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

被引:107
作者
Zhang, Y [1 ]
Arakaki, AK [1 ]
Skolnick, JR [1 ]
机构
[1] SUNY Buffalo, Ctr Excellence Bioinformat, Buffalo, NY 14203 USA
关键词
comparative modeling; threading; ab initio; prediction; TASSER; PROSPECTOR_3;
D O I
10.1002/prot.20724
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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 angstrom with 79% coverage. After TASSER reassembly, the average RMSD decreases to 5.4 angstrom 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.
引用
收藏
页码:91 / 98
页数:8
相关论文
共 32 条
  • [1] Large-scale assessment of the utility of low-resolution protein structures for biochemical function assignment
    Arakaki, AK
    Zhang, Y
    Skolnick, J
    [J]. BIOINFORMATICS, 2004, 20 (07) : 1087 - 1096
  • [2] Protein structure prediction and structural genomics
    Baker, D
    Sali, A
    [J]. SCIENCE, 2001, 294 (5540) : 93 - 96
  • [3] The Protein Data Bank
    Berman, HM
    Westbrook, J
    Feng, Z
    Gilliland, G
    Bhat, TN
    Weissig, H
    Shindyalov, IN
    Bourne, PE
    [J]. NUCLEIC ACIDS RESEARCH, 2000, 28 (01) : 235 - 242
  • [4] A METHOD TO IDENTIFY PROTEIN SEQUENCES THAT FOLD INTO A KNOWN 3-DIMENSIONAL STRUCTURE
    BOWIE, JU
    LUTHY, R
    EISENBERG, D
    [J]. SCIENCE, 1991, 253 (5016) : 164 - 170
  • [5] Feig M, 2000, PROTEINS, V41, P86, DOI 10.1002/1097-0134(20001001)41:1<86::AID-PROT110>3.0.CO
  • [6] 2-Y
  • [7] 3D-SHOTGUN: A novel, cooperative, fold-recognition meta-predictor
    Fischer, D
    [J]. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, 2003, 51 (03) : 434 - 441
  • [8] Protein structure prediction of CASP5 comparative modeling and fold recognition targets using consensus alignment approach and 3D assessment
    Ginalski, K
    Rychlewski, L
    [J]. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, 2003, 53 : 410 - 417
  • [9] 3D-Jury: a simple approach to improve protein structure predictions
    Ginalski, K
    Elofsson, A
    Fischer, D
    Rychlewski, L
    [J]. BIOINFORMATICS, 2003, 19 (08) : 1015 - 1018
  • [10] Protein secondary structure prediction based on position-specific scoring matrices
    Jones, DT
    [J]. JOURNAL OF MOLECULAR BIOLOGY, 1999, 292 (02) : 195 - 202