GENESIS: a hybrid‐parallel and multi‐scale molecular dynamics simulator with enhanced sampling algorithms for biomolecular and cellular simulations

Abstract

GENESIS (Generalized-Ensemble Simulation System) is a new software package for molecular dynamics (MD) simulations of macromolecules. It has two MD simulators, called ATDYN and SPDYN. ATDYN is parallelized based on an atomic decomposition algorithm for the simulations of all-atom force-field models as well as coarse-grained Go-like models. SPDYN is highly parallelized based on a domain decomposition scheme, allowing large-scale MD simulations on supercomputers. Hybrid schemes combining OpenMP and MPI are used in both simulators to target modern multicore computer architectures. Key advantages of GENESIS are (1) the highly parallel performance of SPDYN for very large biological systems consisting of more than one million atoms and (2) the availability of various REMD algorithms (T-REMD, REUS, multi-dimensional REMD for both all-atom and Go-like models under the NVT, NPT, NPAT, and NPγT ensembles). The former is achieved by a combination of the midpoint cell method and the efficient three-dimensional Fast Fourier Transform algorithm, where the domain decomposition space is shared in real-space and reciprocal-space calculations. Other features in SPDYN, such as avoiding concurrent memory access, reducing communication times, and usage of parallel input/output files, also contribute to the performance. We show the REMD simulation results of a mixed (POPC/DMPC) lipid bilayer as a real application using GENESIS. GENESIS is released as free software under the GPLv2 licence and can be easily modified for the development of new algorithms and molecular models. WIREs Comput Mol Sci 2015, 5:310–323. doi: 10.1002/wcms.1220

Biomolecular interactions modulate macromolecular structure and dynamics in atomistic model of a bacterial cytoplasm

“…For MG h , we performed 20 ns MD simulation with the first 5 ns considered as equilibration. MG m1 and MG h trajectories were obtained with GENESIS (Jung et al, 2015) on the K computer. MG m2 was run as a control using NAMD 2.9 (Phillips et al, 2005).…”

Molecular dynamics simulations of biological membranes and membrane proteins using enhanced conformational sampling algorithms

“…The γ-REMD method induced reorientation of WALP23 due to protein–lipid interactions, resulting in more accurate free energy profile than in NPT MD simulations. They further performed γT-REMD, γ-REMD, and T-REMD simulations of mixed POPC/DMPC lipids, whose results are described below in detail in terms of the effect of surface tension on membrane structures [87]. …”

“…Mori et al simulated mixed POPC/DMPC bilayers by using γT-REMD, T-REMD, γ-REMD, and MD simulations, and examined the effect of the surface tension on the membrane structures with multiple components [87]. In their simulations, they observed that some replicas in γT-REMD showed accelerated mixing of two lipids compared to MD (Fig.…”

“…To illustrate the accuracy and efficiency of different molecular models and enhanced sampling algorithms, we review recent simulations of glycophorin A (GpA) [72-75], phospholamban (PLN) [76-80], amyloid precursor protein (APP) [81-84], and mixed lipid bilayers [85-87]. In the simulations of GpA, PLN, and APP, REMD and REUS using implicit membrane models were mostly used to determine their conformational ensembles in membranes.…”

Hybrid All-Atom/Coarse-Grained Simulations of Proteins by Direct Coupling of CHARMM and PRIMO Force Fields

“…As noted before, this is a consequence of the relatively high cost of the GBMV implicit solvent model that is used here. 81 Explicit solvent simulations further benefit from better parallelization schemes 82, 83 and GPU acceleration 84–86 . However, GPU acceleration is also possible for GB models 87 and the replacement of the very expensive GBMV model by better-performing alternative GB implementations and other implicit solvent models, especially when combined with PRIMO or the PRIMO part of an AA/CG model remains a promising avenue for increased computational efficiency.…”