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1. End-to-End Differentiable Reactive Molecular Dynamics Simulations Using JAX

   Kaymak, M.C. ; Schoenholz, S.S. ; Cubuk, E.D. ; O’Hearn, K.A. ; Merz Jr., K.M. ; Aktulga, H.M. ( May 2023 , Lecture notes in computer science)

   Bhatele, A. ; Hammond, J. ; Baboulin, M. ; Kruse, C. (Ed.)

   The reactive force field (ReaxFF) interatomic potential is a powerful tool for simulating the behavior of molecules in a wide range of chemical and physical systems at the atomic level. Unlike traditional classical force fields, ReaxFF employs dynamic bonding and polarizability to enable the study of reactive systems. Over the past couple decades, highly optimized parallel implementations have been developed for ReaxFF to efficiently utilize modern hardware such as multi-core processors and graphics processing units (GPUs). However, the complexity of the ReaxFF potential poses challenges in terms of portability to new architectures (AMD and Intel GPUs, RISC-V processors, etc.), and limits the ability of computational scientists to tailor its functional form to their target systems. In this regard, the convergence of cyber-infrastructure for high performance computing (HPC) and machine learning (ML) presents new opportunities for customization, programmer productivity and performance portability. In this paper, we explore the benefits and limitations of JAX, a modern ML library in Python representing a prime example of the convergence of HPC and ML software, for implementing ReaxFF. We demonstrate that by leveraging auto-differentiation, just-in-time compilation, and vectorization capabilities of JAX, one can attain a portable, performant, and easy to maintain ReaxFF software. Beyond enabling MD simulations, end-to-end differentiability of trajectories produced by ReaxFF implemented with JAX makes it possible to perform related tasks such as force field parameter optimization and meta-analysis without requiring any significant software developments. We also discuss scalability limitations using the current version of JAX for ReaxFF simulations. 

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2. CellDynaMo–stochastic reaction-diffusion-dynamics model: Application to search-and-capture process of mitotic spindle assembly

   https://doi.org/10.1371/journal.pcbi.1010165  

   Kliuchnikov, Evgenii ; Zhmurov, Artem ; Marx, Kenneth A. ; Mogilner, Alex ; Barsegov, Valeri ( June 2022 , PLOS Computational Biology)

   Meier-Schellersheim, Martin (Ed.)

   We introduce a Stochastic Reaction-Diffusion-Dynamics Model (SRDDM) for simulations of cellular mechanochemical processes with high spatial and temporal resolution. The SRDDM is mapped into the CellDynaMo package, which couples the spatially inhomogeneous reaction-diffusion master equation to account for biochemical reactions and molecular transport within the Langevin Dynamics (LD) framework to describe dynamic mechanical processes. This computational infrastructure allows the simulation of hours of molecular machine dynamics in reasonable wall-clock time. We apply SRDDM to test performance of the Search-and-Capture of mitotic spindle assembly by simulating, in three spatial dimensions, dynamic instability of elastic microtubules anchored in two centrosomes, movement and deformations of geometrically realistic centromeres with flexible kinetochores and chromosome arms. Furthermore, the SRDDM describes the mechanics and kinetics of Ndc80 linkers mediating transient attachments of microtubules to the chromosomal kinetochores. The rates of these attachments and detachments depend upon phosphorylation states of the Ndc80 linkers, which are regulated in the model by explicitly accounting for the reactions of Aurora A and B kinase enzymes undergoing restricted diffusion. We find that there is an optimal rate of microtubule-kinetochore detachments which maximizes the accuracy of the chromosome connections, that adding chromosome arms to kinetochores improve the accuracy by slowing down chromosome movements, that Aurora A and kinetochore deformations have a small positive effect on the attachment accuracy, and that thermal fluctuations of the microtubules increase the rates of kinetochore capture and also improve the accuracy of spindle assembly. 

