Zhou Hongyi, Pandit Shashi B, Lee Seung Yup, Borreguero Jose, Chen Huiling, Wroblewska Liliana, Skolnick Jeffrey
Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA.
Proteins. 2007;69 Suppl 8:90-7. doi: 10.1002/prot.21649.
An improved TASSER (Threading/ASSEmbly/Refinement) methodology is applied to predict the tertiary structure for all CASP7 targets. TASSER employs template identification by threading, followed by tertiary structure assembly by rearranging continuous template fragments, where conformational space is searched via Parallel Hyperbolic Monte Carlo sampling with an optimized force-field that includes knowledge-based statistical potentials and restraints derived from threading templates. The final models are selected by clustering structures from the low temperature replicas. Improvements in TASSER over CASP6 involve use of better templates from 3D-jury applied to three threading programs, PROSPECTOR_3, SP(3), and SPARKS, and a fragment comparison method for better model ranking. For targets with no reliable templates, a variant of TASSER (chunk-TASSER) is also applied with potentials and restraints extracted from ab initio folded supersecondary chunks of the target to build full-length models. For all 124 CASP targets/domains, the average root-mean-square-deviation (RMSD) from native and alignment coverage of the best initial threading models from 3D-jury are 6.2 A and 93%, respectively. Following TASSER reassembly, the average RMSD of the best model in the template aligned region decreases to 4.9 A and the average TM-score increases from 0.617 for the template to 0.678 for the best full-length model. Based on target difficulty, the average TM-scores of the final model to native are 0.904, 0.671, and 0.307 for high-accuracy template-based modeling, template-based modeling, and free modeling targets/domains, respectively. For the more difficult targets, TASSER with modest human intervention performed better in comparison to its server counterpart, MetaTASSER, which used a limited time simulation.
一种改进的TASSER（穿线法/组装/优化）方法被应用于预测所有CASP7目标的三级结构。TASSER通过穿线法识别模板，然后通过重新排列连续的模板片段进行三级结构组装，其中通过并行双曲蒙特卡罗采样搜索构象空间，采用优化的力场，该力场包括基于知识的统计势和从穿线模板导出的约束。最终模型通过对低温复制品的结构进行聚类来选择。与CASP6相比，TASSER的改进包括使用来自3D-jury的更好模板应用于三个穿线程序PROSPECTOR_3、SP(3)和SPARKS，以及一种片段比较方法以实现更好的模型排序。对于没有可靠模板的目标，还应用了TASSER的一个变体（分块TASSER），从目标的从头折叠超二级结构块中提取势和约束来构建全长模型。对于所有124个CASP目标/结构域，来自3D-jury的最佳初始穿线模型与天然结构的平均均方根偏差（RMSD）和比对覆盖率分别为6.2 Å和93%。经过TASSER重新组装后，模板比对区域中最佳模型的平均RMSD降至4.9 Å，平均TM分数从模板的0.617增加到最佳全长模型的0.678。基于目标难度，对于高精度基于模板的建模、基于模板的建模和自由建模目标/结构域，最终模型与天然结构的平均TM分数分别为0.904、0.671和0.307。对于更具挑战性的目标，与使用有限时间模拟的服务器对应物MetaTASSER相比，经过适度人工干预的TASSER表现更好。
