CHARMM-GUI Bicelle Builder: An Extension of Membrane Builder for Modeling and Simulation of Bicelle Systems

Seonghan Kim, Nathan R. Kern, Lauren Paolucci, Alan Jaemin Jo, Christopher P. Baryiames, D. Peter Tieleman, Linda Columbus, Wonpil Im

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Membrane mimetics, such as detergent micelles, nanodiscs, and amphipol complexes, which can provide membrane-like environments while retaining small and soluble features, have been utilized to study membrane proteins. A bicelle, composed of varying lipids and detergents, is a useful membrane mimetic because the lipid-to-detergent ratio, the q-value, can be adjusted to alter the properties of the aggregate, including the thickness and size of the bicelle. However, building a bicelle model for modeling and simulation studies requires nontrivial efforts, even for experts. We introduce CHARMM-GUI Bicelle Builder, a web-based platform that can generate various all-atom bicelle systems via a graphical user interface with all available lipids and detergents in Membrane Builder. To illustrate and validate Bicelle Builder with practical systems, we have modeled and simulated pure bicelles consisting of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) lipids with 1,2-dihexanoyl-sn-glycero-3-phosphocholine (C6DHPC) detergents and protein-bicelle complexes, composed of DMPC with C6DHPC, foscholine-10 (FOS10), and lysophosphatidylcholine-12 (LPC12) detergents. Our simulation results indicate that Bicelle Builder can generate reliable and robust bicelle models with and without proteins that retain DMPC bilayer characteristics. Bicelle Builder is expected to help researchers better understand not only bicelles themselves but also atomistic-level structures of protein-bicelle complexes that are often difficult to access through experimental approaches.

Original languageEnglish
Pages (from-to)7081-7088
Number of pages8
JournalJournal of Chemical Information and Modeling
Volume65
Issue number13
DOIs
StatePublished - Jul 14 2025

Funding

This work was supported in part by NIH R35 GM153458, MCB-1810695 (to W.I.), and NSF MCB 1817735 (L.C.). D.P.T. acknowledges support from the Natural Sciences and Engineering Research Council (Canada), the Canada Research Chairs program, and the Digital Research Alliance of Canada.

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