Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds.

ABSTRACT:

Motivation

Our knowledge of the metabolites in cells and their reactions is far from complete as revealed by metabolomic measurements that detect many more small molecules than are documented in metabolic databases. Here, we develop an approach for predicting the reactivity of small-molecule metabolites in enzyme-catalyzed reactions that combines expert knowledge, computational chemistry and machine learning.

Results

We classified 4843 reactions documented in the KEGG database, from all six Enzyme Commission classes (EC 1-6), into 80 reaction classes, each of which is marked by a characteristic functional group transformation. Reaction centers and surrounding local structures in substrates and products of these reactions were represented using SMARTS. We found that each of t

SUBMITTER: Mu F 

PROVIDER: S-EPMC3102224 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature