Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase.
In this study, we describe the development of new machine learning models to predict inhibition of the enzyme 3-dehydroquinate dehydratase (DHQD). This enzyme is the third step of the shikimate pathway and is responsible for the synthesis of chorismate, which is a natural precursor of aromatic amino acids. The enzymes of shikimate pathway are absent in humans, which make them protein targets for the design of antimicrobial drugs. We focus our study on the crystallographic structures of DHQD in complex with competitive inhibitors, for which experimental inhibition constant data is available. Application of supervised machine learning techniques was able to elaborate a robust DHQD-targeted model to predict binding affinity. Combination of high-resolution crystallographic structures and binding information indicates that the prevalence of intermolecular electrostatic interactions between DHQD and competitive inhibitors is of pivotal importance for the binding affinity against this enzyme. The present findings can be used to speed up virtual screening studies focused on the DHQD structure.
['de Ávila|Maurício Boff|MB|http://orcid.org/0000-0002-6511-0011', 'de Azevedo|Walter Filgueira|WF|http://orcid.org/0000-0001-8640-357X']
D019540:Area Under Curve D001665:Binding Sites D006801:Humans D006836:Hydro-Lyases D000069550:Machine Learning D062105:Molecular Docking Simulation D017434:Protein Structure, Tertiary D012372:ROC Curve D012765:Shikimic Acid D055672:Static Electricity
machine learning 3-dehydroquinate dehydratase crystallographic structures drug design protein-ligand interactions systems biology