An Integrated Framework Advancing Membrane Protein Modeling and Design

By Rebecca F. Alford, Julia Koehler Leman, Brian D. Weitzner, Amanda M. Duran, Drew C. Tilley, Assaf Elazar, Jeffrey J. Gray. 2015

154 citations recorded by the source corpus.

Membrane proteins are critical functional molecules in the human body, constituting more than 30% of open reading frames in the human genome. Unfortunately, a myriad of difficulties in overexpression and reconstitution into membrane mimetics severely limit our ability to determine their structures. Computational tools are therefore instrumental to membrane protein structure prediction, consequently increasing our understanding of membrane protein function and their role in disease. Here, we describe a general framework facilitating membrane protein modeling and design that combines the scientific principles for membrane protein modeling with the flexible software architecture of Rosetta3. This new framework, called RosettaMP, provides a general membrane representation that interfaces with scoring, conformational sampling, and mutation routines that can be easily combined to create new protocols. To demonstrate the capabilities of this implementation, we developed four proof-of-concept applications for (1) prediction of free energy changes upon mutation; (2) high-resolution structural refinement; (3) protein-protein docking; and (4) assembly of symmetric protein complexes, all in the membrane environment. Preliminary data show that these algorithms can produce meaningful scores and structures. The data also suggest needed improvements to both sampling routines and score functions. Importantly, the applications collectively demonstrate the potential of combining the flexible nature of RosettaMP with the power of Rosetta algorithms to facilitate membrane protein modeling and design.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

The Rosetta all-atom energy function for macromolecular modeling and design (2017, methodology): “36,60,115,116 While these improve structure prediction accuracy, both models require more computation time.”
GPCRM: a homology modeling web service with triple membrane-fitted quality assessment of GPCR models (2018, methodology): “Additionally, due to three different scoring functions (Rosetta, Rosetta-MP, BCL::Score) it is possible to select accurate models for the required purposes: the structure of the binding site, the transmembrane domain or the overall shape of the receptor.”
Computing structure-based lipid accessibility of membrane proteins with mp_lipid_acc in RosettaMP (2016, methodology): “The structures were then cleaned from additional atoms (such as ligands and co-factors), renumbered and the span files were computed as described previously [29] with a fixed membrane thickness of 30 Å.”
Recent advances in de novo protein design: Principles, methods, and applications (2021, methodology): “Accurate multi-pass transmembrane proteins were designed (148) using a recently developed framework for membrane protein modeling (149) (Fig.”
Protein Structure Prediction and Design in a Biologically Realistic Implicit Membrane. (2020, result): “This is because a second set of measurements in OmpLA (37) correlates well with PagP measurements, but not with M07 predictions (56).”
Systematic evaluation of CS‐Rosetta for membrane protein structure prediction with sparse NOE restraints (2017, background): “The rationale behind this second approach is that the Rosetta all-atom energy function for membrane proteins might still leave room for improvement,17,34 so that model selection could benefit from not being limited to energy alone.”