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Bioinformatics Advance Access published online on May 17, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm266
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© 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.

Statistical prediction of protein-chemical interactions based on chemical structure and mass spectrometry data

Nobuyoshi Nagamine a and Yasubumi Sakakibara a,*

aDepartment of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan

*To whom correspondence should be addressed. Yasubumi Sakakibara, E-mail: yasu{at}bio.keio.ac.jp


  Abstract

Motivation: Prediction of interactions between proteins and chemical compounds is of great benefit in drug discovery processes. In this field, 3D structure based methods such as docking analysis have been developed. However, the genomewide application of these methods is not really feasible as 3D structural information is limited in availability.

Results: We describe a novel method for predicting protein-chemical interaction using SVM.We utilize very general protein data, i.e. amino acid sequences, and combine these with chemical structures and mass spectrometry (MS) data. MS data can be of great use in finding new chemical compounds in the future. We assessed the validity of our method in the dataset of the binding of existing drugs and found that more than 80% accuracy could be obtained. Furthermore, we conducted comprehensive target protein predictions for MDMA, and validated the biological significance of our method by successfully finding proteins relevant to its known functions.

Availability: Available on request from the authors.

Supplementary information: Appendix - technical details of method, Supplementary Table 1-7 and Supplementary Figure 1

Associate Editor: Dr. Jonathan Wren

Received on February 26, 2007; revised on April 21, 2007; accepted on May 10, 2007

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