TASSER: an automated method for the prediction of protein tertiary structures in CASP6

TitleTASSER: an automated method for the prediction of protein tertiary structures in CASP6
Publication TypeJournal Article
Year of Publication2005
AuthorsZhang Y, Arakaki AK, Skolnick J
JournalProteins
Volume61 Suppl 7
Pagination91-8
Date Published2005
ISSN1097-0134
KeywordsAlgorithms, Automation, Computational Biology, Computer Simulation, Computers, Data Interpretation, Statistical, Databases, Protein, Models, Molecular, Monte Carlo Method, Protein Folding, Protein Structure, Secondary, Protein Structure, Tertiary, Proteomics, Reproducibility of Results, Sequence Alignment, Software
Abstract

The recently developed TASSER (Threading/ASSembly/Refinement) method is applied to predict the tertiary structures of all CASP6 targets. TASSER is a hierarchical approach that consists of template identification by the threading program PROSPECTOR_3, followed by tertiary structure assembly via rearranging continuous template fragments. Assembly occurs using parallel hyperbolic Monte Carlo sampling under the guide of an optimized, reduced force field that includes knowledge-based statistical potentials and spatial restraints extracted from threading alignments. Models are automatically selected from the Monte Carlo trajectories in the low-temperature replicas using the clustering program SPICKER. For all 90 CASP targets/domains, PROSPECTOR_3 generates initial alignments with an average root-mean-square deviation (RMSD) to native of 8.4 A with 79% coverage. After TASSER reassembly, the average RMSD decreases to 5.4 A over the same aligned residues; the overall cumulative TM-score increases from 39.44 to 52.53. Despite significant improvements over the PROSPECTOR_3 template alignment observed in all target categories, the overall quality of the final models is essentially dictated by the quality of threading templates: The average TM-scores of TASSER models in the three categories are, respectively, 0.79 [comparative modeling (CM), 43 targets/domains], 0.47 [fold recognition (FR), 37 targets/domains], and 0.30 [new fold (NF), 10 targets/domains]. This highlights the need to develop novel (or improved) approaches to identify very distant targets as well as better NF algorithms.

Alternate JournalProteins: Structure, Function and Bioinformatics

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