This Article Full Text Full Text (PDF) All Versions of this Article: biophysj.107.109959v1 93/5/1510    most recent Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Zhou, H. Articles by Skolnick, J. Search for Related Content PubMed PubMed Citation Articles by Zhou, H. Articles by Skolnick, J.

Ab Initio Protein Structure Prediction Using Chunk-TASSER

Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia

Correspondence: Address reprint requests to Jeffrey Skolnick, E-mail: skolnick{at}gatech.edu.

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. Full-length 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 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 SP³, original TASSER and chunk-TASSER, respectively. For a subset of 80 proteins with predicted -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 SP³, 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 <200 amino-acids-long from CASP7. Chunk-TASSER is 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.

This article has been cited by other articles:

				S. Y. Lee and J. Skolnick Benchmarking of TASSER_2.0: An Improved Protein Structure Prediction Algorithm with More Accurate Predicted Contact Restraints Biophys. J., August 15, 2008; 95(4): 1956 - 1964. [Abstract] [Full Text] [PDF]

				B. Shen, J. Bai, and M. Vihinen Physicochemical feature-based classification of amino acid mutations Protein Eng. Des. Sel., January 1, 2008; 21(1): 37 - 44. [Abstract] [Full Text] [PDF]