Skip Navigation



Bioinformatics Advance Access published online on November 23, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm580
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow Supplementary Data
Right arrowOA All Versions of this Article:
24/2/225    most recent
btm580v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Google Scholar
Right arrow Articles by Faulon, J.-L.
Right arrow Articles by Sapra, R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Faulon, J.-L.
Right arrow Articles by Sapra, R.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Genome Scale Enzyme-Metabolite and Drug-Target Interaction Predictions using the Signature Molecular Descriptor

Jean-Loup Faulon 1,*, Milind Misra 1, Shawn Martin 2, Ken Sale 3 and Rajat Sapra 3

1Sandia National Laboratories, Computational Biosciences Dept., P.O. Box 5800, Albuquerque, NM 87185-1413, USA.2Sandia National Laboratories, Computer Science & Informatics Dept., P.O. Box 5800, Albuquerque, NM, 87185-1316, USA; 3Sandia National Laboratories, Biosystems Research, P.O. Box 969, Livermore, CA 94551-9291, USA.

*To whom correspondence should be addressed. Dr. Jean-Loup Faulon, E-mail: jfaulon{at}sandia.gov


  Abstract

Motivation: Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. There is now sufficient information to apply machine learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein-chemical interactions using heterogeneous input consisting of both protein sequence and chemical information.

Results: Our method relies on expressing proteins and chemicals with a common cheminformatics representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Such predictions cannot be made with current machine learning techniques requiring binding information for individual reactions or individual targets.

Availability & Contact: For questions, paper reprints, please contact Jean-Loup Faulon at jfaulon{at}sandia.gov. Additional information on the signature molecular descriptor and codes can be downloaded at: http://www.cs.sandia.gov/~jfaulon/publication-signature.html

Associate Editor: Dr. Olga Troyanskaya

Received on June 21, 2007; revised on October 21, 2007; accepted on November 19, 2007

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.