Graphlet kernels for prediction of functional residues in protein structures.

ABSTRACT: We introduce a novel graph-based kernel method for annotating functional residues in protein structures. A structure is first modeled as a protein contact graph, where nodes correspond to residues and edges connect spatially neighboring residues. Each vertex in the graph is then represented as a vector of counts of labeled non-isomorphic subgraphs (graphlets), centered on the vertex of interest. A similarity measure between two vertices is expressed as the inner product of their respective count vectors and is used in a supervised learning framework to classify protein residues. We evaluated our method on two function prediction problems: identification of catalytic residues in proteins, which is a well-studied problem suitable for benchmarking, and a much less explored problem of predicti

SUBMITTER: Vacic V 

PROVIDER: S-EPMC2921594 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature