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

Nobuyoshi Nagamine, Yasubumi Sakakibara

Research output: Contribution to journalArticle

62 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)2004-2012
Number of pages9
JournalBioinformatics
Volume23
Issue number15
DOIs
Publication statusPublished - 2007 Aug 1

Fingerprint

Mass Spectrometry
Mass spectrometry
Proteins
Protein
Prediction
Interaction
Chemical compounds
N-Methyl-3,4-methylenedioxyamphetamine
Drug Discovery
Docking
Amino Acid Sequence
Amino acids
Drugs
Availability
Amino Acids
Target
Pharmaceutical Preparations

ASJC Scopus subject areas

  • Clinical Biochemistry
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

Statistical prediction of protein - Chemical interactions based on chemical structure and mass spectrometry data. / Nagamine, Nobuyoshi; Sakakibara, Yasubumi.

In: Bioinformatics, Vol. 23, No. 15, 01.08.2007, p. 2004-2012.

Research output: Contribution to journalArticle

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