Soil Preference and Burrow Characteristics of Two Theraphosidae Species in Penang Island, Malaysia

Posted by
  Soil Preference and Burrow Characteristics of Two Theraphosidae Species in Penang Island, Malaysia Abstract Tarantulas play a crucial role in maintaining ecological balance by regulating insect populations. However, little is known about the soil preferences and burrow structures of tarantulas in Malaysia. This study aims to determine the soil preference as well as the burrow structure of  Coremiocnemis cunciularia  and  Chilobrachys andersoni  from Penang Island. The soil characteristics of the soil samples collected around the burrows of  Coremiocnemis cunciularia  (n = 30) and  Chilobrachys andersoni  (n = 30) were determined using soil texture analysis. The measurements and burrow structures from adults and juveniles of  Coremiocnemis cunciularia  and  Chilobrachys andersoni  were determined. It was revealed that the moisture content and clay percentage in the soil samples around burrows of  Chilobrachys anderso...

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

Posted by

 


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