Presentation Open Access

# Automated Optimization Approaches for the CHARMM Lipid Force Field

Krämer, Andreas

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
<dc:contributor>Yu, Yalun</dc:contributor>
<dc:creator>Krämer, Andreas</dc:creator>
<dc:date>2019-12-18</dc:date>
<dc:description>Andreas Krämer (National Institute of Health) and Yalun Yu (University of Maryland) visited the Chodera lab at MSKCC on Dec 17, 2019. Andreas gave a talk about development of an automated optimization approach to improve the CHARMM36 lipid force field using pair-specific LJ parameters done in collaboration with Yalun.

Abstract: The CHARMM36 (C36) lipid force field is well-validated for many properties of monolayers and bilayers and has been cited in over 2000 studies of membrane simulations. Problematic aspects of C36 include its hard-wired fine-tuning to the TIP3P water model as well as intricacies regarding the Lennard-Jones (LJ) interactions. Concretely, the LJ switch distance is inconsistent between the C36 protein and lipid force fields and monolayer surface tensions only agree with experiment when long-range dispersion is incorporated. In contrast, bilayer properties were optimized without long-range dispersion.

To improve this situation, we first explore the water-model dependence and develop an automated optimization approach to better reproduce membrane permeabilities using pair-specific LJ parameters. We then carry out a reweighting-based optimization of the force field to consistently introduce long-range dispersion. The initial results for 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) show that within few iterations the match with experiment is improved for many important lipid properties, such as bilayer and monolayer surface areas, compressibilities, order parameters, and headgroup hydration.</dc:description>
<dc:identifier>https://zenodo.org/record/3583308</dc:identifier>
<dc:identifier>10.5281/zenodo.3583308</dc:identifier>
<dc:identifier>oai:zenodo.org:3583308</dc:identifier>
<dc:relation>url:https://youtu.be/Hsq1nGr_jo8</dc:relation>
<dc:relation>url:https://pubs.acs.org/doi/10.1021/acs.chemrev.8b00486</dc:relation>
<dc:relation>doi:10.1021/acs.jctc.9b00016</dc:relation>
<dc:relation>doi:10.5281/zenodo.3583307</dc:relation>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:subject>CHARMM36</dc:subject>
<dc:subject>lipids</dc:subject>
<dc:subject>force field</dc:subject>
<dc:subject>automated optimization</dc:subject>
<dc:subject>LJ-PME</dc:subject>
<dc:subject>reweighting</dc:subject>
<dc:title>Automated Optimization Approaches for the CHARMM Lipid Force Field</dc:title>
<dc:type>info:eu-repo/semantics/lecture</dc:type>
<dc:type>presentation</dc:type>
</oai_dc:dc>

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