Prediction of amphipathic helix—membrane interactions with Rosetta

Gulsevin, Alican; Meiler, Jens

doi:10.1371/journal.pcbi.1008818

Cited by 8 publications

(5 citation statements)

References 59 publications

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“…Franklin2019 may perform better with the pore since it was parameterized and tested with the pore implemented. On the other hand, franklin2019 may overestimate the favorability of residues being buried in the membrane (Gulsevin & Meiler, 2021 ); therefore, scoring pore‐facing residues as being in an aqueous environment would help compensate for that.…”

Section: Discussionmentioning

confidence: 99%

Modeling membrane geometries implicitly in Rosetta

Woods,

Leman,

Meiler

2024

Protein Science

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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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Section: Discussionmentioning

confidence: 99%

Modeling membrane geometries implicitly in Rosetta

Woods,

Leman,

Meiler

2024

Protein Science

Self Cite

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show abstract

“…Franklin2019 may perform better with the pore since it was parameterized and tested with the pore implemented. On the other hand, franklin2019 may overestimate the favorability of residues being buried in the membrane [61]; therefore, scoring pore-facing residues as being in an aqueous environment would help compensate for that.…”

Section: Discussionmentioning

confidence: 99%

Modeling membrane geometries implicitly in Rosetta

Woods,

Leman,

Meiler

2023

Preprint

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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 MPs 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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“…The heme ligand was docked into the protein and putative metal binding sites were identified. The franklin2019 score function has been found to overestimate peptide depth in the membrane as compared to native conformations, 65 which may explain higher RMSDs in the loop regions in the test set (Figure S4). The score function might also have affected the prediction accuracy of the flexible loops on the extracellular side of the barrel and the linker between the heme c binding site and the barrel.…”

Section: Discussionmentioning

confidence: 99%

Computational structure prediction provides a plausible mechanism for electron transfer by the outer membrane protein Cyc2 from Acidithiobacillus ferrooxidans

2021

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Cyc2 is the key protein in the outer membrane of Acidithiobacillus ferrooxidans that mediates electron transfer between extracellular inorganic iron and the intracellular central metabolism. This cytochrome c is specific for iron and interacts with periplasmic proteins to complete a reversible electron transport chain. A structure of Cyc2 has not yet been characterized experimentally. Here we describe a structural model of Cyc2, and associated proteins, to highlight a plausible mechanism for the ferrous iron electron transfer chain. A comparative modeling protocol specific for trans membrane beta barrel (TMBB) proteins in acidophilic conditions (pH $ 2) was applied to the primary sequence of Cyc2. The proposed structure has three main regimes: Extracellular loops exposed to low-pH conditions, a TMBB, and an N-terminal cytochrome-like region within the periplasmic space. The Cyc2 model was further refined by identifying likely iron and heme docking sites. This represents the first computational model of Cyc2 that accounts for the membrane microenvironment and the acidity in the extracellular matrix. This approach can be used to model other TMBBs which can be critical for chemolithotrophic microbial growth.

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Prediction of amphipathic helix—membrane interactions with Rosetta

Cited by 8 publications

References 59 publications

Modeling membrane geometries implicitly in Rosetta

Modeling membrane geometries implicitly in Rosetta

Modeling membrane geometries implicitly in Rosetta

Computational structure prediction provides a plausible mechanism for electron transfer by the outer membrane protein Cyc2 from Acidithiobacillus ferrooxidans

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