Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142809
Title: Magic v.3 : an integrated software package for systematic structure-based coarse-graining
Authors: Mirzoev, Alexander
Nordenskiöld, Lars
Lyubartsev, Alexander
Keywords: Science::Biological sciences
Issue Date: 2018
Source: Mirzoev, A., Nordenskiöld, L., & Lyubartsev, A. (2019). Magic v.3 : an integrated software package for systematic structure-based coarse-graining. Computer Physics Communications, 237, 263-273. doi:10.1016/j.cpc.2018.11.018
Project: MOE2014-T2-1-123 (ARC51/14)
MOE2012-T3-1-001
Journal: Computer Physics Communications
Abstract: Molecular simulations of many phenomena related to biomolecular systems, soft matter and nanomaterials require consideration of length scales above 10 nm and time scales longer than 1μs, which necessitates the use of coarse-grained (low resolution) models, where each site of the model represents a group of atoms, and where the solvent is often omitted. Our software package MagiC is designed to perform systematic structure-based coarse-graining of molecular models, in which the effective pairwise potentials between coarse-grained sites of low-resolution molecular models are constructed to reproduce structural distribution functions obtained from modeling of systems in a high resolution (atomistic) description. The software takes as input atomistic trajectories generated by an external molecular dynamics package, and produce as an output interaction potentials for coarse-grained models which can be directly used in a coarse-grained simulations package. Here we present a major update (v.3) of the software with substantially improved functionality, compatibility with several major atomistic and coarse-grained simulations packages (GROMACS, LAMMPS, GALAMOST), analysis suite with graphical possibilities, diagnostics, documentation. We describe briefly the coarse-graining methodology, the structure of the software, describe users actions, and illustrate the whole process with two complex examples: cholesterol containing lipid bilayers and condensation of DNA caused by multivalent ions. Program summary: Program Title: MagiC Program Files doi: http://dx.doi.org/10.17632/9gnfxyshj8.1 Licensing provisions: GPLv3 Programming language: Fortran, Python Nature of problem: Systematic bottom-up multiscale modeling is a complex multi-stage process, in which results of simulations of a high-resolution (atomistic) model are used to construct a low resolution (coarse-grained) model, providing the same structural properties for the coarse-grained system as for the high-resolution system. Within the approach, structural properties of the high-resolution model are computed in terms of radial distribution functions and distributions of intramolecular degrees of freedom. Then the inverse problem is solved, in which the interaction potentials for the low-resolution model are determined which provide distribution functions coinciding with those obtained in the high-resolution simulations. The low-resolution model can be then used for simulations of the same system on larger length and time-scale. Solution method: The presented software package implements all stages of the systematic structure-based coarse-graining. It works as an integrated pipeline, giving the user ability to easily derive a coarse-grained model for a multicomponent complex molecular system and then use it for large-scale simulations. MagiC implements two approaches to solve the inverse problem: (i) the Inverse Monte Carlo method in which the inverse problem is solved using the Newton–Raphson method, with inversion of the Jacobian for the discretized relationship between interaction potentials and structural distribution functions; and (ii) the Iterative Boltzmann approach in which the inverse problem is solved using approximative relationships neglecting correlations between different degrees of freedom. The inverse solver includes also variational Inverse Monte Carlo approach when some of the coarse-grained potentials are fixed while others vary in order to fit the whole set of reference distribution functions. Additional comments: The MagiC main module can be also used for conventional Monte Carlo simulations of molecular systems described by tabulated pairwise potentials in the canonical ensemble.
URI: https://hdl.handle.net/10356/142809
ISSN: 0010-4655
DOI: 10.1016/j.cpc.2018.11.018
Rights: © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-ncnd/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SBS Journal Articles

Files in This Item:
File Description SizeFormat 
1-s2.0-S0010465518304107-main.pdf3.71 MBAdobe PDFView/Open

SCOPUSTM   
Citations 20

5
Updated on Mar 10, 2021

PublonsTM
Citations 20

5
Updated on Mar 4, 2021

Page view(s)

149
Updated on May 16, 2022

Download(s) 20

157
Updated on May 16, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.