title: Protein classification based on text document classification techniques.
abstract: The need for accurate, automated protein classification methods continues to increase as advances in biotechnology uncover new proteins. G-protein coupled receptors (GPCRs) are a particularly difficult superfamily of proteins to classify due to extreme diversity among its members. Previous comparisons of BLAST, k-nearest neighbor (k-NN), hidden markov model (HMM) and support vector machine (SVM) using alignment-based features have suggested that classifiers at the complexity of SVM are needed to attain high accuracy. Here, analogous to document classification, we applied Decision Tree and Naive Bayes classifiers with chi-square feature selection on counts of n-grams (i.e. short peptide sequences of length n) to this classification task. Using the GPCR dataset and evaluation protocol from the previous study, the Naive Bayes classifier attained an accuracy of 93.0 and 92.4% in level I and level II subfamily classification respectively, while SVM has a reported accuracy of 88.4 and 86.3%. This is a 39.7 and 44.5% reduction in residual error for level I and level II subfamily classification, respectively. The Decision Tree, while inferior to SVM, outperforms HMM in both level I and level II subfamily classification. For those GPCR families whose profiles are stored in the Protein FAMilies database of alignments and HMMs (PFAM), our method performs comparably to a search against those profiles. Finally, our method can be generalized to other protein families by applying it to the superfamily of nuclear receptors with 94.5, 97.8 and 93.6% accuracy in family, level I and level II subfamily classification respectively.
authors.affiliation: Language Technologies Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania, USA.
authors.forename: Betty Yee Man
authors.initials: BY
authors.lastname: Cheng
authors.forename: Jaime G
authors.initials: JG
authors.lastname: Carbonell
authors.forename: Judith
authors.initials: J
authors.lastname: Klein-Seetharaman
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001709:Biotechnology / Q000379:methods*; D002462:Cell Membrane / Q000378:metabolism; D002467:Cell Nucleus / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D003663:Decision Trees; D015894:Genome, Human; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D008969:Molecular Sequence Data; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry*; D015203:Reproducibility of Results; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein; D012984:Software; D009626:Terminology as Topic
chemical_list: D011506:Proteins; D043562:Receptors, G-Protein-Coupled