Jang Richard, Wang Yan, Xue Zhidong, Zhang Yang
School of Software Engineering, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.
Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI, 48109-2218, USA.
J Biomol NMR. 2015 Aug;62(4):511-25. doi: 10.1007/s10858-015-9914-y. Epub 2015 Mar 4.
NMR-I-TASSER, an adaption of the I-TASSER algorithm combining NMR data for protein structure determination, recently joined the second round of the CASD-NMR experiment. Unlike many molecular dynamics-based methods, NMR-I-TASSER takes a molecular replacement-like approach to the problem by first threading the target through the PDB to identify structural templates which are then used for iterative NOE assignments and fragment structure assembly refinements. The employment of multiple templates allows NMR-I-TASSER to sample different topologies while convergence to a single structure is not required. Retroactive and blind tests of the CASD-NMR targets from Rounds 1 and 2 demonstrate that even without using NOE peak lists I-TASSER can generate correct structure topology with 15 of 20 targets having a TM-score above 0.5. With the addition of NOE-based distance restraints, NMR-I-TASSER significantly improved the I-TASSER models with all models having the TM-score above 0.5. The average RMSD was reduced from 5.29 to 2.14 Å in Round 1 and 3.18 to 1.71 Å in Round 2. There is no obvious difference in the modeling results with using raw and refined peak lists, indicating robustness of the pipeline to the NOE assignment errors. Overall, despite the low-resolution modeling the current NMR-I-TASSER pipeline provides a coarse-grained structure folding approach complementary to traditional molecular dynamics simulations, which can produce fast near-native frameworks for atomic-level structural refinement.
NMR-I-TASSER是I-TASSER算法的一种改编版本,它结合了用于蛋白质结构测定的核磁共振(NMR)数据,最近参加了第二轮蛋白质结构预测技术关键评估(CASD-NMR)实验。与许多基于分子动力学的方法不同,NMR-I-TASSER采用类似分子置换的方法来解决问题,首先将目标序列穿线到蛋白质数据银行(PDB)中以识别结构模板,然后将这些模板用于迭代的核欧沃豪斯效应(NOE)分配和片段结构组装优化。使用多个模板使NMR-I-TASSER能够对不同的拓扑结构进行采样,而无需收敛到单一结构。对第一轮和第二轮CASD-NMR目标的追溯性和盲测表明,即使不使用NOE峰列表,I-TASSER也能生成正确的结构拓扑,20个目标中有15个的TM分数高于0.5。通过添加基于NOE的距离约束,NMR-I-TASSER显著改进了I-TASSER模型,所有模型的TM分数均高于0.5。第一轮的平均均方根偏差(RMSD)从5.29 Å降至2.14 Å,第二轮从3.18 Å降至1.71 Å。使用原始峰列表和优化后的峰列表的建模结果没有明显差异,这表明该流程对NOE分配错误具有鲁棒性。总体而言,尽管是低分辨率建模,当前的NMR-I-TASSER流程提供了一种与传统分子动力学模拟互补的粗粒度结构折叠方法,它可以快速生成接近天然的框架用于原子级结构优化。