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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: J Comput Biol
November 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 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.

Duke Scholars

Published In

J Comput Biol

DOI

EISSN

1557-8666

Publication Date

November 2011

Volume

18

Issue

11

Start / End Page

1661 / 1679

Location

United States

Related Subject Headings

  • Ubiquitin
  • Transcriptional Elongation Factors
  • Signal-To-Noise Ratio
  • Protein Structure, Tertiary
  • Protein Conformation
  • Models, Molecular
  • Markov Chains
  • Magnetic Resonance Spectroscopy
  • Likelihood Functions
  • Humans
 

Citation

APA
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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. J Comput Biol, 18(11), 1661–1679. https://doi.org/10.1089/cmb.2011.0172
Zeng, Jianyang, Kyle E. Roberts, Pei Zhou, and Bruce Randall Donald. “A Bayesian approach for determining protein side-chain rotamer conformations using unassigned NOE data.J Comput Biol 18, no. 11 (November 2011): 1661–79. https://doi.org/10.1089/cmb.2011.0172.
Zeng J, Roberts KE, Zhou P, Donald BR. A Bayesian approach for determining protein side-chain rotamer conformations using unassigned NOE data. J Comput Biol. 2011 Nov;18(11):1661–79.
Zeng, Jianyang, et al. “A Bayesian approach for determining protein side-chain rotamer conformations using unassigned NOE data.J Comput Biol, vol. 18, no. 11, Nov. 2011, pp. 1661–79. Pubmed, doi:10.1089/cmb.2011.0172.
Zeng J, Roberts KE, Zhou P, Donald BR. A Bayesian approach for determining protein side-chain rotamer conformations using unassigned NOE data. J Comput Biol. 2011 Nov;18(11):1661–1679.
Journal cover image

Published In

J Comput Biol

DOI

EISSN

1557-8666

Publication Date

November 2011

Volume

18

Issue

11

Start / End Page

1661 / 1679

Location

United States

Related Subject Headings

  • Ubiquitin
  • Transcriptional Elongation Factors
  • Signal-To-Noise Ratio
  • Protein Structure, Tertiary
  • Protein Conformation
  • Models, Molecular
  • Markov Chains
  • Magnetic Resonance Spectroscopy
  • Likelihood Functions
  • Humans