Predicting GPCR and enzymes function with a global approach based on LCS

The families of G-Protein Coupled Receptor (GPCR) and enzymes are among the main protein family. They represent to the scientific and medical communities, a significant target for bioactive and drug discovery programs. The model of classification of enzymes and GPCR is characterized by its hierarchical structure in format of tree and this makes more difficult its prediction. In this work we propose an adapted version of Learning Classifier Systems (LCS) data mining algorithm which tends to be more efficient than statistical methods based on homology used in tool such as PSI-BLAST. Hence, a new global model approach, called HLCS (Hierarchical Learning Classifier System) is used to predict the function of enzymes and GPCR, respecting its organizational structure of classes throughout the model development. The HLCS is expressed as a set of IF-THEN classification rules, which have the advantage of representing comprehensible knowledge to biologist users. The HLCS is evaluated with eight datasets from enzymes and GPCR, and compared with a Global Naive Bayes algorithm, named GMNB. In the tests realized the HLCS outperformed the GMNB in the databases of the GPCR proteins group type.