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BioMed Research International
Volume 2013, Article ID 469363, 12 pages
http://dx.doi.org/10.1155/2013/469363
Methodology Report

wFReDoW: A Cloud-Based Web Environment to Handle Molecular Docking Simulations of a Fully Flexible Receptor Model

1Laboratório de Bioinformática, Modelagem e Simulação de Biossistemas (LABIO), Faculdade de Informática (FACIN), Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Avenida Ipiranga 6681, Prédio 32, Sala 608, 90619-900 Porto Alegre, RS, Brazil
2Grupo de Pesquisa em Inteligência de Negócio (GPIN), Faculdade de Informática (FACIN), Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Avenida Ipiranga 6681, Prédio 32, Sala 628, 90619-900 Porto Alegre, RS, Brazil

Received 6 December 2012; Revised 28 February 2013; Accepted 6 March 2013

Academic Editor: Ming Ouyang

Copyright © 2013 Renata De Paris et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Molecular docking simulations of fully flexible protein receptor (FFR) models are coming of age. In our studies, an FFR model is represented by a series of different conformations derived from a molecular dynamic simulation trajectory of the receptor. For each conformation in the FFR model, a docking simulation is executed and analyzed. An important challenge is to perform virtual screening of millions of ligands using an FFR model in a sequential mode since it can become computationally very demanding. In this paper, we propose a cloud-based web environment, called web Flexible Receptor Docking Workflow (wFReDoW), which reduces the CPU time in the molecular docking simulations of FFR models to small molecules. It is based on the new workflow data pattern called self-adaptive multiple instances (P-SaMIs) and on a middleware built on Amazon EC2 instances. P-SaMI reduces the number of molecular docking simulations while the middleware speeds up the docking experiments using a High Performance Computing (HPC) environment on the cloud. The experimental results show a reduction in the total elapsed time of docking experiments and the quality of the new reduced receptor models produced by discarding the nonpromising conformations from an FFR model ruled by the P-SaMI data pattern.