Biological macromolecules function in highly crowded cellular environments. The structure and dynamics of proteins and nucleic acids are well characterized in vitro, but in vivo crowding effects ...remain unclear. Using molecular dynamics simulations of a comprehensive atomistic model cytoplasm we found that protein-protein interactions may destabilize native protein structures, whereas metabolite interactions may induce more compact states due to electrostatic screening. Protein-protein interactions also resulted in significant variations in reduced macromolecular diffusion under crowded conditions, while metabolites exhibited significant two-dimensional surface diffusion and altered protein-ligand binding that may reduce the effective concentration of metabolites and ligands in vivo. Metabolic enzymes showed weak non-specific association in cellular environments attributed to solvation and entropic effects. These effects are expected to have broad implications for the in vivo functioning of biomolecules. This work is a first step towards physically realistic in silico whole-cell models that connect molecular with cellular biology.
Residue-level coarse-grained (CG) molecular dynamics (MD) simulation is widely used to investigate slow biological processes that involve multiple proteins, nucleic acids, and their complexes. ...Biomolecules in a large simulation system are distributed non-uniformly, limiting computational efficiency with conventional methods. Here, we develop a hierarchical domain decomposition scheme with dynamic load balancing for heterogeneous biomolecular systems to keep computational efficiency even after drastic changes in particle distribution. These schemes are applied to the dynamics of intrinsically disordered protein (IDP) droplets. During the fusion of two droplets, we find that the changes in droplet shape correlate with the mixing of IDP chains. Additionally, we simulate large systems with multiple IDP droplets, achieving simulation sizes comparable to those observed in microscopy. In our MD simulations, we directly observe Ostwald ripening, a phenomenon where small droplets dissolve and their molecules redeposit into larger droplets. These methods have been implemented in CGDYN of the GENESIS software, offering a tool for investigating mesoscopic biological processes using the residue-level CG models.
Sarcoplasmic reticulum (SR) Ca2+-ATPase transports two Ca2+ ions from the cytoplasm to the SR lumen against a large concentration gradient. X-ray crystallography has revealed the atomic structures of ...the protein before and after the dissociation of Ca2+, while biochemical studies have suggested the existence of intermediate states in the transition between E1P⋅ADP⋅2Ca2+ and E2P. Here, we explore the pathway and free energy profile of the transition using atomistic molecular dynamics simulations with the mean-force string method and umbrella sampling. The simulations suggest that a series of structural changes accompany the ordered dissociation of ADP, the A-domain rotation, and the rearrangement of the transmembrane (TM) helices. The luminal gate then opens to release Ca2+ ions toward the SR lumen. Intermediate structures on the pathway are stabilized by transient sidechain interactions between the A- and P-domains. Lipid molecules between TM helices play a key role in the stabilization. Free energy profiles of the transition assuming different protonation states suggest rapid exchanges between Ca2+ ions and protons when the Ca2+ ions are released toward the SR lumen.
Residue-level coarse-grained (CG) models have become one of the most popular tools in biomolecular simulations in the trade-off between modeling accuracy and computational efficiency. To investigate ...large-scale biological phenomena in molecular dynamics (MD) simulations with CG models, unified treatments of proteins and nucleic acids, as well as efficient parallel computations, are indispensable. In the GENESIS MD software, we implement several residue-level CG models, covering structure-based and context-based potentials for both well-folded biomolecules and intrinsically disordered regions. An amino acid residue in protein is represented as a single CG particle centered at the Cα atom position, while a nucleotide in RNA or DNA is modeled with three beads. Then, a single CG particle represents around ten heavy atoms in both proteins and nucleic acids. The input data in CG MD simulations are treated as GROMACS-style input files generated from a newly developed toolbox, GENESIS-CG-tool. To optimize the performance in CG MD simulations, we utilize multiple neighbor lists, each of which is attached to a different nonbonded interaction potential in the cell-linked list method. We found that random number generations for Gaussian distributions in the Langevin thermostat are one of the bottlenecks in CG MD simulations. Therefore, we parallelize the computations with message-passing-interface (MPI) to improve the performance on PC clusters or supercomputers. We simulate Herpes simplex virus (HSV) type 2 B-capsid and chromatin models containing more than 1,000 nucleosomes in GENESIS as examples of large-scale biomolecular simulations with residue-level CG models. This framework extends accessible spatial and temporal scales by multi-scale simulations to study biologically relevant phenomena, such as genome-scale chromatin folding or phase-separated membrane-less condensations.
