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Functional Annotation of Hypothetical proteins of Lactobacillus rhamnosus: An In Silico Approach

Probiotic bacterium, Lactobacillus rhamnosus (LR24) is widely used in the food and nutraceutical industries owing to its numerous health benefits and industrial applications. To efficiently use the bacterium, various genetic engineering techniques and strategies are in use like Genome shuffling, Anti-sense RNA technology and so forth. It is really important to understand the system biology and metabolism of this bacterium and hence the proteome analysis of the bacterium becomes an essential research prospect. However, in NCBI-Genome out of 2640 proteins, 420 are termed hypothetical. Adding functional information to these hypothetical proteins has become strategically important to study systems level and metabolic pathway analysis of the bacterium. The present study focuses on using the in silico strategies like homology analysis based on sequence similarity, sub-cellular localization prediction, domain extraction, searching for essential proteins, mapping with gene ontology terms and functional annotation. Out of 420 proteins, total 46 proteins were annotated and among them 18 proteins were known to have homologs in the Database of essential genes (DEG). Out of 18 proteins, One protein KFK47528.1 had the maximum no.(55) of homologs with DEG so it was modelled with good confidence and query coverage. Overall approach was to assign a putative function to the hypothetical proteins by integrating the information obtained from the various resources. This study also reports a need to develop a standardized pipeline based on intelligent learning with fast and exhaustive approach to solve the biological problem of accumulating hypothetical proteins.