A comparison of adhesive performance among six cursorial spider species

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  A comparison of adhesive performance among six cursorial spider species Abstract The ability to adhere to surfaces is particularly relevant for cursorial predatory arthropods like hunting spiders, which often traverse relatively complex environments characterized by large variation in substrate properties. Here, we evaluated the adhesive performance of six hunting spider species that are common in eastern temperate North America and lack specialized tarsi for climbing smooth or inclined surfaces [Lycosidae: Pardosa lapidicina Emerton, 1885 and Rabidosa rabida (Walckenaer, 1837); Oxyopidae: Oxyopes salticus Hentz, 1845; Pisauridae: Pisaurina mira (Walckenaer, 1837); Dolomedidae: Dolomedes triton (Walckenaer, 1837), and Dolomedes scriptus Hentz, 1845]. We tested adhesion performance as shear load resistance (g) on a glass plate, and as the angle of failure (°) when the plate was gradually inclined relative to horizontal. Average angle of failure and shear resistance differed among ...

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