In Silico Identification Of Cryptic Antimicrobial Peptides From The Ichthyophthirius Multifiliis Proteome As Templates For Next-Generation Therapeutics

Abstract

The rapid emergence of antimicrobial resistance has intensified the demand for novel therapeutic agents. Cryptic antimicrobial peptides (AMPs), embedded within larger protein sequences, represent an underexplored reservoir of bioactive molecules with potential for next-generation antimicrobial development. Protozoan parasites, which inhabit microbe-rich host environments, are particularly promising yet neglected sources of such peptides. In this study, we performed a comprehensive in silico analysis of the Ichthyophthirius multifiliis proteome to uncover cryptic AMP candidates. A total of 16,761 protein sequences were analyzed using a Python/Biopython workflow integrating sequence preprocessing, physicochemical evaluation, and deep learning–based AMP prediction. In silico fragmentation produced 197,916 peptide fragments, of which 10,949 were initially predicted as high-confidence AMPs. Sequential filtering based on net positive charge, hydrophobicity, and predicted structural stability reduced this pool to 1,574 biologically plausible peptides. From these, seven unique top candidates were selected for detailed structural analysis. Secondary structure predictions and helical wheel visualization revealed amphipathic α-helical organization, with clear segregation of hydrophobic and positively charged residues, consistent with classical membrane-active AMP mechanisms. Collectively, this study reveals a previously hidden repertoire of cryptic AMPs in I. multifiliis, providing a valuable computational framework for the rational design of next-generation synthetic AMPs for aquaculture and antimicrobial therapeutics.