Huntsman spiders of the genus Sinopoda (Araneae, Sparassidae, Heteropodinae) from the Honghe Hani and Yi Autonomous Prefecture, southwestern China, with descriptions of two new species

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  Huntsman spiders of the genus Sinopoda (Araneae, Sparassidae, Heteropodinae) from the Honghe Hani and Yi Autonomous Prefecture, southwestern China, with descriptions of two new species Abstract Spiders of the genus Sinopoda Jäger, 1999 from Honghe Hani and Yi Autonomous Prefecture, Yunnan Province, China are studied. A total of four species are reported and illustrated of which two, Sinopoda honghe Yu & Zhong, sp. nov . and Sinopoda kuan Yu & Zhong, sp. nov ., are described as new to science. The other two previously described species from this region: S. tengchongensis Fu & Zhu, 2008 and S. tumefacta Zhong, Jäger, Chen & Liu, 2019 are also illustrated. Detailed descriptions, diagnoses, illustrations and DNA barcodes of the two new species are given. A distribution map of these four species in Honghe is provided. Zhang C, Xing Y, Zhong Y, Yu H (2026) Huntsman spiders of the genus Sinopoda (Araneae, Sparassidae, Heteropodinae) from the Honghe Hani and Yi ...

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