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

[Image: see text] 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 accura...

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Autores principales: Capelli, Riccardo, Gardin, Andrea, Empereur-mot, Charly, Doni, Giovanni, Pavan, Giovanni M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2021
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8311647/
https://www.ncbi.nlm.nih.gov/pubmed/34254518
http://dx.doi.org/10.1021/acs.jpcb.1c02503
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author Capelli, Riccardo
Gardin, Andrea
Empereur-mot, Charly
Doni, Giovanni
Pavan, Giovanni M.
author_facet Capelli, Riccardo
Gardin, Andrea
Empereur-mot, Charly
Doni, Giovanni
Pavan, Giovanni M.
author_sort Capelli, Riccardo
collection PubMed
description [Image: see text] 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.
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spelling pubmed-83116472021-07-27 A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields Capelli, Riccardo Gardin, Andrea Empereur-mot, Charly Doni, Giovanni Pavan, Giovanni M. J Phys Chem B [Image: see text] 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. American Chemical Society 2021-07-13 2021-07-22 /pmc/articles/PMC8311647/ /pubmed/34254518 http://dx.doi.org/10.1021/acs.jpcb.1c02503 Text en © 2021 The Authors. Published by American Chemical Society Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Capelli, Riccardo
Gardin, Andrea
Empereur-mot, Charly
Doni, Giovanni
Pavan, Giovanni M.
A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields
title A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields
title_full A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields
title_fullStr A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields
title_full_unstemmed A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields
title_short A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields
title_sort data-driven dimensionality reduction approach to compare and classify lipid force fields
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8311647/
https://www.ncbi.nlm.nih.gov/pubmed/34254518
http://dx.doi.org/10.1021/acs.jpcb.1c02503
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