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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
Backrub-like backbone simulation recapitulates natural protein conformational variability and improves mutant
Colin A Smith1, Tanja Kortemme
1Graduate Program in Biological and Medical Informatics, University of California, San Francisco, CA 94158, USA. colin.smith@ucsf.edu
Journal of Molecular Biology
|June 13, 2008
Summary
A new "backrub" method improves protein modeling by simulating localized backbone movements. This approach enhances the accuracy of protein design and simulations of protein flexibility.
Area of Science:
- Computational biology
- Structural biology
- Biophysics
Background:
- Accurate computational protein modeling requires effective backbone sampling.
- Localized, hinge-like backbone conformations are observed in crystal structures.
- A new "backrub" model describes these localized conformational changes.
Purpose of the Study:
- To implement and evaluate a backrub-inspired sampling method in Rosetta.
- To assess the impact of this method on protein simulation and design accuracy.
Main Methods:
- Implemented a backrub sampling method in the Rosetta program.
- Evaluated the method using crystal structure correlations, point-mutant predictions, and loop flexibility simulations.
- Focused on internal backbone rotations about axes between C-alpha atoms.
Main Results:
- Rosetta backrub simulations successfully reproduced backbone-side chain conformation correlations from crystal structures.
- Backbone flexibility sampling improved the accuracy of predicting point-mutant side-chain conformations.
- Backrub sampling captured the open-closed state oscillations of triosephosphate isomerase loop 6.
Conclusions:
- The backrub sampling method effectively captures localized conformational changes in proteins.
- This model of backbone motion can significantly enhance protein design and atomistic simulations.
- Incorporating backrub dynamics improves the fidelity of computational protein modeling.
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