Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor.
BACKGROUND
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.
Sandia National Laboratories, Computational Biosciences Department, P.O. Box 5800, Albuquerque, NM 87185-1413, USA. jfaulon@sandia.gov
Jean-Loup
JL
Faulon
Milind
M
Misra
Shawn
S
Martin
Ken
K
Sale
Rajat
R
Sapra
D001665:Binding Sites; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D030562:Databases, Protein; D016503:Drug Delivery Systems / Q000379:methods*; D015195:Drug Design; D004798:Enzymes / Q000737:chemistry* / Q000145:classification* / Q000235:genetics; D008956:Models, Chemical; D008958:Models, Molecular; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*
D004798:Enzymes