Classification of Rice Seed Storage Proteins using Neural Networks

Abstract

Bioinformatics techniques are increasingly employed in annotation of information from sequencing of model organisms. Development of rice varieties with improved protein quality requires detailed annotation and classification of seed storage proteins in rice germplasm. Two neural network tools were employed for classification of seed storage proteins into four classes- Albumins, Globulins, Gluteins and Prolamins using biochemical, structural properties and conserved sequence patterns. Protein classification results obtained from two software tools were cross compared and superior protein family classification accuracy of 95.3 percent is achieved with Alyuda Neurointelligence computational tool. This is the first report on classification of rice seed proteins using sequenced rice genome information.