Overlooked Short Toxin-Like Proteins: A Shortcut to Drug Design

Abstract

Short stable peptides have huge potential for novel therapies and biosimilars. Cysteine-rich short proteins are characterized by multiple disulfide bridges in a compact structure. Many of these metazoan proteins are processed, folded, and secreted as soluble stable folds. These properties are shared by both marine and terrestrial animal toxins. These stable short proteins are promising sources for new drug development. We developed ClanTox (classifier of animal toxins) to identify toxin-like proteins (TOLIPs) using machine learning models trained on a large-scale proteomic database. Insects proteomes provide a rich source for protein innovations. Therefore, we seek overlooked toxin-like proteins from insects (coined iTOLIPs). Out of 4180 short (<75 amino acids) secreted proteins, 379 were predicted as iTOLIPs with high confidence, with as many as 30% of the genes marked as uncharacterized. Based on bioinformatics, structure modeling, and data-mining methods, we found that the most significant group of predicted iTOLIPs carry antimicrobial activity. Among the top predicted sequences were 120 termicin genes from termites with antifungal properties. Structural variations of insect antimicrobial peptides illustrate the similarity to a short version of the defensin fold with antifungal specificity. We also identified 9 proteins that strongly resemble ion channel inhibitors from scorpion and conus toxins. Furthermore, we assigned functional fold to numerous uncharacterized iTOLIPs. We conclude that a systematic approach for finding iTOLIPs provides a rich source of peptides for drug design and innovative therapeutic discoveries.

Shotgun Proteomics Analysis of Saliva and Salivary Gland Tissue from the Common Octopus Octopus vulgaris

“…Short secreted cysteine rich proteins (SSCRs) include a diverse range of potent toxins 70,71 but they are particularly challenging to study for two reasons; firstly because their small size makes them difficult to identify based purely on genomic or transcriptomic sequences 72 and secondly 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 because they frequently do not have homologs in model taxa and lack conserved domains that would provide an indication of function. Perhaps as a consequence of this, these proteins form a large component of what has been termed the dark proteome 73 , proteins with little structural or functional information.…”

WITHDRAWN: ToxVec: Deep Language Model-Based Representation Learning for Venom Peptide Classification

“…This gap motivates computational methods that can automatically and accurately identify venom peptides in the large protein datasets. The prediction of venoms versus non-venom sequences is not a trivial task protein classification task, where the use of BLAST-based approaches is challenging: venoms are often (i) evolved from non-toxic proteins (Hargreaves et al, 2014), (ii) and then have highly diverged (Linial et al, 2017). Several studies have proposed computational and machine learning-based methods for predicting or analyzing toxin/venom peptides (Cole and Brewer, 2019;Dao et al, 2017;Gacesa et al, 2016;Naamati et al, 2009;Ojeda et al, 2018;Pan et al, 2020;Wong et al, 2013).…”

ProtTox: Toxin identification from Protein Sequences

“…Homology or motif discovery based based clustering has been performed to group proteins which utilizes this concept, for instance UniRef clusters in UniProt database. Such methods have had limited success [9] in toxin prediction, due to the varying nature of proteins and structural similarity to non toxins owing to factors such as shared evolutionary lineage [8].…”