Venom composition of the Iberian adder (Vipera seoanei) from northern Portugal reveals limited sex and size variation

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  Venom composition of the Iberian adder (Vipera seoanei) from northern Portugal reveals limited sex and size variation Abstract Snake venom is a complex and dynamic ecological trait, with high potential for intraspecific variation. In this study, the venom composition of the Iberian adder ( Vipera seoanei ) from the Peneda Mountains of northern Portugal was investigated to explore whether venom varies with body size and sex. Venom proteomes from six individuals representing juvenile, subadult, and adult males and females were characterised using LC–MS/MS, while individual variation was assessed from SDS–PAGE profiles of 18 snakes. LC–MS/MS identified 144 proteins, dominated by phospholipases A 2 (PLA 2 ), snake venom serine proteases (svSP), and C-type lectin-related proteins (CTL). Descriptive comparisons revealed contrasting individual profiles, particularly between the juvenile male, characterised by high svSP and svMP abundance, and the juvenile female, characterised by high ...

A Machine Learning-Enabled Venom Peptide Platform for Rapid Drug Discovery

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A Machine Learning-Enabled Venom Peptide Platform for Rapid Drug Discovery

Abstract

Background/Objectives: Nature has evolved millions of venom-derived peptides with diverse biological functions, a substantial fraction of which target complex membrane proteins such as G-protein-coupled receptors and ion channels. Many of these peptides are stabilized by multiple disulfide bonds, endowing them with exceptional structural stability and favorable pharmacological properties. 

Methods: Leveraging this natural diversity, we developed a robust venom peptide therapeutics discovery system built on phage display technology and constructed a library using approximately 482 venom-derived scaffolds. The library design was guided by a machine learning (ML) model capable of predicting mutation-tolerant residues that preserve peptide foldability, maximizing structural integrity and sequence diversity. 

Results: The resulting VCX library was evaluated through screening against four diverse targets (CD47, DLL3, IL33, and P2X7R), yielding strong binders for all four, a success rate of 100%. Furthermore, by integrating high-throughput recombinant expression of thioredoxin–venom fusion proteins along with ML-assisted affinity maturation, we rapidly identified potential leads for DLL3 binders. 

Conclusions: This venom-based discovery platform offers significant advantages in both functionality and developability compared with conventional peptide discovery approaches. By combining natural structural diversity, ML-guided design, and recombinant expression, it enables efficient identification of “antibody-like” binders with molecular weights much smaller than those of antibodies. Consequently, it provides a powerful strategy for developing next-generation peptide therapeutics targeting challenging protein–protein interactions and complex membrane proteins.

Cai, F., Zhou, L., Delgado, B., Chang, W., Tom, J., Hernandez, E., Joshi, P., Song, A., Masureel, M., Maun, H. R., Chang, A., & Zhang, Y. (2026). A Machine Learning-Enabled Venom Peptide Platform for Rapid Drug Discovery. Pharmaceuticals, 19(2), 288. https://doi.org/10.3390/ph19020288