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Bioinformatics. 2010 Nov 1;26(21):2698-704. doi: 10.1093/bioinformatics/btq519. Epub 2010 Sep 23.

Metric learning for enzyme active-site search.

Author information

1
GSFS, University of Tokyo, 5-1-5 Kashiwahoha, Kashiwa, Chiba, Japan. kato-tsuyoshi@k.u-tokyo.ac.jp

Abstract

MOTIVATION:

Finding functionally analogous enzymes based on the local structures of active sites is an important problem. Conventional methods use templates of local structures to search for analogous sites, but their performance depends on the selection of atoms for inclusion in the templates.

RESULTS:

The automatic selection of atoms so that site matches can be discriminated from mismatches. The algorithm provides not only good predictions, but also some insights into which atoms are important for the prediction. Our experimental results suggest that the metric learning automatically provides more effective templates than those whose atoms are selected manually.

AVAILABILITY:

Online software is available at http://www.net-machine.net/∼kato/lpmetric1/

PMID:
20870642
PMCID:
PMC2958746
DOI:
10.1093/bioinformatics/btq519
[Indexed for MEDLINE]
Free PMC Article

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