AI-Driven Discovery and Characterization of Bacterial Secretion Systems and Their Effectors for Addressing Antimicrobial Resistance

With over 1 million bacterial genomes available, 5 billion bacterial protein coding genes, more than 1 million bacteria-related publications and a myriad of relevant databases and resources, bioinformatics has become a key tool to investigate and understand biology in continuously growing data rich environment. In this project we aim to use bioinformatics and artificial intelligence (AI) techniques and methods to investigate bacterial secretion systems (BSS). BSS represent an important biological mechanism used by bacteria to respond to their environment by transporting proteins and other molecules across their membranes or the membranes of eukaryotic cells. These mechanisms can be used in a contact-dependent manner to kill other surrounding bacteria to remove competence or to kill or hijack the behaviour of surrounding eukaryotic cells. BSS have been associated with bacterial virulence, therefore making them interesting from a health perspective specially in the context of antimicrobial resistance (AMR).

In this project the student will investigate, develop and validate novel bioinformatics and AI tools to study and identify BSSs. For this purpose, the project will consist in three main objectives. The first objective will consist in a critical review and benchmarking of the existing tools and methods. The second objective will focus on the development of novel bioinformatics solutions to identify BSS, BSS effectors and the immunity proteins necessary to prevent the toxic effects in the bacteria using them. As in other fields the quality and quantity of data used to develop and train any models is of paramount importance therefore this objective will include the generation of novel updated and extended datasets. Finally, the third objective will experimentally validate the bioinformatics tools and prediction in clinically relevant bacteria.

The student will be trained in G Lopez Campos’ Lab and Valvano’s Lab in the WWIEM providing them with a unique combination of “in silico” and “wet-lab” skills.

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