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3. Computational study on the binding of Mango-II RNA aptamer and fluorogen using the polarizable force field AMOEBA

   https://doi.org/10.3389/fmolb.2022.946708  

   Yang, Xudong ; Liu, Chengwen ; Kuo, Yu-An ; Yeh, Hsin-Chih ; Ren, Pengyu ( September 2022 , Frontiers in Molecular Biosciences)

   Fluorescent light-up aptamers (FLAPs) are well-performed biosensors for cellular imaging and the detection of different targets of interest, including RNA, non-nucleic acid molecules, metal ions, and so on. They could be easily designed and emit a strong fluorescence signal once bound to specified fluorogens. Recently, one unique aptamer called Mango-II has been discovered to possess a strong affinity and excellent fluorescent properties with fluorogens TO1-Biotin and TO3-Biotin. To explore the binding mechanisms, computational simulations have been performed to obtain structural and thermodynamic information about FLAPs at atomic resolution. AMOEBA polarizable force field, with the capability of handling the highly charged and flexible RNA system, was utilized for the simulation of Mango-II with TO1-Biotin and TO3-Biotin in this work. The calculated binding free energy using published crystal structures is in excellent agreement with the experimental values. Given the challenges in modeling complex RNA dynamics, our work demonstrates that MD simulation with a polarizable force field is valuable for understanding aptamer-fluorogen binding and potentially designing new aptamers or fluorogens with better performance. 

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4. Discovering new potential inhibitors to SARS-CoV-2 RNA dependent RNA polymerase (RdRp) using high throughput virtual screening and molecular dynamics simulations

   https://doi.org/10.1038/s41598-022-24695-4  

   Brunt, Dylan ; Lakernick, Phillip M. ; Wu, Chun ( November 2022 , Scientific Reports)

   RNA dependent RNA polymerase (RdRp), is an essential in the RNA replication within the life cycle of the severely acute respiratory coronavirus-2 (SARS-CoV-2), causing the deadly respiratory induced sickness COVID-19. Remdesivir is a prodrug that has seen some success in inhibiting this enzyme, however there is still the pressing need for effective alternatives. In this study, we present the discovery of four non-nucleoside small molecules that bind favorably to SARS-CoV-2 RdRp over the active form of the popular drug remdesivir (RTP) and adenosine triphosphate (ATP) by utilizing high-throughput virtual screening (HTVS) against the vast ZINC compound database coupled with extensive molecular dynamics (MD) simulations. After post-trajectory analysis, we found that the simulations of complexes containing both ATP and RTP remained stable for the duration of their trajectories. Additionally, it was revealed that the phosphate tail of RTP was stabilized by both the positive amino acid pocket and magnesium ions near the entry channel of RdRp which includes residues K551, R553, R555 and K621. It was also found that residues D623, D760, and N691 further stabilized the ribose portion of RTP with U10 on the template RNA strand forming hydrogen pairs with the adenosine motif. Using these models of RdRp, we employed them to screen the ZINC database of ~ 17 million molecules. Using docking and drug properties scoring, we narrowed down our selection to fourteen candidates. These were subjected to 200 ns simulations each underwent free energy calculations. We identified four hit compounds from the ZINC database that have similar binding poses to RTP while possessing lower overall binding free energies, with ZINC097971592 having a binding free energy two times lower than RTP.

    

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5. Atomistic simulation assisted error-inclusive Bayesian machine learning for probabilistically unraveling the mechanical properties of solidified metals

   https://doi.org/10.1038/s41524-024-01200-1  

   Mahata, A. ; Mukhopadhyay, T. ; Chakraborty, S. ; Asle Zaeem, M. ( January 2024 , npj Computational Materials)

   Solidification phenomenon has been an integral part of the manufacturing processes of metals, where the quantification of stochastic variations and manufacturing uncertainties is critically important. Accurate molecular dynamics (MD) simulations of metal solidification and the resulting properties require excessive computational expenses for probabilistic stochastic analyses where thousands of random realizations are necessary. The adoption of inadequate model sizes and time scales in MD simulations leads to inaccuracies in each random realization, causing a large cumulative statistical error in the probabilistic results obtained through Monte Carlo (MC) simulations. In this work, we present a machine learning (ML) approach, as a data-driven surrogate to MD simulations, which only needs a few MD simulations. This efficient yet high-fidelity ML approach enables MC simulations for full-scale probabilistic characterization of solidified metal properties considering stochasticity in influencing factors like temperature and strain rate. Unlike conventional ML models, the proposed hybrid polynomial correlated function expansion here, being a Bayesian ML approach, is data efficient. Further, it can account for the effect of uncertainty in training data by exploiting mean and standard deviation of the MD simulations, which in principle addresses the issue of repeatability in stochastic simulations with low variance. Stochastic numerical results for solidified aluminum are presented here based on complete probabilistic uncertainty quantification of mechanical properties like Young’s modulus, yield strength and ultimate strength, illustrating that the proposed error-inclusive data-driven framework can reasonably predict the properties with a significant level of computational efficiency.

    

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