Our research interests revolve around three main areas:
The Zoonotic and Pathogenic Potential of Emerging Viruses
Over the past two decades, outbreaks of zoonotic viruses—such as SARS-CoV, MERS, SARS-CoV-2, Zika virus, Ebola virus, and Influenza A—have shown how rapidly a local spillover can escalate into a global health, economic, and social crisis. Preventing the next pandemic requires more than reactive measures: it demands the ability to anticipate viral threats before they emerge. Along these lines, a promising avenue combines epidemiological surveillance, computational tools, and experimental validation to rapidly evaluate the potential risk posed by newly discovered viruses and emerging variants.
Our lab develops computational tools that allow us to evaluate the zoonotic risk and immune evasion potential of emerging viruses. By integrating these insights, we aim to create an early-warning framework that helps the scientific and public health community stay ahead of the next viral threat.
Host Genetic Variation in Viral Infections
The outcome of a viral infection is shaped by a dynamic interplay between pathogen and host genetic factors. While most infections are self-limiting, specific genetic variants in the host can confer resistance, increase susceptibility, or modulate disease severity.
Our goal is to uncover genotype–phenotype associations that explain this variability. We integrate protein structure, functional properties, and multi-omics data within machine learning frameworks to reveal how host genetic diversity influences infection, immunity, and disease progression—ultimately guiding the development of personalized therapeutic strategies.
Molecular Interactions Underpinning Viral Infection Diseases
Viruses rely on the host’s cellular machinery to complete their life cycle—from entry and replication to immune evasion. Viruses achieve such an intimate relationship with their host through interactions with hosts proteins, finely tuning the underlying protein interaction network for their own benefit.
We characterize these protein-protein interactions (PPIs) from structural, functional, and evolutionary perspectives using computational methods. By understanding these interactions in detail, we can identify promising therapeutic targets and engineer proteins with novel binding properties and therapeutic potential.