Post-doc project This project aims to discover essential pathogen proteins during infection as potential new antibiotic targets, by combining experimental data with advanced computational tools. By leveraging gene expression levels and machine learning models, we intend to predict bacterial protein levels and identify promising therapeutic candidates.
Antibiotic-resistant bacteria are a growing global health problem, driven in part by antibiotics that act on a small set of targets. A promising strategy is to focus on proteins that pathogens need during infection, but protein levels are hard to measure in infected tissues. RNA-sequencing shows which genes are active, yet gene activity does not always reflect protein abundance. This project will develop machine-learning models to predict pathogen protein levels from RNA-seq data collected during infection.
The increasing number of antibiotic-resistant bacteria is a world public health threat. Current antibiotic targets are limited, leading to the use of large spectrum treatments and ultimately increasing probability of more resistant bacteria. Addressing this challenge requires innovative approaches to identify novel therapeutic targets.
A promising strategy involves targeting proteins essential for pathogens during host infection. However, existing technologies cannot directly measure protein abundance in infected tissues. While RNA-sequencing provides accessible gene expression data that correlates with protein levels, translating this information into protein predictions remains a critical gap.
This project aims to bridge this gap by developing machine learning models that predict protein abundance from RNA-seq data. By accurately inferring protein production during infection, we will identify essential pathogen proteins that have remained undetectable with current methods. These proteins represent high-value candidates for next-generation antibiotic development.
The project leverages complementary strengths across three research groups. The Avican Lab contributes expertise in microbial infectious mechanisms and RNA-sequencing technologies, while the Erdem Lab provides knowledge in developing machines learning predictive models for disease applications. The project will be housed at IceLab, an interdisciplinary research environment with experience in developing network inference, stress response modeling and computational biology. This collaborative framework positions the project to make significant advances in both understanding infection biology and developing computational tools for new antibiotic discovery.
