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1. OpenAWSEM with Open3SPN2: A fast, flexible, and accessible framework for large-scale coarse-grained biomolecular simulations

   https://doi.org/10.1371/JOURNAL.PCBI.1008308  

   Lu, Wei ; Bueno, Carlos ; Schafer, Nicholas P. ; Moller, Joshua ; Jin, Shikai ; Chen, Xun ; Chen, Mingchen ; Gu, Xinyu ; Davtyan, Aram ; de Pablo, Juan J. ; et al ( February 2021 , PLOS Computational Biology)

   Schneidman-Duhovny, Dina (Ed.)

   We present OpenAWSEM and Open3SPN2, new cross-compatible implementations of coarse-grained models for protein (AWSEM) and DNA (3SPN2) molecular dynamics simulations within the OpenMM framework. These new implementations retain the chemical accuracy and intrinsic efficiency of the original models while adding GPU acceleration and the ease of forcefield modification provided by OpenMM’s Custom Forces software framework. By utilizing GPUs, we achieve around a 30-fold speedup in protein and protein-DNA simulations over the existing LAMMPS-based implementations running on a single CPU core. We showcase the benefits of OpenMM’s Custom Forces framework by devising and implementing two new potentials that allow us tomore »address important aspects of protein folding and structure prediction and by testing the ability of the combined OpenAWSEM and Open3SPN2 to model protein-DNA binding. The first potential is used to describe the changes in effective interactions that occur as a protein becomes partially buried in a membrane. We also introduced an interaction to describe proteins with multiple disulfide bonds. Using simple pairwise disulfide bonding terms results in unphysical clustering of cysteine residues, posing a problem when simulating the folding of proteins with many cysteines. We now can computationally reproduce Anfinsen’s early Nobel prize winning experiments by using OpenMM’s Custom Forces framework to introduce a multi-body disulfide bonding term that prevents unphysical clustering. Our protein-DNA simulations show that the binding landscape is funneled towards structures that are quite similar to those found using experiments. In summary, this paper provides a simulation tool for the molecular biophysics community that is both easy to use and sufficiently efficient to simulate large proteins and large protein-DNA systems that are central to many cellular processes. These codes should facilitate the interplay between molecular simulations and cellular studies, which have been hampered by the large mismatch between the time and length scales accessible to molecular simulations and those relevant to cell biology.« less
2. Machine learning for parameter auto-tuning in molecular dynamics simulations: Efficient dynamics of ions near polarizable nanoparticles

   https://doi.org/10.1177/1094342019899457  

   Kadupitiya, JCS ; Fox, Geoffrey C ; Jadhao, Vikram ( May 2020 , The International Journal of High Performance Computing Applications)

   Simulating the dynamics of ions near polarizable nanoparticles (NPs) using coarse-grained models is extremely challenging due to the need to solve the Poisson equation at every simulation timestep. Recently, a molecular dynamics (MD) method based on a dynamical optimization framework bypassed this obstacle by representing the polarization charge density as virtual dynamic variables and evolving them in parallel with the physical dynamics of ions. We highlight the computational gains accessible with the integration of machine learning (ML) methods for parameter prediction in MD simulations by demonstrating how they were realized in MD simulations of ions near polarizable NPs. An artificialmore »neural network–based regression model was integrated with MD simulation and predicted the optimal simulation timestep and optimization parameters characterizing the virtual system with 94.3% success. The ML-enabled auto-tuning of parameters generated accurate dynamics of ions for ≈ 10 million steps while improving the stability of the simulation by over an order of magnitude. The integration of ML-enhanced framework with hybrid Open Multi-Processing / Message Passing Interface (OpenMP/MPI) parallelization techniques reduced the computational time of simulating systems with thousands of ions and induced charges from thousands of hours to tens of hours, yielding a maximum speedup of ≈ 3 from ML-only acceleration and a maximum speedup of ≈ 600 from the combination of ML and parallel computing methods. Extraction of ionic structure in concentrated electrolytes near oil–water emulsions demonstrates the success of the method. The approach can be generalized to select optimal parameters in other MD applications and energy minimization problems.« less
3. Molecular Dynamics Simulations on Relaxed Reduced-DimensionalPotential Energy Surfaces

   Lui, C. ( April 2019 , The journal of physical chemistry. A.)

