Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework

Abstract

The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. Here, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (∼98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ∼1.5 million atoms is discussed in the context of MuMMI.

Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure

“…For the two feedback regions used in the current study, the RAF CRD loops region changes in β-strands length upon membrane insertion, but the change stabilizes and remains constant through the rest of the simulation campaign, as shown in López et al For the RAS HVR region, however, the RAS helix 5 shrinks by an average of 2.2 residues before feedback and 1.9 residues afterit does not exhibit a stable single value during this simulation campaign (Figure S6). The continued helix shortening is consistent with recent NMR backbone chemical shifts experiments, indicating RAS helix 5 preference to end at residue 170 . Nevertheless, as the helix length has not stabilized in these simulations, we cannot rule out a potential bias in the feedback-backmapping protocol.…”

“…The AA scale is used for single RAS-RBDCRD simulations to capture lipid-dependent structural changes in secondary structures, which are used to update the CG model. The AA models and parameters are described in previous publications. , …”

“…The selected CG frames are then transformed to initial atomistic structures for AA simulations by the MuMMI Backmapping module and an updated CG-to-AA conversion tool called sinceCG with ∼98% success rate per iterative attempt . This tool, which is a modified version of the backward method, converts selected snapshots from Martini CG simulations to CHARMM36 AA model.…”

“…The finest scale currently supported by MuMMI is AA resolution using the CHARMM36 force field. , Snapshots from all CG simulations are evaluated (sampled every 2 ns), and selected snapshots are extracted and converted to the AA representation using a modified backward tool, , followed by energy minimization using GROMACS, and format conversion using ParmEd . A description of the MuMMI AA modules and a complete protocol of this backmapping procedure are given by López et al The 30 × 30 nm 2 AA representations are then evolved on a single-GPU using AMBER18 . AA simulations comprise, on average, ∼1.6 M atoms and run at ∼14 ns per day per NVIDIA V100 GPU.…”

Molecular dynamics simulation of an entire cell

“…These could be straightforwardly obtained from backmapping representative regions taken from the whole-cell CG model. A number of such backmapping tools, optimized for Martini, already exist ( Louison et al, 2021 ; Vickery and Stansfeld, 2021 ; López et al, 2022 ).…”