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High-performance drug discovery: computational screening by combining docking and molecular dynamics simulations.

ABSTRACT: Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, this method is not completely reliable and therefore unsatisfactory. In this study, we used massive molecular dynamics simulations of protein-ligand conformations obtained by molecular docking in order to improve the enrichment performance of molecular docking. Our screening approach employed the molecular mechanics/Poisson-Boltzmann and surface area method to estimate the binding free energies. For the top-ranking 1,000 compounds obtained by docking to a target protein, approximately 6,000 molecular dynamics simulations were performed using multiple docking poses in about a week. As a result, the enrichment performance of the top 100 compounds by our approach was improved by 1.6-4.0 times that of the enrichment performance of molecular dockings. This result indicates that the application of molecular dynamics simulations to virtual screening for lead discovery is both effective and practical. However, further optimization of the computational protocols is required for screening various target proteins.

SUBMITTER: Okimoto N

PROVIDER: S-EPMC2746282 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature

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High-performance drug discovery: computational screening by combining docking and molecular dynamics simulations.

Okimoto Noriaki N Futatsugi Noriyuki N Fuji Hideyoshi H Suenaga Atsushi A Morimoto Gentaro G Yanai Ryoko R Ohno Yousuke Y Narumi Tetsu T Taiji Makoto M

PLoS computational biology 20091009 10

Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, this method is not completely reliable and therefore unsatisfactory. In this study, we used massive molecular dynamics simulations of protein-ligand conformations obtained by molecular docking in order to improve the enrichment performance of molecular docking. Our screening approach employed the molecular mechanics/Poisson-Boltzmann and surface area method to estima ...[more]

PMID: 19816553

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Project description:Hypertension (HTN) is a major risk factor for cardiovascular and renal diseases, cerebrovascular accidents (CVA) and a prime underlying cause of worldwide morbidity and mortality. Hypertension is a complex condition and a strong interplay of multiple genetic, epigenetic and environmental factors is involved in its etiology. Previous studies showed an association of overexpression of genes with hypertension. Satisfactory control of Blood Pressure (BP) levels is not achieved in a major portion of hypertensive patients who take antihypertensive drugs. Since existing antihypertensive drugs have many severe or irreversible side effects and give rise to further complications like frequent micturition and headaches, dizziness, dry irritating cough, hypoglycemia, GI hemorrhage, impaired left ventricular function, hyperkalemia, Anemia, angioedema and azotemia. There is a need to identify new antihypertensive agents that can inhibit the expression of these overexpressed genes contributing to hypertension. The study was designed to identify drug-able targets against overexpressed genes involved in hypertension to intervene the disease. The structure of the protein encoded by an overexpressed gene Endothelin-1 was retrieved from Protein Database (PDB). A library of five thousand phytochemicals was docked against Endothelin-1. The top four hits against Endothelin-1 protein were selected based on S score and Root Mean Square Deviation (RMSD). S score is a molecular docking score which is used to determine the preferred orientation, binding mode, site of the ligand and binding affinity. RMSD refines value for drug target identification. Absorption, distribution, metabolism, excretion, and toxicity profiling (ADMET) was done. The study provides novel insights into HTN etiology and improves our understanding of BP pathophysiology. These findings help to understand the impact of gene expression on BP regulation. This study might be helpful to develop an antihypertensive drug with a better therapeutic profile and least side effects.

Dataset Information

High-performance drug discovery: computational screening by combining docking and molecular dynamics simulations.

Publications

High-performance drug discovery: computational screening by combining docking and molecular dynamics simulations.

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