Laboratório de Bioinformática, Modelagem e Simulação de Biossistemas (LABIO), Faculdade de Informática (FACIN), Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil.
Biomed Res Int. 2013;2013:469363. doi: 10.1155/2013/469363. Epub 2013 Apr 11.
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.
全柔性蛋白受体（FFR）模型的分子对接模拟已经成熟。在我们的研究中，FFR 模型由受体分子动力学模拟轨迹中衍生出的一系列不同构象表示。对于 FFR 模型中的每个构象，都执行和分析一次对接模拟。一个重要的挑战是使用 FFR 模型以顺序模式对数百万个配体进行虚拟筛选，因为这可能会在计算上非常耗费资源。在本文中，我们提出了一个名为 web Flexible Receptor Docking Workflow（wFReDoW）的基于云的 Web 环境，该环境可减少 FFR 模型中小分子的分子对接模拟的 CPU 时间。它基于称为自适应多实例（P-SaMIs）的新工作流数据模式和基于 Amazon EC2 实例构建的中间件。P-SaMI 减少了分子对接模拟的数量，而中间件则通过在云中使用高性能计算（HPC）环境来加速对接实验。实验结果表明，对接实验的总耗时减少，并且通过丢弃由 P-SaMI 数据模式控制的 FFR 模型中不可行的构象，可以产生质量更好的新简化受体模型。
