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Identifying residue coupling relationships within a protein family can
provide important insights into intrinsic molecular processes, and has
significant applications in modeling structure and dynamics,
understanding function, and designing new or modified proteins. We
present the first algorithm to infer an undirected graphical model
representing residue coupling in protein families. Such a model
serves as a compact description of the joint amino acid distribution,
and can be used for predictive (will this newly designed protein be
folded and functional?), diagnostic (why is this protein not stable or
functional?), and abductive reasoning (what if I attempt to graft
features of one protein family onto another?). Unlike current
correlated mutation algorithms that are focused on assessing
dependence, which can conflate direct and indirect relationships, our
algorithm focuses on assessing independence, which modularizes
variation and thus enables efficient reasoning of the types described
above. Further, our algorithm can readily incorporate, as priors,
hypotheses regarding possible underlying mechanistic/energetic
explanations for coupling. The resulting approach constitutes a
powerful and discriminatory mechanism to identify residue coupling
from protein sequences and structures. Analysis results on the
G-protein coupled receptor (GPCR) and PDZ domain families demonstrate
the ability of our approach to effectively uncover and exploit models
of residue coupling.
To appear in BIOKDD05.
Bibliographic citation for this report: [plain text] [BIB] [BibTeX] [Refer]
Or copy and paste:
John Thomas, Naren Ramakrishnan, and Chris Bailey-Kellogg, "Graphical Models of Residue Coupling in Protein Families." Dartmouth Computer Science Technical Report TR2005-535, March 2005.
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