   Molecular dynamics (MD) simulations with full-dimensional potential energy surfaces (PESs) obtained from high-level ab initio calculations are frequently used to model reaction dynamics of small molecules (i.e., molecules with up to 10 atoms). Construction of full-dimensional PESs for larger molecules is, however, not feasible since the number of ab initio calculations required grows rapidly with the increase of dimension. Only a small number of coordinates are often essential for describing the reactivity of even very large systems, and reduced-dimensional PESs with these coordinates can be built for reaction dynamics studies. While analytical methods based on transition-state theory framework are wellmore »established for analyzing the reduced-dimensionalPESs, MD simulation algorithms capable of generating trajectories on such surfaces are more rare. In this work, we present a new MD implementation that utilizes the relaxed reduced-dimensional PES for standard micro canonical (NVE) and canonical (NVT) MD simulations.The method is applied to the pyramidal inversion of a NH3molecule. The results from the MD simulations on a reduced, three-dimensional PES are validated against the ab initio MD simulations, as well as MD simulations on full-dimensional PES and experimental data.« less
4. Phase transition in Polymer Derived Ceramics (PDCs) and its effect on mechanical response

   Ma, C. & ( July 2021 , Proceedings of the International Conference on Computational Methods)

   Polymer-derived ceramics (PDCs) which are fabricated through pyrolysis of preceramic polymers have attracted increasing attention due to their versatility in structure architecture design and property tailoring. Shaping at the polymer state using 3D printing allows the final ceramic products to exhibit arbitrary shapes and complex architectures that are otherwise impossible to achieve through traditional processing routes. The polymer-to-ceramic phase transition also provides additional space for mechanical property tailoring. A multiscale computational model is developed to explore the phase transition mechanisms and their correlations with processing parameters and mechanical response. Calculations in this work concern PMHS/DVB. Molecular dynamics simulations are carriedmore »out first to track the chemical reaction mechanisms and atomic structure evolution. The density of generated gas during pyrolysis is transferred to the finite element model (FEM) for coupled heat transfer and phase transition analysis. FEM calculations reveal the effect of pyrolysis temperature and heating rate on structure-level phase composition and elastic modulus. It is found that there is a threshold of pyrolysis temperature above which full ceramic phase is formed. Higher heating rate promotes ceramization and leads to higher elastic modulus. In addition, volume shrinkage is found to accelerate ceramic formation which slightly enhances material strength.« less
5. Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics

   https://doi.org/10.1039/D1SM00364J  

   Ma, Zhan ; Wang, Shu ; Kim, Minhee ; Liu, Kaibo ; Chen, Chun-Long ; Pan, Wenxiao ( June 2021 , Soft Matter)

   The present work concerns the transferability of coarse-grained (CG) modeling in reproducing the dynamic properties of the reference atomistic systems across a range of parameters. In particular, we focus on implicit-solvent CG modeling of polymer solutions. The CG model is based on the generalized Langevin equation, where the memory kernel plays the critical role in determining the dynamics in all time scales. Thus, we propose methods for transfer learning of memory kernels. The key ingredient of our methods is Gaussian process regression. By integration with the model order reduction via proper orthogonal decomposition and the active learning technique, the transfermore »learning can be practically efficient and requires minimum training data. Through two example polymer solution systems, we demonstrate the accuracy and efficiency of the proposed transfer learning methods in the construction of transferable memory kernels. The transferability allows for out-of-sample predictions, even in the extrapolated domain of parameters. Built on the transferable memory kernels, the CG models can reproduce the dynamic properties of polymers in all time scales at different thermodynamic conditions (such as temperature and solvent viscosity) and for different systems with varying concentrations and lengths of polymers.« less