Caroline E. Cameron

Deep Learning-Based Structure Modeling of the Treponema pallidum Proteome: Insights into Pathogenesis and Syphilis Vaccine Development

This paper uses AI to model the structure of T. pallidum proteins, annotates their functions, identifies potential pathogenesis-related proteins, and highlights vaccine targets.

Time-course Transcriptomics Reveals the Impact of Treponema pallidum on Microvascular Endothelial Cell Function and Phenotype

This study uses time-course transcriptomics to show how Treponema pallidum alters endothelial cell pathways, leading to endothelial-to-mesenchymal transition and potential disease mechanisms.

Treponema pallidum periplasmic and membrane proteins are recognized by circulating and skin CD4+ T cells

The paper identifies Tp-specific CD4+ T cell responses in blood and skin of syphilis patients, showing persistence after treatment and highlighting potential vaccine targets.

AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens

AMPlify is a deep learning model that predicts antimicrobial peptides from bullfrog genomes, identifying novel peptides effective against WHO priority pathogens including drug-resistant bacteria.