OpenMSCG: A Software Tool for Bottom-Up Coarse-Graining

Abstract

The "bottom-up" approach to coarse-graining, for building accurate and efficient computational models to simulate large-scale and complex phenomena and processes, is an important approach in computational chemistry, biophysics, and materials science. As one example, the Multiscale Coarse-Graining (MS-CG) approach to developing CG models can be rigorously derived using statistical mechanics applied to fine-grained, i.e., all-atom simulation data for a given system. Under a number of circumstances, a systematic procedure, such as MS-CG modeling, is particularly valuable. Here, we present the development of the OpenMSCG software, a modularized open-source software that provides a collection of successful and widely applied bottom-up CG methods, including Boltzmann Inversion (BI), Force-Matching (FM), Ultra-Coarse-Graining (UCG), Relative Entropy Minimization (REM), Essential Dynamics Coarse-Graining (EDCG), and Heterogeneous Elastic Network Modeling (HeteroENM). OpenMSCG is a high-performance and comprehensive toolset that can be used to derive CG models from large-scale fine-grained simulation data in file formats from common molecular dynamics (MD) software packages, such as GROMACS, LAMMPS, and NAMD. OpenMSCG is modularized in the Python programming framework, which allows users to create and customize modeling "recipes" for reproducible results, thus greatly improving the reliability, reproducibility, and sharing of bottom-up CG models and their applications.

Figure 1

Overview of the OpenMSCG software illustrated as a four-layer framework: (1) input, (2) featurization, (3) parameterization, and (4) output layers (from bottom to top).

Figure 2

Illustrations of workflows for the CGFM and CGREM tools: (A) The CGFM tool is used for FM and UCG methods, in which the force-field coefficients are solved from linear regression. (B) The CGREM tool was used for the REM method, in which the force-field coefficients are obtained by iteratively minimizing the relative entropy between all-atom and CG trajectories calculated by the CGDERIV tool.

Figure 3

Example of UCG modeling for the liquid–vapor interface of a methanol droplet. (A) A snapshot from the all-atom MD simulation. (B) A snapshot from the UCG simulation that demonstrates similar liquid–vapor structures. (C) The pair potentials from MS-CG (dashed line) and UCG (solid lines) methods. (D) Radial density profiles originated from the center of the droplet in simulations with all-atom, MS-CG, and UCG models.

Figure 4

Example use of OpenMSCG for methanol–hexane interface. (A) A snapshot of the methanol (green) and hexane (red) interfaces at the CG level. (B) Local density distributions of methanol–methanol (left) and hexane–hexane (right) in the high-density (red circles) and low-density (blue squares) regions. (C) Comparison of density profiles of methanol (left) and hexane (right) from the MS-CG (blue diamonds) and UCG (green plus) models with the reference all-atom structures (red circles).

Figure 5

Example of use of OpenMSCG to optimize HIV-1 CA/SP1 interactions using REM. (A) Schematic of the all-atom HIV-1 CA/SP1 oligomer that is used as a reference to generate the CG model. (B) Profiles depicting parameter changes across iterations during REM. Each color depicts a distinct Gaussian prefactor. (C) Comparison of RDFs for select CG pairs computed from the all-atom reference (blue) and CG model (red).