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

Journal Article · · Bioinformatics
 [1];  [1];  [2];  [3];  [3]
  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
+ Show Author Affiliations

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.

Research Organization:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000
OSTI ID:
1426948
Report Number(s):
SAND--2007-1648J; 526857
Journal Information:
Bioinformatics, Journal Name: Bioinformatics Journal Issue: 2 Vol. 24; ISSN 1367-4803
Publisher:
Oxford University PressCopyright Statement
Country of Publication:
United States
Language:
English

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