Abstract
With about 13,000 known species, ants are the most abundant venomous insects. Their venom consists of polypeptides, enzymes, alkaloids, biogenic amines, formic acid, and hydrocarbons. In this study, we investigated, using in silico techniques, the peptides composing a putative antimicrobial arsenal from the venom gland of the neotropical trap-jaw ant Odontomachus chelifer. Focusing on transcripts from the body and venom gland of this insect, it was possible to determine the gland secretome, which contained about 1022 peptides with putative signal peptides. The majority of these peptides (75.5%) were unknown, not matching any reference database, motivating us to extract functional insights via machine learning-based techniques. With several complementary methodologies, we investigated the existence of antimicrobial peptides (AMPs) in the venom gland of O. chelifer, finding 112 non-redundant candidates. Candidate AMPs were predicted to be more globular and hemolytic than the remaining peptides in the secretome. There is evidence of transcription for 97% of AMP candidates across the same ant genus, with one of them also verified as translated, thus supporting our findings. Most of these potential antimicrobial sequences (94.8%) matched transcripts from the ant's body, indicating their role not solely as venom toxins.