A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields.

Riccardo Capelli,Giovanni Doni,Charly Empereur-Mot,Giovanni M Pavan,Andrea Gardin

doi:10.1021/acs.jpcb.1c02503

Abstract

Molecular dynamics simulations of all-atom and coarse-grained lipid bilayer models are increasingly used to obtain useful insights for understanding the structural dynamics of these assemblies. In this context, one crucial point concerns the comparison of the performance and accuracy of classical force fields (FFs), which sometimes remains elusive. To date, the assessments performed on different classical potentials are mostly based on the comparison with experimental observables, which typically regard average properties. However, local differences of the structure and dynamics, which are poorly captured by average measurements, can make a difference, but these are nontrivial to catch. Here, we propose an agnostic way to compare different FFs at different resolutions (atomistic, united-atom, and coarse-grained), by means of a high-dimensional similarity metrics built on the framework of Smooth Overlap of Atomic Position (SOAP). We compare and classify a set of 13 FFs, modeling 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayers. Our SOAP kernel-based metrics allows us to compare, discriminate, and correlate different FFs at different model resolutions in an unbiased, high-dimensional way. This also captures differences between FFs in modeling nonaverage events (originating from local transitions), for example, the liquid-to-gel phase transition in dipalmitoylphosphatidylcholine (DPPC) bilayers, for which our metrics allows us to identify nucleation centers for the phase transition, highlighting some intrinsic resolution limitations in implicit versus explicit solvent FFs.

Highlights

Lipid membranes are ubiquitous in biological systems, and their chemical and mechanical characteristics directly impact the regulation of the cell machinery.[1]
We performed 1 μs of molecular dynamics (MD) simulation using GROMACS 2018.652 patched with PLUMED53,54
We presented a data-driven dimensionality reduction approach based on a metrics coming from the Smooth Overlap of Atomic Position (SOAP) framework which is able to quantify the similarity between force fields (FFs) at different levels of resolution

Summary

Introduction

Lipid membranes are ubiquitous in biological systems, and their chemical and mechanical characteristics directly impact the regulation of the cell machinery.[1]. A plethora of experimental techniques such as NMR,[5] calorimetry,[6] SANS,[7] and SAXS8 have been applied to lipid bilayers to obtain average structural and dynamic information at different resolutions. This large amount of experimental data paved the way to the creation and the cross-validation of reliable models that can be simulated by means of molecular dynamics (MD). Comparing various FFs becomes awkward when different representations/resolutions in the modeling of the chemical system are employed and when the internal dynamic organization of the membrane, its uniformity/non-uniformity, and (local) dynamic fluctuations become important

Methods

Results

Conclusion