Sayan Poddar

My research primarily focuses towards the In silico domain of structural biology, with an emphasis on macromolecular modelling and the investigation of molecular motions and interactions between various macromolecules and small molecules using molecular dynamics simulations. My work centres on the analysis of miniproteins, which are small open reading frames (sORFs) of 20-100 amino acids. Despite their limited size, these molecules play significant roles in biological processes, including signalling cascades, regulation of transcription and translation, molecular recognition, and antimicrobial defence.

Presently, I am engaged in the systematic discovery and characterisation of these miniproteins, which exhibit considerable structural diversity and functional versatility. Notably, a large proportion of these proteins remain uncharacterised and are often annotated as hypothetical in genomic databases. To address this gap, a multi-tiered, AI-driven computational framework designed to identify and classify potential miniproteins through genomic data mining is currently being developed. This framework integrates sequence, structural modelling, validation, and dynamic features to enable automated discovery and functional annotation. Ultimately, this integrative strategy aims to accelerate the identification of novel therapeutic candidates, particularly next-generation antimicrobial peptides (AMPs).