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Xia J , Flynn W , Gallicchio E , Uplinger K , Armstrong JD , Forli S , Olson AJ , Levy RM
Massive-Scale Binding Free Energy Simulations of HIV Integrase Complexes Using Asynchronous Replica Exchange Framework Implemented on the IBM WCG Distributed Network
J Chem Inf Model. 2019 Apr 22;59(4) :1382-1397
PMID: 30758197    PMCID: PMC6496938    URL: https://www.ncbi.nlm.nih.gov/pubmed/30758197
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To perform massive-scale replica exchange molecular dynamics (REMD) simulations for calculating binding free energies of protein-ligand complexes, we implemented the asynchronous replica exchange (AsyncRE) framework of the binding energy distribution analysis method (BEDAM) in implicit solvent on the IBM World Community Grid (WCG) and optimized the simulation parameters to reduce the overhead and improve the prediction power of the WCG AsyncRE simulations. We also performed the first massive-scale binding free energy calculations using the WCG distributed computing grid and 301 ligands from the SAMPL4 challenge for large-scale binding free energy predictions of HIV-1 integrase complexes. In total there are approximately 10000 simulated complexes, approximately 1 million replicas, and approximately 2000 mus of aggregated MD simulations. Running AsyncRE MD simulations on the WCG requires accepting a trade-off between the number of replicas that can be run (breadth) and the number of full RE cycles that can be completed per replica (depth). As compared with synchronous Replica Exchange (SyncRE) running on tightly coupled clusters like XSEDE, on the WCG many more replicas can be launched simultaneously on heterogeneous distributed hardware, but each full RE cycle requires more overhead. We compared the WCG results with that from AutoDock and more advanced RE simulations including the use of flattening potentials to accelerate sampling of selected degrees of freedom of ligands and/or receptors related to slow dynamics due to high energy barriers. We propose a suitable strategy of RE simulations to refine high throughput docking results which can be matched to corresponding computing resources: from HPC clusters, to small or medium-size distributed campus grids, and finally to massive-scale computing networks including millions of CPUs like the resources available on the WCG.
1549-960x Xia, Junchao ORCID: http://orcid.org/0000-0002-5026-2872 Flynn, William Gallicchio, Emilio ORCID: http://orcid.org/0000-0002-2606-4913 Uplinger, Keith Armstrong, Jonathan D Forli, Stefano ORCID: http://orcid.org/0000-0002-5964-7111 Olson, Arthur J ORCID: http://orcid.org/0000-0003-0558-4618 Levy, Ronald M ORCID: http://orcid.org/0000-0001-8696-5177 Journal Article R01 GM030580/GM/NIGMS NIH HHS/ S10 OD020095/OD/NIH HHS/ U54 GM103368/GM/NIGMS NIH HHS/ J Chem Inf Model. 2019 Apr 22;59(4):1382-1397. doi: 10.1021/acs.jcim.8b00817. Epub 2019 Feb 22.