Kernel methods for Calmodulin binding and binding site prediction

Authors:
Michael Hamilton;A. S. N. Reddy;Asa Ben-Hur
Affiliations:
Colorado State University, Fort Collins, CO;Colorado State University, Fort Collins, CO;Colorado State University, Fort Collins, CO
Venue:
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Year:
2011

Citing 6
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Abstract

Calmodulin (CaM) is a calcium-binding protein that is involved in a variety of cellular processes, interacting with many proteins. Since many CaM interactions are calcium-dependent, they are difficult to detect using high-throughput methods like yeast-two-hybrid. Furthermore, detection of CaM binding sites requires a significant experimental effort. Using a collection of CaM binding sites extracted from the Calmodulin Target Database we trained SVM-based classifiers to detect CaM binding sites using a variety of sequence features; our best classifier achieved an area under the ROC curve of 0.89 for detecting binding site locations at the amino acid level. We apply our classifiers to the problem of detecting CaM binding proteins in Arabidopsis; at a false-positive level of 0.05 we detected 638 novel putative CaM binding proteins. These proteins share overrepresented Gene Ontology terms associated with the functions of known CaM binders.