Porting Adaptive Ensemble Molecular Dynamics Workflows to the Summit Supercomputer

Ossyra, John R.; Sedova, Ada; Tharrington, Arnold; Noé, Frank; Clementi, Cecilia; Smith, Jeremy C.

doi:10.1007/978-3-030-34356-9_30

Cited by 9 publications

(6 citation statements)

References 42 publications

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“…Furthermore, shared parallel filesystems allow for rapid, in-place data analysis on large output datasets using parallel tools [19]. The ability to make use of either the high-speed interconnect for traditional parallelization with MPI [17], or alternately, a dataflow execution model [20,21], or a combination of the two [18], helps with design of flexible, multi-task workflows that are often found in computational biology.…”

Section: Background and Related Workmentioning

confidence: 99%

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Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer

Gao¹,

Coletti²,

Davidson³

et al. 2022

Preprint

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Deep learning has contributed to major advances in the prediction of protein structure from sequence, a fundamental problem in structural bioinformatics. With predictions now approaching the accuracy of crystallographic resolution in some cases, and with accelerators like GPUs and TPUs making inference using large models rapid, fast genome-level structure prediction becomes an obvious aim. Leadership-class computing resources can be used to perform genome-scale protein structure prediction using state-of-the-art deep learning models, providing a wealth of new data for systems biology applications. Here we describe our efforts to efficiently deploy the AlphaFold2 program, for full-proteome structure prediction, at scale on the Oak Ridge Leadership Computing Facility's resources, including the Summit supercomputer. We performed inference to produce the predicted structures for 35,634 protein sequences, corresponding to three prokaryotic proteomes and one plant proteome, using under 4,000 total Summit node hours, equivalent to using the majority of the supercomputer for one hour. We also designed an optimized structure refinement that reduced the time for the relaxation stage of the AlphaFold pipeline by over 10X for longer sequences. We demonstrate the types of analyses that can be performed on proteome-scale collections of sequences, including a search for novel quaternary structures and implications for functional annotation.

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Section: Background and Related Workmentioning

confidence: 99%

“…In the original AlphaFold program, the CPU version is used. We have found that the GPU version can provide exceptional performance on Summit [20], so we designed a relaxation protocol that used OpenMM's GPU platform. We also decoupled this step into its own separate workflow.…”

Section: Geometry Optimizationmentioning

confidence: 99%

Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer

Gao¹,

Coletti²,

Davidson³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…BioExcel CoE joined together some of the most important HPC WFMS around the world in a dedicated workshop (2018), with the goal of identifying specific software gaps and opportunities for improved workflow practices. 118 A representation of the most popular WFMS in the field were present in the workshop: Parsl 119 , AdaptiveMD, 120 PyCOMPSs, 121 BioSimSpace, 122 LongBow, 123 FireWorks, 124 Swift, 125 ExTASY, 126 , 127 and RADICAL. 128 , 129 Although sharing many of the most important features (automation, data control flow, task management, dependency graph), each WFMS has its own design goal, and adoption of one versus another implies a thorough analysis of the particular research project needs and the WFMS features.…”

Section: Taking Advantage Of High Performance Computingmentioning

confidence: 99%

Pre‐exascale HPC approaches for molecular dynamics simulations. Covid‐19 research: A use case

Wieczór

Genna

Aranda

et al. 2022

WIREs Comput Mol Sci

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Exascale computing has been a dream for ages and is close to becoming a reality that will impact how molecular simulations are being performed, as well as the quantity and quality of the information derived for them. We review how the biomolecular simulations field is anticipating these new architectures, making emphasis on recent work from groups in the BioExcel Center of Excellence for High Performance Computing. We exemplified the power of these simulation strategies with the work done by the HPC simulation community to fight Covid‐19 pandemics. This article is categorized under: Data Science > Computer Algorithms and Programming Data Science > Databases and Expert Systems Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods

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“…Seq: YP_009724390.1) and the crystal structure of SARS-CoV S-protein as a template to generate the model of the SARS-CoV-2 S-protein:ACE2 complex. In the second phase, molecular dynamics simulations were carried out using GROMACS (compiled on ORNL SUMMIT and run with CHARMM36 force-field (Ossyra et al, 2019;Abraham et al, 2015)) to generate an ensemble of highest probability, lowest energy conformations of the complex which were selected via clustering of the conformations. In the final in-silico docking phase, AutoDock Vina was run in parallel using an MPI wrapper.…”

Section: Supercomputers and Covid-19 221 Drug Repurposingmentioning

confidence: 99%

Distributed Computing in a Pandemic

Alnasir

2022

ADCAIJ

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The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus has resulted in over a million deaths and is having a grave socio-economic impact, hence there is an urgency to find solutions to key research challenges. Much of this COVID-19 research depends on distributed computing. In this article, I review distributed architectures -- various types of clusters, grids and clouds -- that can be leveraged to perform these tasks at scale, at high-throughput, with a high degree of parallelism, and which can also be used to work collaboratively. High-performance computing (HPC) clusters will be used to carry out much of this work. Several bigdata processing tasks used in reducing the spread of SARS-CoV-2 require high-throughput approaches, and a variety of tools, which Hadoop and Spark offer, even using commodity hardware. Extremely large-scale COVID-19 research has also utilised some of the world's fastest supercomputers, such as IBM's SUMMIT -- for ensemble docking high-throughput screening against SARS-CoV-2 targets for drug-repurposing, and high-throughput gene analysis -- and Sentinel, an XPE-Cray based system used to explore natural products. Grid computing has facilitated the formation of the world's first Exascale grid computer. This has accelerated COVID-19 research in molecular dynamics simulations of SARS-CoV-2 spike protein interactions through massively-parallel computation and was performed with over 1 million volunteer computing devices using the Folding@home platform. Grids and clouds both can also be used for international collaboration by enabling access to important datasets and providing services that allow researchers to focus on research rather than on time-consuming data-management tasks.

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Porting Adaptive Ensemble Molecular Dynamics Workflows to the Summit Supercomputer

Cited by 9 publications

References 42 publications

Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer

Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer

Pre‐exascale HPC approaches for molecular dynamics simulations. Covid‐19 research: A use case

Distributed Computing in a Pandemic

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