Article: Identification of significant descriptors for enzyme inhibition using the LASSO method and a genetic algorithm search Journal: International Journal of Computational Biology and Drug Design (IJCBDD) 2022 Vol.15 No.3 pp.195 - 248 Abstract: Enzyme inhibitors are molecules that inhibit enzyme activities. Since most drugs are enzyme inhibitors, the analysis of the properties of enzyme inhibitors is essential for effective drug discovery. In this study, we analysed the properties that are influencing inhibition in a non-redundant set of enzyme inhibitor complexes from the PDB. The binding free energies and interaction parameters of the complexes, as well as the physical and structural properties of enzymes and ligands, are found out. Then, LASSO regression using the GLMNET module of R and genetic algorithm search using BuildQSAR were performed to identify significant descriptors. Through the analysis, we identified twelve properties with a correlation of R = 0.73 and prediction accuracy was evaluated using the ROC/AUROC curve. These descriptors were tested on 255 enzyme inhibitor complexes from the MOAD database and obtained a correlation of R = 0.63. The present study finds inhibitory properties that would be useful in the initial screening of enzyme inhibition. Inderscience Publishers - linking academia, business and industry through research

Title: Identification of significant descriptors for enzyme inhibition using the LASSO method and a genetic algorithm search

Authors: Mary Rajathei David; Parthasarathy Subbiah; Selvaraj Samuel

Addresses: Department of Biotechnology and Bioinformatics, Bishop Heber College, Tiruchirappalli, 620 017, India ' Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, 620024, India ' Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, 620024, India

Abstract: Enzyme inhibitors are molecules that inhibit enzyme activities. Since most drugs are enzyme inhibitors, the analysis of the properties of enzyme inhibitors is essential for effective drug discovery. In this study, we analysed the properties that are influencing inhibition in a non-redundant set of enzyme inhibitor complexes from the PDB. The binding free energies and interaction parameters of the complexes, as well as the physical and structural properties of enzymes and ligands, are found out. Then, LASSO regression using the GLMNET module of R and genetic algorithm search using BuildQSAR were performed to identify significant descriptors. Through the analysis, we identified twelve properties with a correlation of R = 0.73 and prediction accuracy was evaluated using the ROC/AUROC curve. These descriptors were tested on 255 enzyme inhibitor complexes from the MOAD database and obtained a correlation of R = 0.63. The present study finds inhibitory properties that would be useful in the initial screening of enzyme inhibition.

Keywords: enzyme inhibitors; binding free energies of enzyme inhibitors; enzyme properties; ligand properties; significant descriptors of inhibition; inhibition analysis; drug discovery.

DOI: 10.1504/IJCBDD.2022.128247

International Journal of Computational Biology and Drug Design, 2022 Vol.15 No.3, pp.195 - 248

Received: 09 Mar 2022
Accepted: 15 Aug 2022
Published online: 12 Jan 2023 *

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Title: Identification of significant descriptors for enzyme inhibition using the LASSO method and a genetic algorithm search

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