Joo Keehyoung, Joung InSuk, Cheng Qianyi, Lee Sung Jong, Lee Jooyoung
Center for in Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea.
Center for Advanced Computation, Korea Institute for Advanced Study, Seoul, 130-722, Korea.
Proteins. 2016 Sep;84 Suppl 1:189-99. doi: 10.1002/prot.24975. Epub 2016 Jan 11.
We have applied the conformational space annealing method to the contact-assisted protein structure modeling in CASP11. For Tp targets, where predicted residue-residue contact information was provided, the contact energy term in the form of the Lorentzian function was implemented together with the physical energy terms used in our template-free modeling of proteins. Although we observed some structural improvement of Tp models over the models predicted without the Tp information, the improvement was not substantial on average. This is partly due to the inaccuracy of the provided contact information, where only about 18% of it was correct. For Ts targets, where the information of ambiguous NOE (Nuclear Overhauser Effect) restraints was provided, we formulated the modeling in terms of the two-tier optimization problem, which covers: (1) the assignment of NOE peaks and (2) the three-dimensional (3D) model generation based on the assigned NOEs. Although solving the problem in a direct manner appears to be intractable at first glance, we demonstrate through CASP11 that remarkably accurate protein 3D modeling is possible by brute force optimization of a relevant energy function. For 19 Ts targets of the average size of 224 residues, generated protein models were of about 3.6 Å Cα atom accuracy. Even greater structural improvement was observed when additional Tc contact information was provided. For 20 out of the total 24 Tc targets, we were able to generate protein structures which were better than the best model from the rest of the CASP11 groups in terms of GDT-TS. Proteins 2016; 84(Suppl 1):189-199. © 2015 Wiley Periodicals, Inc.
我们已将构象空间退火方法应用于CASP11中接触辅助的蛋白质结构建模。对于提供了预测残基-残基接触信息的Tp靶标,以洛伦兹函数形式的接触能项与我们在无模板蛋白质建模中使用的物理能项一起实施。尽管我们观察到Tp模型相对于没有Tp信息预测的模型在结构上有一些改进,但平均而言改进并不显著。这部分是由于所提供接触信息的不准确,其中只有约18%是正确的。对于提供了模糊的核Overhauser效应(NOE)约束信息的Ts靶标,我们将建模表述为两层优化问题,该问题包括:(1)NOE峰的分配和(2)基于分配的NOE生成三维(3D)模型。尽管乍一看直接解决该问题似乎难以处理,但我们通过CASP11证明,通过对相关能量函数进行强力优化,可以实现非常精确的蛋白质3D建模。对于平均大小为224个残基的19个Ts靶标,生成的蛋白质模型的Cα原子精度约为3.6 Å。当提供额外的Tc接触信息时,观察到了更大的结构改进。在总共24个Tc靶标中,有20个我们能够生成在全局距离测试-总分(GDT-TS)方面优于CASP11其他组最佳模型的蛋白质结构。蛋白质2016;84(增刊1):189 - 199。© 2015威利期刊公司。