As major components of spider venoms, neurotoxic peptides exhibit structural diversity, target specificity, and have great pharmaceutical potential. Deep learning may be an alternative to the laborious and time-consuming methods for identifying these peptides. However, the major hurdle in developing a deep learning model is the limited data on neurotoxic peptides. Here, we present a peptide data augmentation method that improves the recognition of neurotoxic peptides via a convolutional neural network model. The neurotoxic peptides were augmented with the known neurotoxic peptides from UniProt database, and the models were trained using a training set with or without the generated sequences to verify the augmented data. The model trained with the augmented dataset outperformed the one with the unaugmented dataset, achieving accuracy of 0.9953, precision of 0.9922, recall of 0.9984, and F1 score of 0.9953 in simulation dataset. From the set of all RNA transcripts of Callobius koreanus spider, we discovered neurotoxic peptides via the model, resulting in 275 putative peptides of which 252 novel sequences and only 23 sequences showing homology with the known peptides by Basic Local Alignment Search Tool. Among these 275 peptides, four were selected and shown to have neuromodulatory effects on the human neuroblastoma cell line SH-SY5Y. The augmentation method presented here may be applied to the identification of other functional peptides from biological resources with insufficient data.

The prevalence of antibiotic-resistant bacteria has become an immediate threat to public health. Antimicrobial peptides are attracting attention as a new source of antibiotics due to their ability to prevent drug-resistances with fewer side effects. Spider venom is composed of various bioactive substances with multiple functionalities such as antimicrobial and anti-inflammatory effects. Here, RNA sequencing was conducted on the venom gland of the spider Pardosa astrigera, and a potential toxin peptide with antibacterial properties was selected via homology and in silico analysis. A novel toxin, Lycotoxin-Pa4a, inhibited both gram-negative and gram-positive bacteria by disrupting the outer and bacterial cytoplasmic membrane. Moreover, the peptide downregulated the expression of pro-inflammatory mediators while upregulating the level of anti-inflammatory cytokine by inactivating mitogen-activated protein kinase signaling in a lipopolysaccharide-stimulated murine macrophage cell line. In this research, we identified a novel peptide toxin, Lycotoxin-pa4a, with antibacterial and anti-inflammatory properties, suggesting its potential for the development of a new antibiotics, as well as offering insights into the utilization of biological resources.