Methods / Servers
RosettaMP - mp_domain_assembly
- first tool to create full-length models of membrane proteins from known domain structures
- full-length models can be used to create hypotheses of how these proteins function
- requires the sequence of the full-length construct and structures or models of individual domains (extracellular, transmembrane, intracellular) that are assembled into full-length models with linkers in between
- depending on the length of the linkers, incorporation of experimental data might be required to build high-quality models
- please cite
- first tool to classify lipid accessibility from the protein structure
- input structure needs to be transformed into membrane coordinates, so use structures from PDBTM or OPM database
- please cite
- Koehler Leman, J, Lyskov, S. and Bonneau, R., Computing structure-based lipid accessibility of membrane proteins with mp_lipid_acc in RosettaMP, BMC Bioinformatics, vol. 18, no. 1, p. 115, Dec. 2017.
RosettaMP - toolkit
- small, yet useful tools for membrane protein modeling
- this includes
- de novo modeling of single transmembrane span helices
- transformation of a protein into the membrane coordinate frame
- creating a Rosetta spanfile for membrane protein modeling
- making mutations with different levels of backbone and side-chain flexibility
- scoring a protein with the membrane scoring function
- visualizing the protein in and with the membrane in PyMOL
- please cite
- general framework for membrane protein modeling in the Rosetta software suite
- can be combined with existing Rosetta applications to create new methods for membrane protein modeling, docking, and design
- proof-of-concept applications implemented for protein-protein docking, high-resolution refinement, ddG prediction, and symmetric assembly, all in the membrane environment
- please cite:
- Alford, R. F., Koehler Leman, J., Weitzner, B. D., Duran, A. M., Tilley, D. C., Elazar, A. & Gray, J. J. An Integrated Framework Advancing Membrane Protein Modeling and Design. PLoS Comput. Biol. 11, e1004398 (2015).
- Prediction of Secondary Structure and Trans-Membrane Spans in Proteins
- prediction accuracies are higher or comparable to state-of-the-art methods, such as PsiPred, Octopus, and beta-barrel TM predictors
- sequence-based prediction based on Artificial Neural Network
- please cite: