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

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.

P-SaMI

“…Thus, the hypothesis "The use of PSami results in gains" was reached. Indeed, similar gain figures were obtained in a cloud-based implementation of P-SaMI using triclosan (TCL400 from PDB ID:1P45A) by the use of 3 different clustering schemes on the same 3,100 snapshots [10].…”

Inverse docking method for new proteins targets identification: A parallel approach

“…Different strategies are used such as Message Passing Interface (MPI or openMP) used alone [21] or combined with multi-threading programming [22], cloud-computing to treat Full Flexible Receptors (FFR) models [23,24] or even FPGAs and GPUs accelerators utilization [25].…”

Clustering Molecular Dynamics trajectories with a univariate estimation of distribution algorithm

“…These MD ensembles, hereby called a Fully-Flexible Receptor (FFR) model, typically hold over 10 4 MD structures. For this reason, recent studies on combining docking and MD simulations have developed novel techniques to systematically reduce the number of MD structures without losing essential information, usually employing clustering algorithms for achieving the desired reduction [5]- [7]. By clustering highly-similar MD conformations regarding their substrate-binding cavities, one can extract the most relevant information during the molecular docking experiments, reducing its overall computational cost.…”