TASSER: An automated method for the prediction of protein tertiary structures in CASP6
TASSER: An automated method for the prediction of protein tertiary structures in CASP6
复制标题
DOI：
10.1002/prot.20724
复制
发表时间：
2005-01-01
影响因子：
2.9
通讯作者：
Skolnick, JR
中科院分区：
文献类型：
--
作者：
Zhang, Y;Arakaki, AK;Skolnick, JR
最近开发的TASSER（Threading/ASSSEMBLY/Refinement）方法被应用于预测所有CASP 6目标的三级结构。TASSER是一种分层方法，包括通过线程程序PROSPECTOR_3进行模板识别，然后通过重排连续模板片段进行三级结构组装。组装使用并行双曲线蒙特卡罗采样的指导下，优化，减少力场，包括基于知识的统计潜力和空间限制提取线程对齐。使用聚类程序SPICKER从低温副本中的Monte Carlo轨迹中自动选择模型。对于所有90个CASP靶标/结构域，PROSPECTOR_3生成初始比对，其平均均方根偏差（RMSD）为8.4埃，覆盖率为79%。TASSER重组后，相同比对残基的平均RMSD降低至5.4埃;总体累积TM评分从39.44增加至52.53。尽管在所有目标类别中观察到的PROSPECTOR_3模板对齐有显著改进，但最终模型的总体质量基本上取决于线程模板的质量：TASSER模型在三个类别中的平均TM分数分别为0.79 [比较建模（CM），43个目标/域]，0.47 [折叠识别（FR），37个目标/域]，和0.30 [新折叠（NF），10个靶标/结构域]。这突出了需要开发新的（或改进的）方法来识别非常遥远的目标以及更好的NF算法。
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.