Ab initio protein structure prediction using chunk-TASSER By

We have developed an ab initio protein structure prediction method called chunk-TASSER that uses ab initio folded supersecondary structure chunks of a given target as well as threading templates for obtaining contact potentials and distance restraints. The predicted chunks, selected on the basis of a new fragment comparison method, are folded by a fragment insertion method. Fulllength models are built and refined by the TASSER methodology, which searches conformational space via parallel hyperbolic Monte Carlo. We employ an optimized reduced force field that includes knowledge-based statistical potentials and restraints derived from the chunks as well as threading templates. The method is tested on a dataset of 425 hard target proteins ≤ 250 amino acids (AA) in length. The average TM-scores of the best of top five models per target are 0.266, 0.336 and 0.362 by the threading algorithm SP 3 , original TASSER and chunk-TASSER respectively. For a subset of 80 proteins with predicted α−helix content ≥ 50%, these averages are 0.284, 0.356 and 0.403 respectively. The percentages of proteins with the best of top five models having TM-score ≥ 0.4 (a statistically significant threshold for structural similarity) are 3.76%, 20.94% and 28.94% by SP 3 , TASSER and chunk-TASSER respectively overall, while for the subset of 80 predominantly helical proteins, these percentages are 2.50%, 23.75% and 41.25%. Thus, chunk-TASSER shows a significant improvement over TASSER for modeling hard targets where no good template can be identified. We also tested chunk-TASSER on 21 medium/hard targets less than 200 AA long from CASP7. Chunk-TASSER is about 11% (10%) better than TASSER for the total TM-score of the first (best of top five) models. Chunk-TASSER is fully automated and can be used in proteome scale protein structure prediction.