Research

Bacteriophages, or simply phages, are viruses that infect bacteria and are the most abundant biological entities on Earth. Despite their enormous diversity, they all face the same challenge: entering a bacterial cell and taking control of its machinery to produce more viruses. Over billions of years, phages have evolved many different ways to do this, most of which we still know very little about.

Our lab studies how phages take over bacterial cells at the molecular level. We combine proteomics, functional genomics and machine learning to discover which bacterial proteins phages interact with, how these interactions change the way bacteria work and why some phages can kill certain bacterial strains but not others.

We also want to turn this knowledge into better treatments for bacterial infections. With antibiotic resistance on the rise, phages offer a promising alternative because they can be selected, combined and potentially engineered to target specific bacteria. By understanding how phages work, we aim to make phage therapy less dependent on trial and error and more guided by molecular knowledge.

Mining phage dark matter

More than 70% of phage proteins have no assigned function. This “dark matter” represents one of the largest unexplored protein spaces in biology, with millions of sequences spread across tens of thousands of genomes. Viewed from this perspective, phage dark matter is an enormous drug-discovery-like library of proteins shaped by billions of years of evolution to manipulate and control bacterial cells. Phage genomes are therefore both a reservoir of molecular tools and a record of the bacterial processes that phages have evolved to exploit. Our goal is to uncover what these proteins do and turn this largely unexplored genetic diversity into a resource for phage engineering. By identifying the proteins that allow phages to manipulate bacterial cells, we hope to develop ways to improve their ability to infect and kill bacteria, overcome bacterial defenses and ultimately make them more effective antimicrobials.

Key work: Conserved mycobacteriophage protein uses structural mimicry to displace host initiation factors (Parker et al., bioRxiv 2026)

Discovering bacterial defense and phage counter-defense systems

Phages and bacteria have been locked in an evolutionary arms race for billions of years. Every bacterial defense drives the emergence of new viral countermeasures, and every countermeasure selects for new defenses. Just as we have an immune system, bacteria carry their own antiviral defense networks. CRISPR is the best known, but more than a hundred other defense systems have been described in recent years, and many more are still undiscovered. We study this conflict at the molecular scale. We look for these “molecular alarm systems”, learn what triggers them, and identify the phage proteins that switch them off. These anti-defense proteins are remarkably precise tools, refined by evolution to shut down bacterial immunity. Understanding this tug-of-war helps us predict which phages can overcome a pathogen’s defenses, and points to new antibacterial strategies based on natural viral mechanisms.

Key work: Anti-restriction functions of injected phage proteins revealed by peeling back layers of bacterial immunity (Silas et al., Nature Communications 2025)

Mapping the architecture of life

Most proteins do not act alone but as subunits of stable or transient complexes. To take over a cell, a phage must therefore capture, inhibit or repurpose entire molecular machines rather than individual proteins, and it does so across many host complexes at once. This proteome-wide remodelling is difficult to capture with classical approaches, which probe one bait, one interaction or one gene at a time. We use co-fractionation mass spectrometry (CF-MS) as a scalable, system-wide interactomics strategy. By resolving native complexes from infected cells and quantifying their components in parallel, we can follow how thousands of proteins assemble, dissociate or exchange partners during infection, without knowing in advance which complexes are involved. Our group develops machine-learning methods that infer complex organization and interaction networks from these data, providing a scalable framework for mapping proteome architecture.

Schematic of a co-fractionation mass-spectrometry experiment.

Key work: DIP-MS (Frommelt, Fossati et al., Nature Methods 2024) · PCprophet (Fossati, Li et al., Nature Methods 2021)

Designing phage-based therapeutics

By linking viral proteins to their bacterial targets, we can start to engineer phages and phage-derived molecules with defined functions: shutting down essential bacterial pathways, making pathogens more sensitive to antibiotics, or reshaping the course of infection. This reflects the broader goal to move from simply observing what viruses do to designing phages we can predict and control.

Key work: The porin MspA is a phage-specific determinant of mycobacteriophage infection (Kim et al., bioRxiv 2026)

Interested in joining?
We welcome students and postdocs from biology, engineering and computational backgrounds who want to explore the viral frontier. See join the lab for current openings and how to apply.