ML-Based Functional Annotation of Hypothetical Proteins in Bacterial Genomes

Authors- VAISHNAVI

Abstract-– Bacterial genomes contain numerous genes that encode hypothetical proteins—proteins whose functions remain unknown due to lack of experimental validation or sequence-based annotation. The presence of these uncharacterized proteins represents a significant gap in our understanding of microbial biology and their potential roles in various physiological processes, including virulence, antimicrobial resistance, and metabolism. Traditional methods of protein annotation rely heavily on sequence homology, which may not always be applicable to novel or poorly characterized proteins. Machine learning (ML) techniques have emerged as powerful tools for overcoming these limitations. This article explores the application of ML-based approaches to functionally annotate hypothetical proteins in bacterial genomes, highlighting the potential of these methods to predict protein functions, identify novel protein families, and contribute to the advancement of microbiological research.

DOI: /10.61463/ijset.vol.13.issue2.432