Prediction of Protein Beta-Sheets: Dynamic Programming versus Grammatical Approach

Authors:
Yuki Kato;Tatsuya Akutsu;Hiroyuki Seki
Affiliations:
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Japan 611-0011;Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Japan 611-0011;Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Japan 630-0192
Venue:
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Year:
2008

Citing 5
Cited 0

Predicting Protein Secondary Structure Using Stochastic Tree Grammars

Machine Learning - Special issue on learning with probabilistic representations
On the approximation of protein threading

Theoretical Computer Science - Special issue: Genome informatics
Tree adjoining grammars for RNA structure prediction

Theoretical Computer Science - Special issue: Genome informatics
The Complexity of Some Problems on Subsequences and Supersequences

Journal of the ACM (JACM)
A simple and fast secondary structure prediction method using hidden neural networks

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Protein secondary structure prediction is one major task in bioinformatics and various methods in pattern recognition and machine learning have been applied. In particular, it is a challenge to predict β-sheet structures since they range over several discontinuous regions in an amino acid sequence. In this paper, we propose a dynamic programming algorithm for some kind of antiparallel β-sheet, where the proposed approach can be extended for more general classes of β-sheets. Experimental results for real data show that our prediction algorithm has good performance in accuracy. We also show a relation between the proposed algorithm and a grammar-based method. Furthermore, we prove that prediction of planar β-sheet structures is NP-hard.