Graph-based clustering for finding distant relationships in a large set of protein sequences.

Abstract

Clustering of protein sequences is widely used for the functional characterization of proteins. However, it is still not easy to cluster distantly-related proteins, which have only regional similarity among their sequences. It is therefore necessary to develop an algorithm for clustering such distantly-related proteins. We have developed a time and space efficient clustering algorithm. It uses a graph representation where its vertices and edges denote proteins and their sequence similarities above a certain cutoff score, respectively. It repeatedly partitions the graph by removing edges that have small weights, which correspond to low sequence similarities. To find the appropriate partitions, we introduce a score combining the normalized cut and a locally minimal cut capacities. Our method is applied to the entire 40,703 human proteins in SWISS-PROT and TrEMBL. The resulting clusters shows a 76% recall (20,529 proteins) of the 26,917 classified by InterPro. It also finds relationships not found by other clustering methods. The complete result of our algorithm for all the human proteins in SWISS-PROT and TrEMBL, and other supplementary information are available at http://motif.ics.es.osaka-u.ac.jp/Ncut-KL/