{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Sahrmann PG"],"funding":["National Institute of General Medical Sciences","NIGMS NIH HHS"],"pagination":["10235-10246"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11604101"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["20(22)"],"pubmed_abstract":["A plethora of key biological events occur at the cellular membrane where the large spatiotemporal scales necessitate dimensionality reduction or coarse-graining approaches over conventional all-atom molecular dynamics simulation. Constructing coarse-grained descriptions of membranes systematically from statistical mechanical principles has largely remained challenging due to the necessity of capturing amphipathic self-assembling behavior in coarse-grained models. We show that bottom-up coarse-grained lipid models can possess metastable morphological behavior and that this potential metastability has ramifications for accurate development and training. We in turn develop a training algorithm which evades metastability issues by linking model training to self-assembling behavior, and demonst"],"journal":["Journal of chemical theory and computation"],"pubmed_title":["Enhancing the Assembly Properties of Bottom-Up Coarse-Grained Phospholipids."],"pmcid":["PMC11604101"],"funding_grant_id":["R01 GM063796","R01GM063796"],"pubmed_authors":["Sahrmann PG","Voth GA"],"additional_accession":[]},"is_claimable":false,"name":"Enhancing the Assembly Properties of Bottom-Up Coarse-Grained Phospholipids.","description":"A plethora of key biological events occur at the cellular membrane where the large spatiotemporal scales necessitate dimensionality reduction or coarse-graining approaches over conventional all-atom molecular dynamics simulation. Constructing coarse-grained descriptions of membranes systematically from statistical mechanical principles has largely remained challenging due to the necessity of capturing amphipathic self-assembling behavior in coarse-grained models. We show that bottom-up coarse-grained lipid models can possess metastable morphological behavior and that this potential metastability has ramifications for accurate development and training. We in turn develop a training algorithm which evades metastability issues by linking model training to self-assembling behavior, and demonst","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Nov","modification":"2026-06-05T12:51:00.658Z","creation":"2026-05-17T03:08:04.553Z"},"accession":"S-EPMC11604101","cross_references":{"pubmed":["39535391"],"doi":["10.1021/acs.jctc.4c00905"]}}