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Bioinformatics Advance Access published online on January 29, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth031
Bioinformatics © Oxford University Press 2004; all rights reserved
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Accepted October 22, 2003

Article

Measuring the similarity of protein structures by means of the Universal Similarity Metric

N. Krasnogor 1* D. A. Pelta 2*

1 Automated Scheduling, Optimisation and Planning Group University of Nottingham, Nottingham, NG8 1BB, UK
2 Department of Computer Science and Artificial Intelligence E.T.S.I. Informatica, Umversidad de Granada, 18071, Granada, Spain

* To whom correspondence should be addressed. E-mail: dpelta{at}ugr.es.


  Abstract

Motivation

As an increasing number of protein structures become available, the need for algorithms that can quantify the similarity between protein structures increases as well. Thus, the comparison of proteins' structures, and their clustering accordingly to a given similarity measure, is at the core of today's biomedical research. In this paper we show how an algorithmic information theory inspired Universal Similarity Metric can be used to calculate similarities between protein pairs. The method, besides being theoretically supported, is surprisingly simple to implement and computationally efficient.

Results

Structural similarity between proteins in four different data sets was measured using the Universal Similarity Metric. The sample employed represented alpha, beta, alpha-beta, tim-barrel, globins and serpine protein types. The use of the proposed metric allows for a correct measurement of similarity and classification of the proteins in the four data sets.

Availability

All the scripts and programs used for the preparation of this paper are available at http://www.cs.nott.ac.uk/~nxk/USM/protocol.html In that web-page the reader will find a brief description on how to use the various scripts and programs.

Supplementary Information

The protein data sets used are collected in http://www.cs.nott.ac.uk/~nxk/USM/datasets.html The calculated similarity values for the proteins used in this paper can be found in http://www.cs.nott.ac.uk/~nxk/USM/similar.htnil The clustering of the data set based on these similarity values can be found in http://www.cs.nott.ac.uk/~nxk/USM/clustering.html


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