Project Details
Predicting the effects of ribosome-targeting antibiotic combinations
Applicant
Professor Dr. Tobias Bollenbach
Subject Area
Medical Microbiology and Mycology, Hygiene, Molecular Infection Biology
Bioinformatics and Theoretical Biology
Biophysics
Bioinformatics and Theoretical Biology
Biophysics
Term
from 2019 to 2024
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 422345533
Drug combinations are increasingly important in the treatment of diverse diseases and conditions. Identifying new synergistic combinations can revive old antibiotics that were discontinued due to excessive resistance. Smartly designed drug pairs have potential for preventing future drug resistance. Despite this vast potential, drug interactions like synergism and antagonism are still unpredictable and their systematic identification requires large-scale screens that quickly become infeasible due to a combinatorial explosion. Here, we aim to develop a quantitative model that can faithfully predict drug interactions between antibiotics targeting the ribosome – a major class of antibiotics that is ideally suited for quantitative studies. To this end, we will use a combination of precise high-throughput growth measurements in two-drug environments, specific synthetic bottlenecks in translation, biochemical assays, and theoretical modeling. Our central hypothesis is that the effects of multiple translation inhibitors on cell physiology, as captured by bacterial growth laws, together with the interplay between specific translation steps targeted by different antibiotics will provide mechanistic explanations for drug interactions. We will characterize the effects of diverse genetic and pharmacological perturbations on key translation parameters, pinpoint the precise mode of action of individual antibiotics, and ultimately develop a theoretical description of translation that captures multiple translation steps. Overall, this project will provide fundamental insights into protein translation and reveal the underlying mechanisms of drug interactions between antibiotics targeting this fundamental process.
DFG Programme
Research Grants