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容错核磁共振主链共振归属及自动结构生成

Error tolerant NMR backbone resonance assignment and automated structure generation.

作者信息

Alipanahi Babak, Gao Xin, Karakoc Emre, Li Shuai Cheng, Balbach Frank, Feng Guangyu, Donaldson Logan, Li Ming

机构信息

David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario N2L3G1, Canada.

出版信息

J Bioinform Comput Biol. 2011 Feb;9(1):15-41. doi: 10.1142/s0219720011005276.

Abstract

Error tolerant backbone resonance assignment is the cornerstone of the NMR structure determination process. Although a variety of assignment approaches have been developed, none works sufficiently well on noisy fully automatically picked peaks to enable the subsequent automatic structure determination steps. We have designed an integer linear programming (ILP) based assignment system (IPASS) that has enabled fully automatic protein structure determination for four test proteins. IPASS employs probabilistic spin system typing based on chemical shifts and secondary structure predictions. Furthermore, IPASS extracts connectivity information from the inter-residue information and the (automatically picked) (15)N-edited NOESY peaks which are then used to fix reliable fragments. When applied to automatically picked peaks for real proteins, IPASS achieves an average precision and recall of 82% and 63%, respectively. In contrast, the next best method, MARS, achieves an average precision and recall of 77% and 36%, respectively. The assignments generated by IPASS are then fed into our protein structure calculation system, FALCON-NMR, to determine the 3D structures without human intervention. The final models have backbone RMSDs of 1.25Å, 0.88Å, 1.49Å, and 0.67Å to the reference native structures for proteins TM1112, CASKIN, VRAR, and HACS1, respectively. The web server is publicly available at http://monod.uwaterloo.ca/nmr/ipass.

摘要

容错主链共振归属是核磁共振结构测定过程的基石。尽管已经开发了多种归属方法,但在噪声环境下对全自动挑选出的峰,没有一种方法能很好地发挥作用,以实现后续的自动结构测定步骤。我们设计了一种基于整数线性规划(ILP)的归属系统(IPASS),该系统已实现了对四种测试蛋白的全自动蛋白质结构测定。IPASS采用基于化学位移和二级结构预测的概率性自旋系统分型。此外,IPASS从残基间信息和(自动挑选的)(15)N编辑的NOESY峰中提取连接性信息,然后用于确定可靠的片段。当应用于真实蛋白质的自动挑选峰时,IPASS的平均精度和召回率分别达到82%和63%。相比之下,次优方法MARS的平均精度和召回率分别为77%和36%。然后将IPASS生成的归属结果输入我们的蛋白质结构计算系统FALCON-NMR,以在无需人工干预的情况下确定三维结构。最终模型与蛋白质TM1112、CASKIN、VRAR和HACS1的参考天然结构相比,主链的均方根偏差(RMSD)分别为1.25Å、0.88Å、1.49Å和0.67Å。该网络服务器可在http://monod.uwaterloo.ca/nmr/ipass上公开获取。

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