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

Publication ,  Journal Article
Zeng, J; Roberts, KE; Zhou, P; Donald, BR
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2011

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 record the through-space dipolar interactions between protons nearby in 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 that is derived from the Bayesian framework. We tested our approach on real NMR data of three proteins, including 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 approach 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.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2011

Volume

6577 LNBI

Start / End Page

563 / 578

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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MLA
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Zeng, J., Roberts, K. E., Zhou, P., & Donald, B. R. (2011). A Bayesian Approach for Determining Protein Side-Chain Rotamer Conformations Using Unassigned NOE Data. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6577 LNBI, 563–578. https://doi.org/10.1007/978-3-642-20036-6_49
Zeng, J., K. E. Roberts, P. Zhou, and B. R. Donald. “A Bayesian Approach for Determining Protein Side-Chain Rotamer Conformations Using Unassigned NOE Data.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6577 LNBI (January 1, 2011): 563–78. https://doi.org/10.1007/978-3-642-20036-6_49.
Zeng J, Roberts KE, Zhou P, Donald BR. A Bayesian Approach for Determining Protein Side-Chain Rotamer Conformations Using Unassigned NOE Data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2011 Jan 1;6577 LNBI:563–78.
Zeng, J., et al. “A Bayesian Approach for Determining Protein Side-Chain Rotamer Conformations Using Unassigned NOE Data.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6577 LNBI, Jan. 2011, pp. 563–78. Scopus, doi:10.1007/978-3-642-20036-6_49.
Zeng J, Roberts KE, Zhou P, Donald BR. A Bayesian Approach for Determining Protein Side-Chain Rotamer Conformations Using Unassigned NOE Data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2011 Jan 1;6577 LNBI:563–578.

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2011

Volume

6577 LNBI

Start / End Page

563 / 578

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences