PROXIMAL: a Method for Prediction of Xenobiotic Metabolism

Overview

Journal BMC Syst Biol

Publisher Biomed Central

Specialty Biology

Date 2015 Dec 24

PMID 26695483

Citations 14

Authors

Mona Yousofshahi

Sara Manteiga

Charmian Wu

Kyongbum Lee

Soha Hassoun

Affiliations

Soon will be listed here.

Abstract

Background: Contamination of the environment with bioactive chemicals has emerged as a potential public health risk. These substances that may cause distress or disease in humans can be found in air, water and food supplies. An open question is whether these chemicals transform into potentially more active or toxic derivatives via xenobiotic metabolizing enzymes expressed in the body. We present a new prediction tool, which we call PROXIMAL (Prediction of Xenobiotic Metabolism) for identifying possible transformation products of xenobiotic chemicals in the liver. Using reaction data from DrugBank and KEGG, PROXIMAL builds look-up tables that catalog the sites and types of structural modifications performed by Phase I and Phase II enzymes. Given a compound of interest, PROXIMAL searches for substructures that match the sites cataloged in the look-up tables, applies the corresponding modifications to generate a panel of possible transformation products, and ranks the products based on the activity and abundance of the enzymes involved.

Results: PROXIMAL generates transformations that are specific for the chemical of interest by analyzing the chemical's substructures. We evaluate the accuracy of PROXIMAL's predictions through case studies on two environmental chemicals with suspected endocrine disrupting activity, bisphenol A (BPA) and 4-chlorobiphenyl (PCB3). Comparisons with published reports confirm 5 out of 7 and 17 out of 26 of the predicted derivatives for BPA and PCB3, respectively. We also compare biotransformation predictions generated by PROXIMAL with those generated by METEOR and Metaprint2D-react, two other prediction tools.

Conclusions: PROXIMAL can predict transformations of chemicals that contain substructures recognizable by human liver enzymes. It also has the ability to rank the predicted metabolites based on the activity and abundance of enzymes involved in xenobiotic transformation.

Citing Articles

Extending PROXIMAL to predict degradation pathways of phenolic compounds in the human gut microbiota.

Balzerani F, Blasco T, Perez-Burillo S, Valcarcel L, Hassoun S, Planes F NPJ Syst Biol Appl. 2024; 10(1):56.

PMID: 38802371 PMC: 11130242. DOI: 10.1038/s41540-024-00381-1.

Pickaxe: a Python library for the prediction of novel metabolic reactions.

Shebek K, Strutz J, Broadbelt L, Tyo K BMC Bioinformatics. 2023; 24(1):106.

PMID: 36949401 PMC: 10031857. DOI: 10.1186/s12859-023-05149-8.

Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products.

Porokhin V, Liu L, Hassoun S Bioinformatics. 2023; 39(3).

PMID: 36790067 PMC: 9991054. DOI: 10.1093/bioinformatics/btad089.

Constructing xenobiotic maps of metabolism to predict enzymes catalyzing metabolites capable of binding to DNA.

Conan M, Theret N, Langouet S, Siegel A BMC Bioinformatics. 2021; 22(1):450.

PMID: 34548010 PMC: 8454073. DOI: 10.1186/s12859-021-04363-6.

Analysis of metabolic network disruption in engineered microbial hosts due to enzyme promiscuity.

Porokhin V, Amin S, Nicks T, Endalur Gopinarayanan V, Nair N, Hassoun S Metab Eng Commun. 2021; 12:e00170.

PMID: 33850714 PMC: 8039717. DOI: 10.1016/j.mec.2021.e00170.

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