Title: Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor

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. Additionally, 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. Lastly, such predictions cannot be made with current machine-learning techniques requiring binding information for individual reactions or individual targets.

Authors:

Faulon, Jean-Loup ^[1]; Misra, Milind ^[1]; Martin, Shawn ^[2]; Sale, Ken ^[3]; Sapra, Rajat ^[3]

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+ Show Author Affiliations

1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Computational Biosciences Dept.
2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Computer Science & Informatics Department
3. Sandia National Lab. (SNL-CA), Livermore, CA (United States). Biosystems Research

Publication Date:

Fri Nov 23 00:00:00 EST 2007

Research Org.:

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Org.:

USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:

1426948

Report Number(s):

SAND-2007-1648J
Journal ID: ISSN 1367-4803; 526857

Grant/Contract Number:  

AC04-94AL85000

Resource Type:

Accepted Manuscript

Journal Name:

Bioinformatics

Additional Journal Information:

Journal Volume: 24; Journal Issue: 2; Journal ID: ISSN 1367-4803

Publisher:

Oxford University Press

Country of Publication:

United States

Language:

English

Subject:

59 BASIC BIOLOGICAL SCIENCES; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY