Learning Free Energy Pathways through Reinforcement Learning of Adaptive Steered Molecular Dynamics

Ho, Nicholas; Cava, John Kevin; Vant, John; Shukla, Ankita; Miratsky, Jake; Turaga, Pavan; Maciejewski, Ross; Singharoy, Abhishek

doi:10.1101/2022.10.04.510845

Cited by 2 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(6) Again, EnTK uses the data staging area to store this information in files and provide it to the replicas. This feature not only makes the algorithm adaptive, but offers future scope of improvement for applications requiring advanced decision making, either based on inferencing − ,, or neural network-based machine learning algorithms. − Finally, the application converges to yield a refined ensemble (7), which exits the R-MDFF workflow and downloads results to the end-user’s working directory.…”

Section: Adaptive Integrative Modeling Using R-mdffmentioning

confidence: 99%

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

Sarkar,

Lee,

Vant

et al. 2023

J. Chem. Inf. Model.

Self Cite

View full text Add to dashboard Cite

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-driven 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 that 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. We introduce such an adaptive decision-making scheme during the molecular dynamics flexible fitting (MDFF) of biomolecules. Using RADICAL-Cybertools, the new RADICAL augmented MDFF implementation (R-MDFF) is examined in high-performance computing environments for refinement of two prototypical protein systems, adenylate kinase and carbon monoxide dehydrogenase. For these 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 are 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 the chimpanzee adenovirus. The overhead for decision making remains low and robust to computing environments. The software is publicly available on GitHub and includes a short user guide to install R-MDFF on different computing environments, from local Linux-based workstations to high-performance computing environments.

show abstract

Section: Adaptive Integrative Modeling Using R-mdffmentioning

confidence: 99%

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

Sarkar,

Lee,

Vant

et al. 2023

J. Chem. Inf. Model.

Self Cite

View full text Add to dashboard Cite

show abstract

“…This feature not only makes the algorithm adaptive, but offers future scope of improvement for applications requiring advanced decision-making, either based on inferencing [12][13][14]54,55 or neural network based machine learning algorithms. [56][57][58][59][60] Finally, the application converges to yield a refined ensemble (7), which exit the R-MDFF workflow and downloads results to the end-user's working directory. The R-MDFF API is implemented as a Python module, loaded into the workflow application's code.…”

Section: Introductionmentioning

confidence: 99%

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

Sarkar

Lee

Vant

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Recent advances in cryo-electron microscopy (cryo-EM) has enabled modeling macromolecular complexes that are essential components of life. The density maps obtained from cryo-EM experiments is often integrated with ab-initio, knowledge-driven or first principles-based computational methods to build, fit and refine protein structures inside the electron density maps. Going beyond a single stationary-structure determination scheme, it is becoming more common to interpret the experimental data with a set of multiple physical models all of which contributes to the average observation seen by the experiment. 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. In this work, we demonstrate such adaptive decision making capabilities during flexible fitting of biomolecules. Our solution uses RADICAL tools (RCT) and we test this new implementation in exascale high performance computing environment for two proteins, Adenylate Kinase (ADK) and Carbon Monoxide Dehydrogenase (CODH). Our results indicate that using multiple replicas in flexible fitting with adaptive decision making improves the overall quality of fit and model by 40 % improvement when compared against the traditional flexible fitting approach. These advances are agnostic to system-size and computing environments.

show abstract

Learning Free Energy Pathways through Reinforcement Learning of Adaptive Steered Molecular Dynamics

Cited by 2 publications

References 25 publications

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

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

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

Contact Info

Product

Resources

About