An Artificial Immune System for Evolving Amino Acid Clusters Tailored to Protein Function Prediction
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
International Journal of Data Mining and Bioinformatics
Classification of GPCRs Using Family Specific Motifs
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Large Margin Hierarchical Classification with Mutually Exclusive Class Membership
The Journal of Machine Learning Research
PRIB'12 Proceedings of the 7th IAPR international conference on Pattern Recognition in Bioinformatics
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Motivation: G protein-coupled receptors (GPCRs) play an important role in many physiological systems by transducing an extracellular signal into an intracellular response. Over 50% of all marketed drugs are targeted towards a GPCR. There is considerable interest in developing an algorithm that could effectively predict the function of a GPCR from its primary sequence. Such an algorithm is useful not only in identifying novel GPCR sequences but in characterizing the interrelationships between known GPCRs. Results: An alignment-free approach to GPCR classification has been developed using techniques drawn from data mining and proteochemometrics. A dataset of over 8000 sequences was constructed to train the algorithm. This represents one of the largest GPCR datasets currently available. A predictive algorithm was developed based upon the simplest reasonable numerical representation of the protein's physicochemical properties. A selective top-down approach was developed, which used a hierarchical classifier to assign sequences to subdivisions within the GPCR hierarchy. The predictive performance of the algorithm was assessed against several standard data mining classifiers and further validated against Support Vector Machine-based GPCR prediction servers. The selective top-down approach achieves significantly higher accuracy than standard data mining methods in almost all cases. Contact: m.davies@mail.cryst.bbk.ac.uk