Title

The Metric Space of Proteins: Comparative Study of Clustering Algorithms

Publication Type

Journal Article

Publication Date

4-2002

Abstract

A large fraction of biological research concentrates on individual proteins and on small families of proteins. One of the current major challenges in bioinformatics is to extend our knowledge to very large sets of proteins. Several major projects have tackled this problem. Such undertakings usually start with a process that clusters all known proteins or large subsets of this space. Some work in this area is carried out automatically, while other attempts incorporate expert advice and annotation. We propose a novel technique that automatically clusters protein sequences. We consider all proteins in SWISSPROT, and carry out an all-against-all BLAST similarity test among them. With this similarity measure in hand we proceed to perform a continuous bottom-up clustering process by applying alternative rules for merging clusters. The outcome of this clustering process is a classification of the input proteins into a hierarchy of clusters of varying degrees of granularity. Here we compare the clusters that result from alternative merging rules, and validate the results against InterPro. Our preliminary results show that clusters that are consistent with several rather than a single merging rule tend to comply with InterPro annotation. This is an affirmation of the view that the protein space consists of families that differ markedly in their evolutionary conservation.

Keywords

protein families, protein classification, sequence alignment, clustering

Discipline

Bioinformatics | Computer Sciences

Research Areas

Intelligent Systems and Decision Analytics

Publication

Bioinformatics

Volume

18

Issue

Suppl 1

First Page

S14

Last Page

S21

ISSN

1367-4803

Identifier

10.1093/bioinformatics/18.suppl_1.S14

Publisher

Oxford University Press

Additional URL

http://dx.doi.org/10.1093/bioinformatics/18.suppl_1.S14

Comments

This article appears in: Proceedings of the Tenth International Conference on Intelligent Systems for Molecular Biology