K., & Coley, C. W. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009853 · Cited by 120 · see citing works
Flagged first, then by citations
- Authors
- Hennen, S. G., Bomble, Y. J., Urbanowicz, B. R., & Bharadwaj, V. S.
- Affiliations
- National Laboratory of the Rockies (US)
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
- Corresponding author
- Bharadwaj, Vivek S.
- Venue
- Digital Discovery
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2025
- Volume
- 4
- Issue
- 8
- Pages
- 2214-2228
- ISSN
- 2635-098X (ISSN-L)
- OpenAlex
- W4412031651
- Cited by
- 9
- References
- 3
- Citations by year
- 2026: 9
- FWCI
- 2.72
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Digital Discovery (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Funders
- U.S. Department of Energy (DE-AC36-08GO28308)
- Biological and Environmental Research (DE-SC0023223)
- Workforce Development for Teachers and Scientists
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Glycosylation and Glycoproteins Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- substrate specificity, machine learning prediction, sequence-based prediction, structure-based prediction, model interpretability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
