title: Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information.
abstract: Deciphering three dimensional structure of a protein sequence is a challenging task in biological science. Protein fold recognition and protein secondary structure prediction are transitional steps in identifying the three dimensional structure of a protein. For protein fold recognition, evolutionary-based information of amino acid sequences from the position specific scoring matrix (PSSM) has been recently applied with improved results. On the other hand, the SPINE-X predictor has been developed and applied for protein secondary structure prediction. Several reported methods for protein fold recognition have only limited accuracy. In this paper, we have developed a strategy of combining evolutionary-based information (from PSSM) and predicted secondary structure using SPINE-X to improve protein fold recognition. The strategy is based on finding the probabilities of amino acid pairs (AAP). The proposed method has been tested on several protein benchmark datasets and an improvement of 8.9% recognition accuracy has been achieved. We have achieved, for the first time over 90% and 75% prediction accuracies for sequence similarity values below 40% and 25%, respectively. We also obtain 90.6% and 77.0% prediction accuracies, respectively, for the Extended Ding and Dubchak and Taguchi and Gromiha benchmark protein fold recognition datasets widely used for in the literature.
authors.forename: Kuldip K
authors.initials: KK
authors.lastname: Paliwal
authors.forename: Alok
authors.initials: A
authors.lastname: Sharma
authors.forename: James
authors.initials: J
authors.lastname: Lyons
authors.forename: Abdollah
authors.initials: A
authors.lastname: Dehzangi
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D066264:Datasets as Topic; D019143:Evolution, Molecular; D006801:Humans; D056510:Position-Specific Scoring Matrices; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D060388:Support Vector Machine
chemical_list: D011506:Proteins