Multiscale Factorization Method for Simulating Mesoscopic Systems with Atomic Precision

2015

Computation

Abstract:Many mesoscopic N -atom systems derive their structural and dynamical properties from processes coupled across multiple scales in space and time. That is, they simultaneously deform or display collective behaviors, while experiencing atomic scale vibrations and collisions. Due to the large number of atoms involved and the need to simulate over long time periods of biological interest, traditional computational tools, like molecular dynamics, are often infeasible for such systems. Hence, in the current review article, we present and discuss two recent multiscale methods, stemming from the N -atom formulation and an underlying scale separation, that can be used to study such systems in a friction-dominated regime: multiscale perturbation theory and multiscale factorization. These novel analytic foundations provide a self-consistent approach to yield accurate and feasible long-time simulations with atomic detail for a variety of multiscale phenomena, such as viral structural transitions and macromolecular self-assembly. As such, the accuracy and efficiency of the associated algorithms are demonstrated for a few representative biological systems, including satellite tobacco mosaic virus (STMV) and lactoferrin.

Section: Demonstration Systems and Discussionmentioning

confidence: 99%

A Review of Two Multiscale Methods for the Simulation of Macromolecular Assemblies: Multiscale Perturbation and Multiscale Factorization

Pankavich

2015

Computation

“…However, this expanded representation facilitates theoretical developments and an associated conceptual picture, which ultimately imply efficient and accurate computational algorithms. This extended description introduces approximations that enter only via well-founded multiscale perturbation [25,26,11,13] and Trotter factorization [14,27] methods. Besides the computational reduction, there are several advantages to our approach.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…Rather than attempting to untangle the collective and fast particle modes directly, a Trotter factorization strategy is used [28]. In this approach, the untangling of the collective and fast-atom modes is achieved via an alternating stepping evolution procedure [14,27]. In each step, the collective and fast-particle modes are updated: the former via the collective integration of the momenta constructed from the MD phase, and the latter by conventional MD.…”

Section: Multiscale Factorizationmentioning

confidence: 99%

“…These methods can be generally divided into two classes. The first is based on coevolving both the atomistic and CG states simultaneously [10,11,12,13,14]; in these methods, the atomic resolution is never lost. The second class coarse-grains the atomistic structure and evolves the CG variables based on dynamical equations that depend on parameters to be determined [15,16,17,18,19,20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ProtoMD: A prototyping toolkit for multiscale molecular dynamics

Somogyi

Mansour

Computer Physics Communications

2016

Self Cite

ProtoMD is a toolkit that facilitates the development of algorithms for multiscale molecular dynamics (MD) simulations. It is designed for multiscale methods which capture the dynamic transfer of information across multiple spatial scales, such as the atomic to the mesoscopic scale, via coevolving microscopic and coarse-grained (CG) variables. ProtoMD can be also be used to Licensing provisions: GPL v3 (or above)Programming language: python 2.7.3Computer: x86 / x86 64Operating system: Linux RAM: Depends on the size of the system being simulated and duration of the simulation (few MBs to TBs)Number of processors used: 12 -128Supplementary material:Keywords: python, molecular dynamics, multiscale, coarse-graining Classification:Computational Methods External routines/libraries:GROMACS, MDAnalysis, GromacsWrapper, numpy, scipySubprograms used:Nature of problem:Prototyping multiscale coarse-grained algorithms for molecular dynamics Solution method:Combining the open-source GROMACS molecular dynamics package and the pythonbased MDAnalysis library for running, debugging, and analyzing multiscale simulations Restrictions:The system under study must be characterized by a clear separation of timescales; otherwise, the multiscale algorithm fails to capture the slowly-varying modes. Unusual features:Additional comments:Running time:Depending on the problem size, simulations can take few hours to months.

“…[2][3][4]18,19 Furthermore, the MD phase of the multiscale computation should be sufficiently long to generate a representative ensemble of fluctuations in the CG momenta (i.e. longer than the 'stationarity time' 16 ). To complete the multiscale cycle, a microstate consistent with the updated CG variables must be constructed before the MD phase of the computation is resumed.…”

Section: Introductionmentioning

confidence: 99%

Reverse Coarse-Graining for Equation-Free Modeling: Application to Multiscale Molecular Dynamics

Mansour

J. Chem. Theory Comput.

2016

Self Cite

Constructing atom-resolved states from low-resolution data is of practical importance in many areas of science and engineering. This problem is addressed in this paper in the context of multiscale factorization methods for molecular dynamics. These methods capture the crosstalk between atomic and coarse-grained scales arising in macromolecular systems. This crosstalk is accounted for by Trotter factorization, which is used to separate the all-atom from the coarse-grained phases of the computation. In this approach, short molecular dynamics runs are used to advance in time the coarse-grained variables, which in turn guide the all-atom state. To achieve this coevolution, an all-atom microstate consistent with the updated coarse-grained variables must be recovered. This recovery is cast here as a non-linear optimization problem that is solved with a quasi-Newton method. The approach yields a Boltzmann-relevant microstate whose coarse-grained representation and some of its fine-scale features are preserved.Embedding this algorithm in multiscale factorization is shown to be accurate and scalable for simulating proteins and their assemblies.