The growing interest in the complexity of biological interactions is continuously driving the need to increase system size in biophysical simulations, requiring not only powerful and advanced ...hardware but adaptable software that can accommodate a large number of atoms interacting through complex forcefields. To address this, we developed and implemented strategies in the GENESIS molecular dynamics package designed for large numbers of processors. Long‐range electrostatic interactions were parallelized by minimizing the number of processes involved in communication. A novel algorithm was implemented for nonbonded interactions to increase single instruction multiple data (SIMD) performance, reducing memory usage for ultra large systems. Memory usage for neighbor searches in real‐space nonbonded interactions was reduced by approximately 80%, leading to significant speedup. Using experimental data describing physical 3D chromatin interactions, we constructed the first atomistic model of an entire gene locus (GATA4). Taken together, these developments enabled the first billion‐atom simulation of an intact biomolecular complex, achieving scaling to 65,000 processes (130,000 processor cores) with 1 ns/day performance. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.
The first atomistic view of an intact gene (GATA4), based on experimental HiC data, consisting of 83 kilobases of double‐stranded helical DNA wrapped around 427 nucleosomes. Including water molecules and ions, the simulation box contains over 1 billion atoms.
Conventional torsion angle potentials used in molecular dynamics (MD) have a singularity problem when three bonded particles are collinearly aligned. This problem is often encountered in ...coarse-grained (CG) simulations. Here, we propose a new form of the torsion angle potential, which introduces an angle-dependent modulating function. By carefully tuning the parameters for this modulating function, our method can eliminate the problematic angle-dependent singularity while being combined with existing models. As an example, we optimized the modulating function of the torsion angle potential for popular CG models of biomolecules based on the statistics over experimental structures deposited in the Protein Data Bank. By applying our method to designed and natural biomolecules, we show that the new torsion angle potential is able to eliminate the singularity problem while maintaining the structural features in the original models. Furthermore, by comparing our design with previous methods, we found that our new potential has advantages in computational efficiency and numerical stability. We strongly recommend the usage of our new potential in the CG simulations of flexible molecules.
The graphics processing unit (GPU) has become a popular computational platform for molecular dynamics (MD) simulations of biomolecules. A significant speedup in the simulations of small- or ...medium-size systems using only a few computer nodes with a single or multiple GPUs has been reported. Because of GPU memory limitation and slow communication between GPUs on different computer nodes, it is not straightforward to accelerate MD simulations of large biological systems that contain a few million or more atoms on massively parallel supercomputers with GPUs. In this study, we develop a new scheme in our MD software, GENESIS, to reduce the total computational time on such computers. Computationally intensive real-space nonbonded interactions are computed mainly on GPUs in the scheme, while less intensive bonded interactions and communication-intensive reciprocal-space interactions are performed on CPUs. On the basis of the midpoint cell method as a domain decomposition scheme, we invent the single particle interaction list for reducing the GPU memory usage. Since total computational time is limited by the reciprocal-space computation, we utilize the RESPA multiple time-step integration and reduce the CPU resting time by assigning a subset of nonbonded interactions on CPUs as well as on GPUs when the reciprocal-space computation is skipped. We validated our GPU implementations in GENESIS on BPTI and a membrane protein, porin, by MD simulations and an alanine-tripeptide by REMD simulations. Benchmark calculations on TSUBAME supercomputer showed that an MD simulation of a million atoms system was scalable up to 256 computer nodes with GPUs.
Multistate Bennett acceptance ratio (MBAR) works as a method to analyze molecular dynamics (MD) simulation data after the simulations have been finished. It is widely used to estimate free-energy ...changes between different states and averaged properties at the states of interest. MBAR allows us to treat a wide range of states from those at different temperature/pressure to those with different model parameters. Due to the broad applicability, the MBAR equations are rather difficult to apply for free-energy calculations using different types of MD simulations including enhanced conformational sampling methods and free-energy perturbation. In this review, we first summarize the basic theory of the MBAR equations and categorize the representative usages into the following four: (i) perturbation, (ii) scaling, (iii) accumulation, and (iv) full potential energy. For each, we explain how to prepare input data using MD simulation trajectories for solving the MBAR equations. MBAR is also useful to estimate reliable free-energy differences using MD trajectories based on a semi-empirical quantum mechanics/molecular mechanics (QM/MM) model and ab initio QM/MM energy calculations on the MD snapshots. We also explain how to use the MBAR software in the GENESIS package, which we call
mbar_analysis
, for the four representative cases. The proposed estimations of free-energy changes and thermodynamic averages are effective and useful for various biomolecular systems.
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