title: SVM-based method for protein structural class prediction using secondary structural content and structural information of amino acids.
abstract: The knowledge collated from the known protein structures has revealed that the proteins are usually folded into the four structural classes: all-α, all-β, α/β and α + β. A number of methods have been proposed to predict the protein's structural class from its primary structure; however, it has been observed that these methods fail or perform poorly in the cases of distantly related sequences. In this paper, we propose a new method for protein structural class prediction using low homology (twilight-zone) protein sequences dataset. Since protein structural class prediction is a typical classification problem, we have developed a Support Vector Machine (SVM)-based method for protein structural class prediction that uses features derived from the predicted secondary structure and predicted burial information of amino acid residues. The examination of different individual as well as feature combinations revealed that the combination of secondary structural content, secondary structural and solvent accessibility state frequencies of amino acids gave rise to the best leave-one-out cross-validation accuracy of ~81% which is comparable to the best accuracy reported in the literature so far.
authors.affiliation: Laboratory of Computational Biology, Centre for DNA Fingerprinting and Diagnostics (CDFD), Nampally, Hyderabad 500001, India. mohammadt@uthscsa.edu
authors.forename: Tabrez Anwar Shamim
authors.initials: TA
authors.lastname: Mohammad
authors.forename: Hampapathalu Adimurthy
authors.initials: HA
authors.lastname: Nagarajaram
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry; D019295:Computational Biology; D030562:Databases, Protein; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000145:classification* / Q000235:genetics; D040681:Structural Homology, Protein; D060388:Support Vector Machine
chemical_list: D000596:Amino Acids; D011506:Proteins