With some simplifications, computational protein folding can be understood as an optimization problem of a potential energy function on a variable space consisting of all conformation for a given protein molecule. It is well known that realistic energy potentials are very "rough" functions, when expressed in the standard variables, and the folding trajectories can be easily trapped in multiple local minima. We have integrated our variation of Parallel Tempering optimization into the protein folding program Rosetta in order to improve its capability to overcome energy barriers and estimate how such improvement will influence the quality of the folded protein domains. Here we report that (1) Parallel Tempering Rosetta (PTR) is significantly better in the exploration of protein structures than previous implementations of the program; (2) systematic improvements are observed across a large benchmark set in the parameters that are normally followed to estimate robustness of the folding; (3) these improvements are most dramatic in the subset of the shortest domains, where high-quality structures have been obtained for >75% of all tested sequences. Further analysis of the results will improve our understanding of protein conformational space and lead to new improvements in the protein folding methodology, while the current PTR implementation should be very efficient for short (up to approximately 80 a.a.) protein domains and therefore may find practical application in system biology studies.
|Original language||English (US)|
|Number of pages||7|
|Journal||Computational systems bioinformatics / Life Sciences Society. Computational Systems Bioinformatics Conference|
|State||Published - 2008|
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