TY - JOUR
T1 - A simple model predicts UGT-mediated metabolism
AU - Le Dang, Na
AU - Hughes, Tyler B.
AU - Krishnamurthy, Varun
AU - Swamidass, S. Joshua
N1 - Funding Information:
Research reported in this publication was supported by the National Library Of Medicine of the National Institutes of Health under Award Number R01LM012222. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We also appreciate the generous support of both the Department of Immunology and Pathology at the Washington University School of Medicine and the Washington University Center for Biological Systems Engineering. Computations were performed using the facilities of the Washington University Center for High Performance Computing, which were partially funded by NIH grants (1S10RR022984-01A1 and 1S10OD018091-01).
Publisher Copyright:
© The Author 2016.
PY - 2016/10/15
Y1 - 2016/10/15
N2 - Motivation: Uridine diphosphate glucunosyltransferases (UGTs) metabolize 15% of FDA approved drugs. Lead optimization efforts benefit from knowing how candidate drugs are metabolized by UGTs. This paper describes a computational method for predicting sites of UGT-mediated metabolism on drug-like molecules. Results: XenoSite correctly predicts test molecule's sites of glucoronidation in the Top-1 or Top-2 predictions at a rate of 86 and 97%, respectively. In addition to predicting common sites of UGT conjugation, like hydroxyl groups, it can also accurately predict the glucoronidation of atypical sites, such as carbons. We also describe a simple heuristic model for predicting UGT-mediated sites of metabolism that performs nearly as well (with, respectively, 80 and 91% Top-1 and Top-2 accuracy), and can identify the most challenging molecules to predict on which to assess more complex models. Compared with prior studies, this model is more generally applicable, more accurate and simpler (not requiring expensive quantum modeling).
AB - Motivation: Uridine diphosphate glucunosyltransferases (UGTs) metabolize 15% of FDA approved drugs. Lead optimization efforts benefit from knowing how candidate drugs are metabolized by UGTs. This paper describes a computational method for predicting sites of UGT-mediated metabolism on drug-like molecules. Results: XenoSite correctly predicts test molecule's sites of glucoronidation in the Top-1 or Top-2 predictions at a rate of 86 and 97%, respectively. In addition to predicting common sites of UGT conjugation, like hydroxyl groups, it can also accurately predict the glucoronidation of atypical sites, such as carbons. We also describe a simple heuristic model for predicting UGT-mediated sites of metabolism that performs nearly as well (with, respectively, 80 and 91% Top-1 and Top-2 accuracy), and can identify the most challenging molecules to predict on which to assess more complex models. Compared with prior studies, this model is more generally applicable, more accurate and simpler (not requiring expensive quantum modeling).
UR - https://www.scopus.com/pages/publications/84995467608
U2 - 10.1093/bioinformatics/btw350
DO - 10.1093/bioinformatics/btw350
M3 - Article
C2 - 27324196
AN - SCOPUS:84995467608
SN - 1367-4803
VL - 32
SP - 3183
EP - 3189
JO - Bioinformatics
JF - Bioinformatics
IS - 20
ER -