2. Department of Botany and Biotechnology, University of Rajasthan, JLN Marg, Jaipur, Rajasthan, India.
Background: The glycoside hydrolase (GH) proteins are found in a wide range of organisms viz., Archea, animals and plant. These family members are involved in diverse processes, including starch metabolism, transport, stress defense and cell wall remodeling. A large number of GH proteins have been identified in several plants viz., Oryza sativa, Arabidopsis thaliana and Populus trichocarpa etc. However, the majority of proteins in sorghum are described as putative uncharacterized till date.
Methods: To annotate these proteins in sorghum, we constructed protein interaction among 13 families of GHs. We developed neural network of machine learning based algorithm for protein interactions. The algorithm was considered total average of high (≥90%) GO terms semantic similarity and remove false proteins interaction (<90%).
Results: As a result, total 1,318 high semantic similar homologous proteins were identified from sorghum, rice and Arabidopsis. These data were used to annotate 238 putative uncharacterized proteins from GH in sorghum. Consequently, the identified proteins belonged to the functional categories of carbohydrate transport & metabolism and hydrolase activity. These functional categories appear to be a distinct mechanism of abiotic stress adaptation in plants.
Conclusions: A novel method was developed to annotate putative uncharacterized proteins by proteins interaction in the GO terms semantic similarity. The proposed method will help in further identifying new proteins that may help in the development of stress resistant cereal crops and bioenergy grasses.
Keywords: Protein interaction, glycoside hydrolase, gene ontology, semantic similarity, Sorghum bicolor, abiotic stress