title: Protein fold recognition using HMM-HMM alignment and dynamic programming.
abstract: Detecting three dimensional structures of protein sequences is a challenging task in biological sciences. For this purpose, protein fold recognition has been utilized as an intermediate step which helps in classifying a novel protein sequence into one of its folds. The process of protein fold recognition encompasses feature extraction of protein sequences and feature identification through suitable classifiers. Several feature extractors are developed to retrieve useful information from protein sequences. These features are generally extracted by constituting protein's sequential, physicochemical and evolutionary properties. The performance in terms of recognition accuracy has also been gradually improved over the last decade. However, it is yet to reach a well reasonable and accepted level. In this work, we first applied HMM-HMM alignment of protein sequence from HHblits to extract profile HMM (PHMM) matrix. Then we computed the distance between respective PHMM matrices using kernalized dynamic programming. We have recorded significant improvement in fold recognition over the state-of-the-art feature extractors. The improvement of recognition accuracy is in the range of 2.7-11.6% when experimented on three benchmark datasets from Structural Classification of Proteins.
authors.affiliation: School of Engineering, Griffith University, Brisbane, QLD 4111, Australia.
authors.forename: James
authors.initials: J
authors.lastname: Lyons
authors.affiliation: School of Engineering, Griffith University, Brisbane, QLD 4111, Australia.
authors.forename: Kuldip K
authors.initials: KK
authors.lastname: Paliwal
authors.affiliation: University of Iowa, USA.
authors.forename: Abdollah
authors.initials: A
authors.lastname: Dehzangi
authors.affiliation: School of Engineering, Griffith University, Brisbane, QLD 4111, Australia.
authors.forename: Rhys
authors.initials: R
authors.lastname: Heffernan
authors.affiliation: Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan; Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan.
authors.forename: Tatsuhiko
authors.initials: T
authors.lastname: Tsunoda
authors.affiliation: Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan; Institute of Integrated and Intelligent Systems, Griffith University, Brisbane, Australia; School of Engineering and Physics, University of the South Pacific, Fiji. Electronic address: alok.sharma@griffith.edu.au.
authors.forename: Alok
authors.initials: A
authors.lastname: Sharma
mesh_terms: D030562:Databases, Protein; D008390:Markov Chains; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D016415:Sequence Alignment / Q000379:methods*; D060388:Support Vector Machine
chemical_list: D011506:Proteins
keywords: Classification; Dynamic time warping; HMM–HMM alignment profile; Protein fold recognition