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. The database is available for free at bioinfonavneet@gmail.comvinaysingh@bhu.ac.in.
Highlights
Accurate predictive success of transmembrane proteins by applying hidden markov model [HMM] is frequently used in biological research
In this work we present our model performance, based on hidden markov model, by taking approach from previous research on TMHMM [10]
The dataset is labeled by three main locations, these are transmembrane helix (m), inside (i) and outside loops (o) on the basis of the existence of deferent amino acids pattern within a transmembrane region. 10-fold cross validation was applied for the validation of model [8, 10]
Summary
Accurate predictive success of transmembrane proteins by applying hidden markov model [HMM] is frequently used in biological research. Looking the feasibility of HMM by going through the recent research articles, is totally statistical approach that compiled by the set of states which have potentially able to emit symbols on the basis of probability [1, 6] These states are estimated by model parameters. Helical membrane proteins are specified a “grammar”, in which cytoplasmic and non-cytoplasmic loops have occurred alternate fashion This feature provides the efficient information, even performs better result in prediction [8, 14]. In the context of outer region of transmembrane topology, five residues length of cap cytoplasmic was used to fix the boundary between helix and loop region The dataset is labeled by three main locations, these are transmembrane helix (m), inside (i) and outside loops (o) on the basis of the existence of deferent amino acids pattern within a transmembrane region. 10-fold cross validation was applied for the validation of model [8, 10]
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