Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting

Sarkar, Daipayan; Lee, Hyungro; Vant, John W.; Turilli, Matteo; Vermaas, Josh V.; Jha, Shantenu; Singharoy, Abhishek

doi:10.1021/acs.jcim.3c00350

Title: Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting

Journal Article · 03 September 2023 · Journal of Chemical Information and Modeling

DOI: https://doi.org/10.1021/acs.jcim.3c00350 · OSTI ID:2228746

^[1]; Lee, Hyungro ^[2]; Vant, John W. ^[3]; Turilli, Matteo ^[4];

^[5]; Jha, Shantenu ^[4];

^[3]

Michigan State Univ., East Lansing, MI (United States). MSU-DOE Plant Research Laboratory; Arizona State Univ., Tempe, AZ (United States)
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Rutgers Univ., New Brunswick, NJ (United States)
Arizona State Univ., Tempe, AZ (United States)
Rutgers Univ., New Brunswick, NJ (United States); Brookhaven National Laboratory (BNL), Upton, NY (United States)
Michigan State Univ., East Lansing, MI (United States). MSU-DOE Plant Research Laboratory

Recent advances in cryo-electron microscopy (cryo-EM) have enabled modeling macromolecular complexes that are essential components of the cellular machinery. The density maps derived from cryo-EM experiments are often integrated with manual, knowledge or artificial intelligence driven, and physics-guided computational methods to build, fit, and refine molecular structures. Going beyond a single stationary- structure determination scheme, it is becoming more common to interpret the experimental data with an ensemble of models, which contributes to an average observation. Hence, there is a need to decide on the quality of an ensemble of protein structures on-the-fly, while refining them against the density maps. Here, we introduce such an adaptive decision making scheme during the molecular dynamics flexible fitting (MDFF) of biomolecules. Using RADICAL-Cybertools, and the new RADICAL augmented MDFF implementation (R-MDFF) is examined in high-performance computing environments for refinement of two protein systems, Adenylate Kinase and Carbon Monoxide Dehydrogenase. For the test cases, use of multiple replicas in flexible fitting with adaptive decision making in R-MDFF improves the overall correlation to the density by 40% relative to the refinements of the brute-force MDFF. The improvements are particularly significant at high, 2 - 3 Å, map resolutions. More importantly, the ensemble model captures key features of biologically relevant molecular dynamics that is inaccessible to a single-model interpretation. Finally, the pipeline is applicable to systems of growing sizes, which is demonstrated using ensemble refinement of capsid proteins from Chimpanzee adenovirus. The overhead for decision making remaining low and robust to computing environments. The software is publicly available on GitHub and includes a short user guide to install the R-MDFF on different computing environments, from local Linux based workstations to High Performance Computing (HPC) environments.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOD; USDOE

Grant/Contract Number:: AC05-00OR22725; AC05-76RL01830

OSTI ID:: 2228746

Report Number(s):: PNNL-SA--189482

Journal Information:: Journal of Chemical Information and Modeling, Journal Name: Journal of Chemical Information and Modeling Journal Issue: 18 Vol. 63; ISSN 1549-9596

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
artificial intelligence
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Title: Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting

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