Literature DB >> 31896525

A molecular interaction field describing nonconventional intermolecular interactions and its application to protein-ligand interaction prediction.

Daichi Hayakawa1, Nahoko Sawada2, Yurie Watanabe2, Hiroaki Gouda3.   

Abstract

Nonconventional noncovalent interactions such as CH/O, CH/π, and halogen bonds play important roles in molecular recognition in biological systems and have been increasingly exploited in structure-based drug design. In silico approaches that consider these interactions would be an effective strategy for drug discovery. The computation of the molecular interaction field (MIF), which is a three-dimensional (3D) potential map describing the interactions formed around a target compound, would assist the design of molecules that bind to the target biomolecule via nonconventional interactions. In this study, we developed a novel MIF calculation method that describes nonconventional interactions. This method evaluates the MIF as the interaction energy between the target ligand molecule and probe molecule. To describe the nonconventional interactions, our method employs quantum chemical calculations with four types of probe molecules. The calculated MIFs for casein kinase 2 (CK2) inhibitors correctly identify the halogen bond, CH/π, and CH/O interactions formed in the CK2/inhibitor complexes. Additionally, we have developed a method for calculating the protein-ligand interaction energy (Eint) based on the MIF and a coarse-grained protein model. The calculated interaction energies for CK2 inhibitors correlate with the experimental log(Ki) values. Thus, MIF and Eint obtained by our method show promise as descriptors for protein-ligand interaction prediction by considering nonconventional noncovalent interactions.
Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Halogen bonds; Molecular interaction field; Nonconventional hydrogen bonds; Protein–ligand binding; Structure-based drug design

Mesh:

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Year:  2019        PMID: 31896525     DOI: 10.1016/j.jmgm.2019.107515

Source DB:  PubMed          Journal:  J Mol Graph Model        ISSN: 1093-3263            Impact factor:   2.518


  2 in total

Review 1.  Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.

Authors:  Vertika Gautam; Anand Gaurav; Neeraj Masand; Vannajan Sanghiran Lee; Vaishali M Patil
Journal:  Mol Divers       Date:  2022-07-11       Impact factor: 3.364

2.  Screening possible drug molecules for Covid-19. The example of vanadium (III/IV/V) complex molecules with computational chemistry and molecular docking.

Authors:  Manos C Vlasiou; Kyriaki S Pafti
Journal:  Comput Toxicol       Date:  2021-01-30
  2 in total

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