Deciphering Amino Acid Features in NRPS Sequences of Biosurfactant- Producing Bacteria: A Framework for Machine Learning Insights

Biosurfactant (BS) are recognized for their extensive exploration and various applications in fields of health, agriculture, pharmaceutical, food, and personal care products highlight. BS are safe, efficient, and ecofriendly substitutes for synthetic surfactants, which is the driving force behind this research in advancing creative and sustainable solutions for several industries. Research studies indicated a strong correlation between NRPS gene clusters and BS production, it has also been proven from earlier research studies that NRPS has a modular organization that can be further divided into domains representing the enzymatic units for catalyzing the synthesis of nonribosomal peptides. Furthermore, the applicability of NRPS sequences for biosurfactant production was assessed across 21 NRPS sequences within prokaryotes. Various insilico computational tools have been used to detect NRPS sequences, conserved domains, amino acid conservation and conserved motifs for BS production.