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Hidden markov model for the prediction of transmembrane proteins using MATLAB BACK TO CONTENTS PDF PREVIOUS
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Title

Hidden markov model for the prediction of transmembrane proteins using MATLAB

 

Authors

Navaneet Chaturvedi1*, Sudhanshu Shanker2, Vinay Kumar Singh3, Dhiraj Sinha4 & Paras Nath Pandey5

 

Affiliation

1,2,4Center of Bioinformatics, University of Allahabad, Allahabad, India; 3Bioinformatics Center, School of Biotechnology, Banaras Hindu University, Varanasi, India; 5Department of Mathematics, University of Allahabad, Allahabad, India

 

Email

bioinfonavneet@gmail.com; *Corresponding author

 

Article Type

Prediction Model

Date

Received November 24, 2011; Accepted December 07, 2011; Published December 21, 2011

 

Abstract

Since membranous proteins play a key role in drug targeting therefore transmembrane proteins prediction is active and challenging area of biological sciences. Location based prediction of transmembrane proteins are significant for functional annotation of protein sequences. Hidden markov model based method was widely applied for transmembrane topology prediction. Here we have presented a revised and a better understanding model than an existing one for transmembrane protein prediction. Scripting on MATLAB was built and compiled for parameter estimation of model and applied this model on amino acid sequence to know the transmembrane and its adjacent locations. Estimated model of transmembrane topology was based on TMHMM model architecture. Only 7 super states are defined in the given dataset, which were converted to 96 states on the basis of their length in sequence. Accuracy of the prediction of model was observed about 74 %, is a good enough in the area of transmembrane topology prediction. Therefore we have concluded the hidden markov model plays crucial role in transmembrane helices prediction on MATLAB platform and it could also be useful for drug discovery strategy.

 

Keywords

Hidden Markov Model, Transmembrane Proteins, MATLAB

 

Citation

Chaturvedi et al. Bioinformation 7(8): 418-421 (2011)
 

Edited by

P Kangueane

 

ISSN

0973-2063

 

Publisher

Biomedical Informatics

 

Copyright

Publisher

 

Copyright Transfer Agreement

The authors of published articles in Bioinformation automatically transfer the copyright to the publisher upon formal acceptance. However, the authors reserve right to use the information contained in the article for non commercial purposes.

 

License

This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.