Predicting Protein Families using Protein Shape Context

Authors:
Jun Tan;Donald Adjeroh
Affiliations:
West Virginia University, Morgantown, WV 26506;West Virginia University, Morgantown, WV 26506
Venue:
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Year:
2013

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Abstract

Given the rapidly increasing quantity of available genomic and proteomic data, efficient and reliable analysis of protein 3D structures has become a major challenge in the post genomic era. In this work, we introduce the sorted protein shape context, and its encoding into a protein shape string as an effective descriptor for protein 3D structures. Based on the new encoding, we present a method for predicting the functional family for a given protein 3D structure. Applying the proposed method on a dataset of known protein families from Pfam resulted in an average Type I error rate of 10% and Type II error rate of 0.1%.