ArticleProtein science : a publication of the Protein Society2024
Modeling membrane geometries implicitly in Rosetta.
Article in Protein science : a publication of the Protein Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The Shape of Things to Come: α-Helical Membrane Protein Folding on the Ribosome.Chemical reviews · 2026Review
- Chemosensory Receptors in Vertebrates: Structure and Computational Modeling Insights.International journal of molecular sciences · 2025Review
- Computational design of de novo bioenergetic membrane proteins.Biochemical Society transactions · 2024Review
- Modeling membrane geometries implicitly in Rosetta.Protein science : a publication of the Protein Society · 2024Article
- ProteinMPNN Recovers Complex Sequence Properties of Transmembrane β-barrels.bioRxiv : the preprint server for biology · 2024Article
- Implicit model to capture electrostatic features of membrane environment.PLoS computational biology · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
Interactions between membrane proteins (MPs) and lipid bilayers are critical for many cellular functions. In the Rosetta molecular modeling suite, the implicit membrane energy function is based on a "slab" model, which represent the membrane as a flat bilayer. However, in nature membranes often have a curvature that is important for function and/or stability. Even more prevalent, in structural biology research MPs are reconstituted in model membrane systems such as micelles, bicelles, nanodiscs, or liposomes. Thus, we have modified the existing membrane energy potentials within the RosettaMP framework to allow users to model MPs in different membrane geometries. We show that these modifications can be utilized in core applications within Rosetta such as structure refinement, protein-protein docking, and protein design. For MP structures found in curved membranes, refining these structures in curved, implicit membranes produces higher quality models with structures closer to experimentally determined structures. For MP systems embedded in multiple membranes, representing both membranes results in more favorable scores compared to only representing one of the membranes. Modeling MPs in geometries mimicking the membrane model system used in structure determination can improve model quality and model discrimination.
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Registered trials
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