Title: MDM2 case study: computational protocol utilising protein flexibility and data mining improves ligand binding mode predictions

Authors: Anthony Ascone; Ridwan Sakidja

Addresses: Department of Physics, Astronomy and Materials Science, Missouri State University, 901 S. National Ave. Springfield, MO 65897, USA ' Department of Physics, Astronomy and Materials Science, Missouri State University, 901 S. National Ave. Springfield, MO 65897, USA

Abstract: Recovery of the P53 tumour suppressor pathway via small molecule inhibitors of onco-protein MDM2 highlights the critical role of computational methodologies in targeted cancer therapies. Molecular docking programs, in particular, have become essential during computer-aided drug design by providing a quantitative ranking of predicted binding geometries of small ligands to proteins based on binding free energy. In this study, we found improved ligand binding mode predictions of small medicinal compounds to MDM2 based on RMSD values using AutoDock and AutoDock Vina employing protein binding site flexibility. Additional analysis suggests a data mining protocol using linear regression can isolate the particular flexible bonds necessary for future optimum docking results. The implementation of a flexible receptor protocol based on 'a priori' knowledge obtained from data mining will improve accuracy and reduce costs of high-throughput virtual screenings of potential cancer drugs targeting MDM2.

Keywords: MDM2; murine double minute 2; Autodock; Autodock Vina; molecular docking; data mining; drug design; molecular dynamics; high throughput virtual screenings; protein flexibility.

DOI: 10.1504/IJCBDD.2017.10006004

International Journal of Computational Biology and Drug Design, 2017 Vol.10 No.3, pp.207 - 224

Received: 15 Aug 2016
Accepted: 11 Jan 2017
Published online: 25 Jul 2017 *

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