Lyons, J. and Paliwal, K.K. and Dehzangi, A. and Heffernan, R. and Tsunoda, T. and Sharma, Alokanand (2016) Protein fold recognition using HMM–HMM alignment and dynamic programming. Journal of Theoretical Biology, 393 . pp. 67-74. ISSN 0022-5193
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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 classi-
fiers. 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.
| Item Type: | Journal Article |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics |
| Depositing User: | Alokanand Sharma |
| Date Deposited: | 31 Jan 2016 23:01 |
| Last Modified: | 22 Oct 2024 10:32 |
| URI: | http://repository.usp.ac.fj/id/eprint/8684 |
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