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.
Faulon
Jean-Loup
JL
Sandia National Laboratories, Computational Biosciences Department, P.O. Box 5800, Albuquerque, NM 87185-1413, USA. jfaulon@sandia.gov
Misra
Milind
M
Martin
Shawn
S
Sale
Ken
K
Sapra
Rajat
R
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