COMPARATIVE ANALYSIS OF PROTEIN CLASSIFICATION METHODS

A large number of new gene candidates are being accumulated in genomic databases day by day. It has become an important task for researchers to identify the functions of these new genes and proteins. Faster and more sensitive and accurate methods are required to classify these proteins into families and predict their functions. Many existing protein clas-sification methods build hidden Markov models (HMMs) and other forms of profiles/motifs based on multiple alignments. These methods in general require a large amount of time for building models and also for predicting functions based on them. Furthermore, they can predict protein functions only if sequences are sufficiently conserved. When there is very little sequence similarity, these methods often fail, even if sequences share some structural similarities. One example of highly diverged protein families is G-protein coupled recep-tors (GPCRs). GPCRs are transmembrane proteins that play important roles in various signal transmission processes, many of which are directly associated with a variety of hu-man diseases. Machine learning methods that have been studied specifically for a problem of GPCR family classification include HMM and support vector machine (SVM) methods. However, amino acid composition has not been studied well as a property for GPCR clas