By Topic

Protein secondary structure pattern discovery and its application in secondary structure prediction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
4 Author(s)
Ming-Hui Li ; Dept. of Comput. Sci. & Technol., Harbin Inst. of Technol., China ; Xiao-Long Wang ; Lei Lin ; Yi Guan

A method of protein secondary structure pattern discovery is presented. The TEIRESIAS algorithm has been improved to discover protein secondary structure patterns. Four protein secondary structure pattern dictionaries have been built for four organisms. The distribution of patterns and common patterns' structure in different dictionaries is different. Different organism's proteins represent different biological language. Based on the organism-specific dictionary, a hidden Markov model is built to predict proteins secondary structure. Dictionary-based prediction has been tested on four organisms and compared with the profile network from HeiDelberg (PHD) method. The experimental results show that our predict method is better than the PHD method for modified segment overlap (SOV) assessment.

Published in:

Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on  (Volume:3 )

Date of Conference:

26-29 Aug. 2004