AI-powered proteomics against antibiotic resistance
Supervision
Jordi Garcia Ojalvo
UPF, 1st Supervisor
Patrick Aloy
IRB, 2nd Supervisor
Objectives
Employ AI-powered proteomics to devise therapeutic strategies using drug combinations for bacteria resistant to certain antibiotics. Produce raw proteomics data under culture conditions where antibiotics are actively expelled by bacteria. Utilize AI algorithms from WP2 to decipher interactions between various efflux pumps and antibiotics. Leverage the derived insights to formulate antibiotic combinations that collaboratively counteract antibiotic resistance.
Methodology
Conduct classical growth experiments exposing bacteria to various antibiotics. Collaborate with the P1 for sample processing. Analyse the resulting proteomics data using AI tools from the project.
Required skills
Applicants should have an undergraduate/master’s degree in one of the following two areas: (1) Biology/Biotechnology/Biochemistry, or (2) Physics/Mathematics/Computer Science/Biomedical Engineering. The PhD training will complement the skills of the selected candidate to the other area outside of their current expertise. Programming skills (in Python, Julia or C) will be considered positively.
Expected Results
The project will design therapeutic strategies using antibiotic combinations for cases where single antibiotics fail. Insights will be derived from AI analysis of proteomics data from partner experiments, leading to high-impact publications and innovative therapeutic solutions.
Planned Secondments
Host: IRB (P. Aloy), Duration: 2 Months; When: Year 1, Goal: Precision drug development for bacterial resistances.
Host: CRG (E. Sabidó), Duration: 1 Month; When: Year2, Goal: Integration of proteomics data into bacterial models.
Host: EMBL-EBI (J. Saez), Duration: 1 Month; When: Year 3, Goal: Development of Bayesian Models for Systems Biology.
Enrolment in doctoral programs
PhD Program in Biomedicine of Pompeu Fabra University
References
1 Magnesium Flux Modulates Ribosomes to Increase Bacterial Survival. Lee DD, Galera-Laporta L, Bialecka-Fornal M, Moon EC, Shen Z, Briggs SP, Garcia-Ojalvo J, Süel GM. Cell 177, 352-360.e13 (2019).https://doi.org/10.1016/j.cell.2019.01.042
2 Antithetic Population Response to Antibiotics in a Polybacterial Community. Galera-Laporta L, Garcia-Ojalvo J Science Advances 6, eaaz5108 (2020). https://doi.org/10.1126/sciadv.aaz5108
3 Proteomic Signatures of Synergistic Interactions in Antimicrobials. Zhou G, Wang Y, Peng H, Li S, Sun T, Shi Q, Garcia-Ojalvo J, Xie X. Journal of Proteomics 270, 104743 (2023). https://dx.doi.org/10.1016/j.jprot.2022.104743
4 Physiological Cost of Antibiotic Resistance: Insights from a Ribosome Variant in Bacteria. Moon EC, Modi T, Lee DD, Yangaliev D, Garcia-Ojalvo J, Ozkan SB, Süel GM. Science Advances 10, eadq5249 (2024). https://dx.doi.org/10.1126/sciadv.adq5249