Large‐scale virtual screening experiments on Windows Azure‐based cloud resources

Kiss, Tamás; Borsody, P.; Terstyánszky, Gábor; Winter, Stephen; Greenwell, Pamela; McEldowney, Sharron; Heindl, Hans

doi:10.1002/cpe.3113

Cited by 12 publications

(14 citation statements)

References 22 publications

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“…Azure was chosen for this study since it is one of the most well-known and well-established cloud platforms. Some studies have used Azure cloud instances to optimize the RDD process, such as prediction of chemical activity using e-SC [ 47 ] and virtual screening practices [ 25 , 28 ].…”

Section: Methodsmentioning

confidence: 99%

“…A number of different methods were proposed in the literature to reduce the elapsed time taken for performing docking-based virtual screening [ 7 ]. Most of these methods scale up simulations based on the volume of drug-like compounds by using High-Performance Computing (HPC) environments, such as computing clusters [ 22 , 23 ], grid computers [ 24 ], and cloud computing [ 25 – 29 ]. Despite having different goals and requirements, all these studies carried out in docking small molecules to rigid biological receptors.…”

Section: Introductionmentioning

confidence: 99%

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A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model

et al. 2018

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BackgroundIn the rational drug design process, an ensemble of conformations obtained from a molecular dynamics simulation plays a crucial role in docking experiments. Some studies have found that Fully-Flexible Receptor (FFR) models predict realistic binding energy accurately and improve scoring to enhance selectiveness. At the same time, methods have been proposed to reduce the high computational costs involved in considering the explicit flexibility of proteins in receptor-ligand docking. This study introduces a novel method to optimize ensemble docking-based experiments by reducing the size of an InhA FFR model at docking runtime and scaling docking workflow invocations on cloud virtual machines.ResultsFirst, in order to find the most affordable cost-benefit pool of virtual machines, we evaluated the performance of the docking workflow invocations in different configurations of Azure instances. Second, we validated the gains obtained by the proposed method based on the quality of the Reduced Fully-Flexible Receptor (RFFR) models produced using AutoDock4.2. The analyses show that the proposed method reduced the model size by approximately 50% while covering at least 86% of the best docking results from the 74 ligands tested. Third, we tested our novel method using AutoDock Vina, a different docking software, and showed the positive accuracy achieved in the resulting RFFR models. Finally, our results demonstrated that the method proposed optimized ensemble docking experiments and is applicable to different docking software. In addition, it detected new binding modes, which would be unreachable if employing only the rigid structure used to generate the InhA FFR model.ConclusionsOur results showed that the selective method is a valuable strategy for optimizing ensemble docking-based experiments using different docking software. The RFFR models produced by discarding non-promising snapshots from the original model are accurately shaped for a larger number of ligands, and the elapsed time spent in the ensemble docking experiments are considerably reduced.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2222-2) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model

et al. 2018

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“…To reduce the overhead, a smaller number of bigger jobs should be created. To do so, more than one ligand should be put into each compute job [ 9 , 10 , 33 ]. Based on this idea, the three components of the workflow in Fig.…”

Section: Methodsmentioning

confidence: 99%

1001 Ways to run AutoDock Vina for virtual screening

Jaghoori

Bleijlevens

Olabarriaga

2016

J Comput Aided Mol Des

158

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Large-scale computing technologies have enabled high-throughput virtual screening involving thousands to millions of drug candidates. It is not trivial, however, for biochemical scientists to evaluate the technical alternatives and their implications for running such large experiments. Besides experience with the molecular docking tool itself, the scientist needs to learn how to run it on high-performance computing (HPC) infrastructures, and understand the impact of the choices made. Here, we review such considerations for a specific tool, AutoDock Vina, and use experimental data to illustrate the following points: (1) an additional level of parallelization increases virtual screening throughput on a multi-core machine; (2) capturing of the random seed is not enough (though necessary) for reproducibility on heterogeneous distributed computing systems; (3) the overall time spent on the screening of a ligand library can be improved by analysis of factors affecting execution time per ligand, including number of active torsions, heavy atoms and exhaustiveness. We also illustrate differences among four common HPC infrastructures: grid, Hadoop, small cluster and multi-core (virtual machine on the cloud). Our analysis shows that these platforms are suitable for screening experiments of different sizes. These considerations can guide scientists when choosing the best computing platform and set-up for their future large virtual screening experiments.Electronic supplementary materialThe online version of this article (doi:10.1007/s10822-016-9900-9) contains supplementary material, which is available to authorized users.

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“…Kiss et al . investigated and analyzed how Windows Azure cloud can be applied for a virtual screening experiment relying on docking simulations, building a framework that exploits the generic worker concept.…”

Section: Special Issue Papersmentioning

confidence: 99%

Latest advances in distributed, parallel, and graphic processing unit accelerated approaches to computational biology

Merelli

Pérez‐Sánchez

Gesing

et al. 2013

Concurrency and Computation

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EDITORIALLatest advances in distributed, parallel, and graphic processing unit accelerated approaches to computational biology INTRODUCTIONBioinformatics is a discipline that performs analyses, modeling, and simulations of complex biological systems by using a computer science approach, which typically means dealing with huge amounts of data. Although the most powerful supercomputers in the world are heavily involved in computational biology research, scalability, portability, integration, and usability of bioinformatics software still represent open issues. Currently, the possibility of parallelizing algorithms and analysis techniques exploiting various high-performance computing (HPC) techniques and platforms is receiving an even increasing interest. Examples include the porting of legacy applications to clusters, such as those for genome analysis, and the use of distributed technologies like grid and cloud computing for large, embarrassingly parallel computations. Also, performance acceleration using on-chip supercomputing, such as graphic processing units (GPUs) and massively parallel architectures for the processing of large data sets, is becoming largely exploited. This trend is motivated by the lightening improvement of novel molecular biology high-throughput technologies, such as next generation sequencing, which allow the analysis of inter personal variations in genomics and transcriptomics, but also to the development of mass spectrometry techniques for proteomics and metabolomics profiles. This clearly calls for novel solutions in the field of HPC. Also in the field of structural biology, in silico molecular dynamic simulations, ligand screening projects for neglected and complex diseases, and drug discovery campaigns are examples of the great advantages that HPC can provide to medicine and healthcare.Many are the projects aiming at developing technologies in the field of high-performance computational biology worldwide. Examples are the EU-funded FP7 projects Venus-C [1] and scientific gateway based user interface (SCI-BUS) [2], and the project Extreme Science and Engineering Discovery Environment (XSEDE) [3] funded by the US National Science Foundation. These projects aim to improve the quality of HPC services for diverse research areas and support a large number of applications and projects in computational biology, for example, Biodrugscore [4] in XSEDE and the Swiss Proteomics Gateway [5] in SCI-BUS. Furthermore, the FP7 projects BioHPC [6] and Elixir [7] are specifically dedicated to the life sciences. Whereas BioHPC offers a suite of applications in a science gateway, Elixir functions as data hub for life sciences organizations. As regards for the most important national initiatives, the Flagship project Interomics All these projects have prompted the diffusion of parallel and distributed solutions for bioinformatics, which also promoted the discussion of these topics in a number of conferences. In particular, this special issue of Concurrency and Computation: Practice and Experience is partia...

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Large‐scale virtual screening experiments on Windows Azure‐based cloud resources

Cited by 12 publications

References 22 publications

A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model

A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model

1001 Ways to run AutoDock Vina for virtual screening

Latest advances in distributed, parallel, and graphic processing unit accelerated approaches to computational biology

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