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Bioinformatics 2006 22(14):e58-e65; doi:10.1093/bioinformatics/btl212
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

AClAP, Autonomous hierarchical agglomerative Cluster Analysis based protocol to partition conformational datasets

Giovanni Bottegoni 1, Walter Rocchia 2, Maurizio Recanatini 1 and Andrea Cavalli 1,*

1 Department of Pharmaceutical Sciences, University of Bologna Via Belmeloro 6, I-40126, Bologna, Italy
2 NEST CNR-INFH, Scuola Normale Superiore of Pisa Piazza dei Cavalieri, 7, I-56126, Pisa, Italy

*To whom correspondence should be addressed.

Motivation: Sampling the conformational space is a fundamental step for both ligand- and structure-based drug design. However, the rational organization of different molecular conformations still remains a challenge. In fact, for drug design applications, the sampling process provides a redundant conformation set whose thorough analysis can be intensive, or even prohibitive. We propose a statistical approach based on cluster analysis aimed at rationalizing the output of methods such as Monte Carlo, genetic, and reconstruction algorithms. Although some software already implements clustering procedures, at present, a universally accepted protocol is still missing.

Results: We integrated hierarchical agglomerative cluster analysis with a clusterability assessment method and a user independent cutting rule, to form a global protocol that we implemented in a MATLAB metalanguage program (AClAP). We tested it on the conformational space of a quite diverse set of drugs generated via Metropolis Monte Carlo simulation, and on the poses we obtained by reiterated docking runs performed by four widespread programs. In our tests, AClAP proved to remarkably reduce the dimensionality of the original datasets at a negligible computational cost. Moreover, when applied to the outcomes of many docking programs together, it was able to point to the crystallographic pose.

Availability: AClAP is available at the "AClAP" section of the website http://www.scfarm.unibo.it.

Contact: E-mail: andrea.cavalli{at}unibo.it.

Supplementary Information: The complete series of AClAP results is available in the "services" section of the website http://www.scfarm.unibo.it.


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