Jaime Fernandez MacGregor
Modeling human network perturbations by bacterial proteins: from bioinformatics to high-throughput experimental interactomics and back.
Team: Christine Brun (TAGC) - Renaud Vincentelli (AFMB) - Andreas Zanzoni (TAGC)
October 2021 - present | CENTURI PhD student
2019 - 2020 | MSc in Complex Systems Modeling at King's College, London
2018 - 2019 | Freelance Consulting and Independent Research
2016 - 2017 | Research Associate, Modern Meadow, New York City
2015 - 2016 | Engineering & Design Associate, VAM Biodesign, New York City
2012 - 2015 | Bachelor of Sciences - Major in Chemical Engineering & Minor in Materials Science, Drexel University, Philadelphia
About his PhD project
Bacterial proteins, from commensals or pathogens, perturb their host protein interaction network by interacting with resident proteins. Do they modify the host interaction network by impairing particular interactions? Do they displace certain regular interactors of their target proteins? How do they modify network topology? This project aims at answering these questions by combining computational predictions of interactions, high-throughput protein production and quantitative experimental interaction validation and network modeling.
Bacteria use secreted proteins displaying a range of “host-like” interaction to hijack the human interaction networks for their own profit, thereby impacting normal cellular functions.
We will (i) select potential instances of interaction impairment between bacterial secreted and host proteins by computationally identify interaction interfaces mediated by host-like interaction elements using our MimicINT method (Zanzoni et al., 2017), based on sequence and structural data analysis, (ii) produce bacterial proteins and domains to confirm and quantify in vitro the interactions using our HTP quantitative Hold-up assay (Vincentelli et al., 2015), (iii) implemented the experimental information in the MimicINT method and pipeline to improve the interaction predictions between host and foreign proteins, (iv) predict and confirm experimentally high affinity interactors to block the bacterial hijacking and finally (v) quantitatively model the functional consequences of the interaction perturbations, based on the observed changes in network topology.