ARTIFICIAL NEURAL NETWORKS (ANNS) APPROACH FOR CLASSIFICATION OF SEED STORAGE PROTEINS OF VARIOUS NUTRITIONALLY SUPERIOR CEREAL CROPS

HIMANSHU AVASHTHI¹, RICHA JHA², MUGDHA SHARMA³, ARVIND KUMAR YADAV⁴, A.K. MISHRA⁵*, PRAMOD WASUDEO RAMTEKE⁶, ANIL KUMAR^7
1ICAR - Agricultural Knowledge Management Unit, Indian Agricultural Research Institute, Pusa Campus, New Delhi, 110012, India
²Department of Biotechnology, Uttaranchal Institute of Technology, Uttaranchal University, Arcadia Grant, Dehradun, 248007, Uttarakhand, India
³Department of Bioscience and Biotechnology, Banasthali University, Banasthali, 304022, Rajasthan, India
⁴ICAR-National Research Centre on Plant Biotechnology, Pusa Campus, New Delhi, 110012, India
⁵ICAR - Agricultural Knowledge Management Unit, Indian Agricultural Research Institute, Pusa Campus, New Delhi, 110012, India
⁶Department of Biological Sciences, School of Basic Science, Sam Higginbottom University of Agriculture, Technology & Sciences, Allahabad, 211007, Uttar Pradesh
⁷Department of Molecular Biology & Genetic Engineering, CBSH, G. B. Pant University of Agriculture & Technology, Pantnagar-263145, Uttarakhand, India
* Corresponding Author : akmishra@iari.res.in

Received : 20-12-2016     Accepted : 14-01-2017     Published : 30-01-2017
Volume : 9     Issue : 5       Pages : 3749 - 3751
Int J Agr Sci 9.5 (2017):3749-3751

Keywords : Seed Storage Proteins, Physicochemical properties, Classification, Artificial Neural Network, Machine learning algorithm
Academic Editor : Dr Shambhavi Yadav, Dr Mamta Pandey
Conflict of Interest : None declared
Acknowledgements/Funding : Authors are grateful and duly acknowledge to DIC Bioinformatics, Biotechnology Information System Network (BTISNet), Department of Biotechnology, Government of India, New Delhi for providing all necessary facilities for conducting quality research in area of Bioinformatics at Agricultural Knowledge Management Unit, Indian Agricultural Research Institute, New Delhi, India
Author Contribution : None declared

Seed storage proteins comprise a key part of the protein content and play pivotal role to maintain the quality of seed. The composition of storage proteins are very essential because they determine the total protein content of the seed and show their effect on nutritional quality of the seed as well as functional properties of food processing. Therefore, classification is required to categorize these proteins and for the development of crops with improved nutritional superior varieties. Bioinformatics tools and techniques are extensively employed in the arena of agriculture to annotate the biological data. Annotation uncovers the structural and functional characteristics of genes as well as proteins also. In present study seed storage proteins of five major cereal crops were categorized into four classes i.e. albumins (12), globulins (42), glutelins (11) and prolamins (68) using six physicochemical properties (number of amino acid, molecular weight, theoretical pI (isoelectric point), aliphatic index, instability index and hydropathicity) by employing Artificial Neural Networks (ANNs) approach.