@misc{10481/77884,
year = {2004},
month = {1},
url = {https://hdl.handle.net/10481/77884},
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 (USM) 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
datasets was measured using the USM.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 datasets.
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.},
organization = {TIC2002-04242-C03-02},
publisher = {Oxford University Press},
keywords = {Inteligencia artificial},
keywords = {Artificial intelligence},
title = {Measuring the similarity of protein structures by means of the universal similarity metric},
doi = {10.1093/bioinformatics/bth031},
author = {Krasnogor, N. and Pelta Mochcovsky, David Alejandro},
}