A Bayesian Approach for Determining Protein Side-Chain Rotamer Conformations Using Unassigned NOE Data

被引:3
|
作者
Zeng, Jianyang [1 ]
Roberts, Kyle E.
Zhou, Pei [2 ]
Donald, Bruce Randall [1 ,2 ,3 ]
机构
[1] Duke Univ, Dept Comp Sci, Durham, NC 27708 USA
[2] Duke Univ, Med Ctr, Dept Biochem, Durham, NC 27710 USA
[3] Duke Univ, Program Computat Biol & Bioinformat, Durham, NC 27708 USA
关键词
Bayesian; Markov random field; nuclear magnetic resonance; nuclear Overhauser effect; side-chain structure determination; RESIDUAL DIPOLAR COUPLINGS; DEAD-END ELIMINATION; NMR STRUCTURE DETERMINATION; STRUCTURE PREDICTION; RAPID-DETERMINATION; BACKBONE STRUCTURE; NOVO DETERMINATION; SEARCH ALGORITHM; NUCLEIC-ACIDS; FORCE-FIELD;
D O I
10.1089/cmb.2011.0172
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
A major bottleneck in protein structure determination via nuclear magnetic resonance (NMR) is the lengthy and laborious process of assigning resonances and nuclear Overhauser effect (NOE) cross peaks. Recent studies have shown that accurate backbone folds can be determined using sparse NMR data, such as residual dipolar couplings (RDCs) or backbone chemical shifts. This opens a question of whether we can also determine the accurate protein side-chain conformations using sparse or unassigned NMR data. We attack this question by using unassigned nuclear Overhauser effect spectroscopy (NOESY) data, which records the through-space dipolar interactions between protons nearby in three-dimensional (3D) space. We propose a Bayesian approach with a Markov random field (MRF) model to integrate the likelihood function derived from observed experimental data, with prior information (i.e., empirical molecular mechanics energies) about the protein structures. We unify the side-chain structure prediction problem with the side-chain structure determination problem using unassigned NMR data, and apply the deterministic dead-end elimination (DEE) and A* search algorithms to provably find the global optimum solution that maximizes the posterior probability. We employ a Hausdorff-based measure to derive the likelihood of a rotamer or a pairwise rotamer interaction from unassigned NOESY data. In addition, we apply a systematic and rigorous approach to estimate the experimental noise in NMR data, which also determines the weighting factor of the data term in the scoring function derived from the Bayesian framework. We tested our approach on real NMR data of three proteins: the FF Domain 2 of human transcription elongation factor CA150 (FF2), the B1 domain of Protein G (GB1), and human ubiquitin. The promising results indicate that our algorithm can be applied in high-resolution protein structure determination. Since our approach does not require any NOE assignment, it can accelerate the NMR structure determination process.
引用
收藏
页码:1661 / 1679
页数:19
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