VenomFlow: An Automated Bioinformatic Pipeline for Identification of Disulfide-Rich Peptides from Venom Arsenals

Abstract

Animal venoms are among the most complex natural secretions known, comprising a mixture of bioactive compounds often referred to as toxins. Venom arsenals are predominately made up of cysteine-rich peptide toxins that manipulate molecular targets, such as ion channels and receptors, making these venom peptides attractive candidates for the development of therapeutics to benefit human health. With the rise of omic strategies that utilize transcriptomic, proteomic, and bioinformatic methods, we are able to identify more venom proteins and peptides than ever before. However, identification and characterization of bioactive venom peptides remains a significant challenge due to the unique chemical structure and enormous number of peptides found in each venom arsenal (upward of 200 per organism). Here, we introduce a rapid and user-friendly in silico bioinformatic pipeline for the de novo identification and characterization of raw RNAseq reads from venom glands to elucidate cysteine-rich peptides from the arsenal of venomous organisms. Implementation: This project develops a user-friendly automated bioinformatics pipeline via a Galaxy workflow to identify novel venom peptides from raw RNAseq reads of terebrid snails. While designed for venomous terebrid snails, with minor adjustments, this pipeline can be made universal to identify secreted disulfide-rich peptide toxins from any venomous organism.