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1. Adaptive Sampling Methods for Molecular Dynamics in the Era of Machine Learning

   https://doi.org/10.1021/acs.jpcb.3c04843  

   Kleiman, Diego E.; Nadeem, Hassan; Shukla, Diwakar (December 2023, The Journal of Physical Chemistry B)

   Molecular dynamics (MD) simulations are fundamental computational tools for the study of proteins and their free energy landscapes. However, sampling protein conformational changes through MD simulations is challenging due to the relatively long time scales of these processes. Many enhanced sampling approaches have emerged to tackle this problem, including biased sampling and path-sampling methods. In this Perspective, we focus on adaptive sampling algorithms. These techniques differ from other approaches because the thermodynamic ensemble is preserved and the sampling is enhanced solely by restarting MD trajectories at particularly chosen seeds rather than introducing biasing forces. We begin our treatment with an overview of theoretically transparent methods, where we discuss principles and guidelines for adaptive sampling. Then, we present a brief summary of select methods that have been applied to realistic systems in the past. Finally, we discuss recent advances in adaptive sampling methodology powered by deep learning techniques, as well as their shortcomings. 

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2. Molecular Latent Space Simulators

   https://doi.org/10.1039/D0SC03635H  

   Sidky, Hythem; Chen, Wei; Ferguson, Andrew L (January 2020, Chemical Science)

   Small integration time steps limit molecular dynamics (MD) simulations to millisecond time scales. Markov state models (MSMs) and equation-free approaches learn low-dimensional kinetic models from MD simulation data by performing configurational or dynamical coarse-graining of the state space. The learned kinetic models enable the efficient generation of dynamical trajectories over vastly longer time scales than are accessible by MD, but the discretization of configurational space and/or absence of a means to reconstruct molecular configurations precludes the generation of continuous all-atom molecular trajectories. We propose latent space simulators (LSS) to learn kinetic models for continuous all-atom simulation trajectories by training three deep learning networks to (i) learn the slow collective variables of the molecular system, (ii) propagate the system dynamics within this slow latent space, and (iii) generatively reconstruct molecular configurations. We demonstrate the approach in an application to Trp-cage miniprotein to produce novel ultra-long synthetic folding trajectories that accurately reproduce all-atom molecular structure, thermodynamics, and kinetics at six orders of magnitude lower cost than MD. The dramatically lower cost of trajectory generation enables greatly improved sampling and greatly reduced statistical uncertainties in estimated thermodynamic averages and kinetic rates. 

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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 well 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. 

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4. Docking-based long timescale simulation of cell-size protein systems at atomic resolution

   https://doi.org/10.1073/pnas.2210249119  

   Vakser, Ilya A.; Grudinin, Sergei; Jenkins, Nathan W.; Kundrotas, Petras J.; Deeds, Eric J. (October 2022, Proceedings of the National Academy of Sciences)

   Computational methodologies are increasingly addressing modeling of the whole cell at the molecular level. Proteins and their interactions are the key component of cellular processes. Techniques for modeling protein interactions, thus far, have included protein docking and molecular simulation. The latter approaches account for the dynamics of the interactions but are relatively slow, if carried out at all-atom resolution, or are significantly coarse grained. Protein docking algorithms are far more efficient in sampling spatial coordinates. However, they do not account for the kinetics of the association (i.e., they do not involve the time coordinate). Our proof-of-concept study bridges the two modeling approaches, developing an approach that can reach unprecedented simulation timescales at all-atom resolution. The global intermolecular energy landscape of a large system of proteins was mapped by the pairwise fast Fourier transform docking and sampled in space and time by Monte Carlo simulations. The simulation protocol was parametrized on existing data and validated on a number of observations from experiments and molecular dynamics simulations. The simulation protocol performed consistently across very different systems of proteins at different protein concentrations. It recapitulated data on the previously observed protein diffusion rates and aggregation. The speed of calculation allows reaching second-long trajectories of protein systems that approach the size of the cells, at atomic resolution. 

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5. Computational microscopy: Revealing molecular mechanisms in plants using molecular dynamics simulations

   https://doi.org/10.1105/tpc.119.tt1219  

   Feng, J.; Chen, J.; Selvam, B.; Shukla, D. (December 2019, The Plant Cell)

   Structural biology has provided valuable insights and high-resolution views of the biophysical processes in plants, such as photosynthesis, hormone signaling, nutrient transport, and toxin efflux. However, structural biology only provides a few “snapshots” of protein structure, whereas in vivo, protein function involves complex dynamical processes such as ligand binding and conformational changes that structures alone are unable to capture in full detail. Here, we present all-atom molecular dynamics (MD) simulations as a “computational microscope” that can be used to capture detailed structural and dynamical information about the molecular machinery in plants and gain high-resolution insights into plant growth and function. In addition to the background information provided here, we have prepared a set of tutorials that allow students to run and explore MD simulations of plant proteins. 

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