International Journal of Pure & Applied Bioscience (IJPAB)
Year : 2017, Volume : 5, Issue : 1
First page : (759) Last page : (770)
Article doi: http://dx.doi.org/10.18782/2320-7051.2586

Peptide Markers based Prediction of Antigen Sequence using Neural Network

Ragini V. Oza^1* and Himanshu S. Mazumdar^2
1Student (M. Tech.), Information Technology, Dharmsinh Desai University, Gujarat, India
²Professor & Head, Research & Development Center, Dharmsinh Desai University, Gujarat, India
*Corresponding Author E-mail: raginioza1206@gmail.com
Received: 13.02.2017  |  Revised: 25.02.2017   |  Accepted: 26.02.2017  

 ABSTRACT

Bioinformatics has witnessed considerable progression in recent years; the prediction of antigen sequence in big data environment still remains challenging. A novel approach is proposed here to generate and evaluate tri-peptide markers, where a combination of high frequency tri-peptides can signify a characteristic of target antigen sequence. A dataset of Plasmodium falciparum antigen sequences is extracted from benchmark uniref100 protein sequence database; Training and test set are generated from extracted P. falciparum dataset. Genetic Algorithm (GA) is used here to identify an optimal set of tri-peptide markers from training set. Through different generations of GA, markers are evaluated using approximate selection function. A total 100 tri-peptides are identified using GA and the rest 150 are extracted by examining fitness function using iterative convergence algorithm. A back propagation neural network is trained to predict target antigen sequences using selected tri-peptide markers. The algorithm is tested on a test set which is non-inclusive in training set and the prediction result obtained shows 93% accuracy. This algorithm can also be useful to synthesis new sequence as possible drug antigen for given target protein.

Key words: Plasmodium Falciparum; Tri-peptide Residue; Occurrence Frequency; Population Ratio; Genetic Algorithm; Iterative Convergence Algorithm; Back-propagation Neural Network

Full Text : PDF; Journal doi : http://dx.doi.org/10.18782

Cite this article: Oza, R.V. and Mazumdar, H.S., Peptide Markers based Prediction of Antigen Sequence using Neural Network, Int. J. Pure App. Biosci.5(1): 759-770 (2017). doi: http://dx.doi.org/10.18782/2320-7051.2586