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Using physicochemical properties of amino acids to induce graphical models of residue couplings

Published: 21 August 2011 Publication History

Abstract

Residue coupling in protein families is an important indicator for structural and functional conservation. Two residues are coupled if changes of amino acid at one residue location are correlated with changes in the other. Many algorithmic techniques have been proposed to discover couplings in protein families. These approaches discover couplings over amino acid combinations but do not yield mechanistic or other explanations for such couplings. We propose to study couplings in terms of amino acid classes such as polarity, hydrophobicity, size, and reactivity, and present two algorithms for learning probabilistic graphical models of amino acid class-based residue couplings. Our probabilistic graphical models provide a sound basis for predictive, diagnostic, and abductive reasoning. Further, our methods can take optional structural priors into account for building graphical models. The resulting models are useful in assessing the likelihood of a new protein to be a member of a family and for designing new protein sequences by sampling from the graphical model. We apply our approaches to understand couplings in two protein families: Nickel-responsive transription factors (NikR) and G-protein coupled receptors (GPCRs). The results demonstrate that our graphcial models based on sequences, physicochemical properties, and protein structure are capable of detecting amino acid class-based couplings between important residues that play roles in activities of these two families.

References

[1]
Abbeel et al. Learning factor graphs in polynomial time and sample complexity. Journal of Machine Learning Research, 7:1743--1788, 2006.
[2]
Bradley et al. Molecular dynamics simulation of the Escherichia coli NikR protein: equilibrium conformational uctuations reveal interdomain allosteric communication pathways. JMB, 378(5):1155--1173, May 2008.
[3]
Durbin et al. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, 1998.
[4]
Horn et al. Collecting and harvesting biological data: The GPCRDB and NucleaRDB databases. Nucleic Acids Research, 29(1):346--349, 2001.
[5]
Humphrey et al. VMD -- Visual Molecular Dynamics. Journal of Molecular Graphics, 14:33--38, 1996.
[6]
Kroeze et al. G-protein-coupled receptors at a glance. Journal of Cell Science, 116:4867--4869, 2003.
[7]
Lichtarge et al. An evolutionary trace method defines binding surfaces common to protein families. Journal of Molecular Biology, 257:342--358, 1996.
[8]
Suel et al. Evolutionary conserved networks of residues mediate allosteric communication in proteins. Nature Structural Biology, 10(1):59--69, Jan 2003.
[9]
Thomas et al. Graphical models of residue coupling in protein families. In 5th ACM SIGKDD Workshop on Data Mining in Bioinformatics (BIOKDD), pages 1--9, 2005.
[10]
Thomas et al. Graphical models of residue coupling in protein families. IEEE/ACM TCBB, 5(2):183--97, 2007.
[11]
A. A. Fodor and R. W. Aldrich. Inuence of conservation on calculations of amino acid covariance in multiple sequence alignments. Proteins: Structure, Function, and Bioinformatics, 56:211--221, 2004.
[12]
John W. Kimball. Cell signaling, June 2006.
[13]
P. F. Lazarsfeld and N. W. Henry. Latent Structure Analysis. Boston, Mass.: Houghton Mifflin., 1968.
[14]
Little. Identification of coevolving residues and coevolution potentials emphasizing structure, bond formation and catalytic coordination in protein evolution. PloS One, 4(3):e4762, January 2009.
[15]
S. W. Lockless and R. Ranganathan. Evolutionarily conserved pathways of energetic connectivity in protein families. Science, 286(5438):295--299, Oct 1999.
[16]
Eric R. Schreiter, Michael D. Sintchak, Yayi Guo, Peter T. Chivers, Robert T. Sauer, and Catherine L Drennan. Crystal structure of the nickel-responsive transcription factor nikr. Nature Structural and Molecular Biology, 10:794--799, September 2003.
[17]
William S J Valdar. Scoring residue conservation. Proteins, 48(2):227--41, August 2002.
[18]
Y. Y Yao. Information-theoretic measures for knowledge discovery and data mining. Entropy Measures, Maximum Entropy Principle and Emerging Applications, pages 115--136, 2003.
[19]
Chen-Hsiang Yeang and David Haussler. Detecting coevolution in and among protein domains. PLoS Computational Biology, 3(11):13, 2007.
[20]
Nevin L. Zhang and Tomás Kocka. Efficient learning of hierarchical latent class models. IEEE International Conference on Tools with Artificial Intelligence, 0:585--593, 2004.

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cover image ACM Conferences
BIOKDD '11: Proceedings of the Tenth International Workshop on Data Mining in Bioinformatics
August 2011
47 pages
ISBN:9781450308397
DOI:10.1145/2003351
  • General Chairs:
  • Mohammed Zaki,
  • Jake Chen,
  • Program Chairs:
  • Mohammad Al Hasan,
  • Jun (Luke) Huan
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Published: 21 August 2011

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Author Tags

  1. amino acid classes
  2. evolutionary co-variation
  3. graphical models
  4. residue coupling

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