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Hidden markov model for the prediction of transmembrane proteins using MATLAB



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



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; *Corresponding author


Article Type

Prediction Model


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



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.



Hidden Markov Model, Transmembrane Proteins, MATLAB



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

Edited by

P Kangueane






Biomedical Informatics



This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.