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Graphical Models of Residue Coupling in Protein Families

Content Provider	IEEE Xplore Digital Library
Author	Thomas, J. Ramakrishnan, N. Bailey-Kellogg, C.
Copyright Year	2004
Abstract	Many statistical measures and algorithmic techniques have been proposed for studying residue coupling in protein families. Generally speaking, two residue positions are considered coupled if, in the sequence record, some of their amino acid type combinations are significantly more common than others. While the proposed approaches have proven useful in finding and describing coupling, a significant missing component is a formal probabilistic model that explicates and compactly represents the coupling, integrates information about sequence, structure, and function, and supports inferential procedures for analysis, diagnosis, and prediction. We present an approach to learning and using probabilistic graphical models of residue coupling (GMRCs). These models capture significant conservation and coupling constraints observable in a multiply aligned set of sequences. Our approach can place a structural prior on considered couplings, so that all identified relationships have direct mechanistic explanations. It can also incorporate information about functional classes, and thereby learn a differential graphical model that distinguishes constraints common to all classes from those unique to individual classes. Such differential models separately account for class-specific conservation and family- wide coupling, two different sources of sequence covariation. They are then able to perform interpretable functional classification of new sequences, explaining classification decisions in terms of the underlying conservation and coupling constraints. We apply our approach in studying both G protein-coupled receptors and PDZ domains, identifying and analyzing family-wide and class-specific constraints, and performing functional classification. The results demonstrate that GMRCs provide a powerful tool for uncovering, representing, and utilizing significant sequence-structure-function relationships in protein families.
Sponsorship	IEEE Computer Society
Page Count	15
File Size	1648325
Starting Page	183
Ending Page	197
File Format	PDF
ISSN	15455963
Volume Number	5
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-04-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Graphical models Proteins Amino acids Hidden Markov models Sequences Genetic mutations Predictive models Information analysis Performance analysis Testing functional classification Correlated mutations graphical models evolutionary covariation sequence-structure-function relationships
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Genetics Biotechnology

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