First catalog of spiders of Egypt (Araneae)

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  First catalog of spiders of Egypt (Araneae) Abstract This study provides a comprehensive catalog of the spider fauna of Egypt, comprising 389 species assigned to 197 genera and 40 families. For each species, all published locality records are compiled and accompanied by complete bibliographic references. The dataset has been carefully reviewed and critically evaluated in light of the most recent taxonomic and faunistic information. Among the recorded species, 55 (14.13%) are currently known only from Egypt and are thus considered endemic. Based on the World Spider Catalog (2026), 28 species are treated as nomina dubia, 23 species lack confirmed records from Egypt, which is not correct, 10 have to be omitted from the Egyptian list and 8 are species inquirenda. Despite the relatively high number of documented species, the Egyptian araneofauna remains insufficiently explored. Given the country’s ecological diversity and biogeographical position, further research is expected to signi...

Computational exploration of global venoms for antimicrobial discovery with Venomics artificial intelligence

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Computational exploration of global venoms for antimicrobial discovery with Venomics artificial intelligence

Abstract

The rise of antibiotic-resistant pathogens, particularly gram-negative bacteria, highlights the urgent need for novel therapeutics. Drug-resistant infections now contribute to approximately 5 million deaths annually, yet traditional antibiotic discovery has significantly stagnated. Venoms form an immense and largely untapped reservoir of bioactive molecules with antimicrobial potential. In this study, we mined global venomics datasets to identify new antimicrobial candidates. Using deep learning, we explored 16,123 venom proteins, generating 40,626,260 venom-encrypted peptides. From these, we identified 386 candidates that are structurally and functionally distinct from known antimicrobial peptides. They display high net charge and elevated hydrophobicity, characteristics conducive to bacterial-membrane disruption. Structural studies revealed that many of these peptides adopt flexible conformations that transition to α-helical conformations in membrane-mimicking environments, supporting their antimicrobial potential. Of the 58 peptides selected for experimental validation, 53 display potent antimicrobial activity. Mechanistic assays indicated that they primarily exert their effects through bacterial-membrane depolarization, mirroring AMP-like mechanisms. In a murine model of Acinetobacter baumannii infection, lead peptides significantly reduced bacterial burden without observable toxicity. Our findings demonstrate that venoms are a rich source of previously hidden antimicrobial scaffolds, and that integrating large-scale computational mining with experimental validation can accelerate the discovery of urgently needed antibiotics.

Guan, C., Torres, M.D.T., Li, S. et al. Computational exploration of global venoms for antimicrobial discovery with Venomics artificial intelligence. Nat Commun 16, 6446 (2025). https://doi.org/10.1038/s41467-025-60051-6