Structural Chemistry & Bioinformatics Services for Vaccine, Antibody, Peptide & Drug Design

I help research teams design and prioritize vaccines, antibodies, antiviral drugs, and other therapeutic candidates using computational approaches, and connect these predictions with experimental validation. My strength is bringing together immunology, structural biology, computational chemistry, bioinformatics, and artificial intelligence to answer practical questions in infectious-disease research: Which antigen should we target? Which epitopes are most relevant? How will a peptide interact with MHC molecules? Which compounds could inhibit a viral protein? How stable is a predicted interaction? And, importantly, which candidates are worth taking forward into the laboratory?

I am Dr. Zahed Khatooni, a Scientist I at the Vaccine and Infectious Disease Organization (VIDO), University of Saskatchewan, and an Adjunct Professor in the Department of Mathematics and Statistics. My research focuses primarily on computational vaccine design, immunoinformatics, antibody and antiviral discovery, molecular modelling, and molecular dynamics simulations. I work across the early stages of discovery, from analysing pathogen genomes and proteins to identifying and optimizing candidates for experimental testing.

For vaccine development, I can contribute to antigen discovery, B- and T-cell epitope prediction, MHC and SLA analysis, population-coverage strategies, structural modelling of peptide–MHC interactions, and multi-epitope or subunit vaccine design. One area of particular interest is understanding immune diversity in outbred populations. For example, I have developed and applied structure-based approaches for selecting representative swine leukocyte antigen molecules and identifying epitopes that could provide broader immune coverage in pigs. My work has contributed to vaccine research involving PRRSV, African swine fever virus, PEDV, Lawsonia intracellularis, SARS-CoV-2 and related coronaviruses.

For drug and antiviral discovery, I use structure-based approaches including molecular docking, high-throughput virtual screening, molecular dynamics simulations, protein–ligand interaction analysis, and free-energy calculations. These approaches allow me to screen large numbers of compounds computationally and progressively narrow them to candidates with stronger mechanistic and structural support before experimental evaluation. I have also worked on antiviral discovery against viral targets, including influenza-related proteins.

I can also contribute to antibody-related research, including structural modelling of antibodies and antigens, analysis of antibody–antigen interactions, protein–protein docking, and computational support for monoclonal antibody design. More broadly, I am interested in using structural information to understand why particular immune or therapeutic interactions work and how they can be improved.

Increasingly, I am integrating artificial intelligence and machine learning into these workflows. My current interests include deep learning for epitope prediction, active learning for more efficient candidate selection, graph-based approaches, and uncertainty-aware prioritization. Rather than using AI as a standalone prediction tool, I am particularly interested in combining machine learning with biological knowledge, structural information, and molecular simulation to generate predictions that can be experimentally tested.

An important part of what I bring to collaborative projects is that my background is not exclusively computational.

I have experience working in multidisciplinary infectious-disease teams and have contributed as a co-applicant to funded projects involving T-cell vaccine development against African swine fever virus and oral vaccines against PRRSV and PEDV, among other research activities. My work has resulted in publications in journals including Bioinformatics, PLOS ONE, Scientific Reports, Cell Reports, and Vaccine. I also supervise students and research staff in computational biology, bioinformatics, immunology, and related areas.

For a Horizon Europe consortium, I can therefore contribute across several interconnected areas: computational vaccine and antigen design, antibody modelling, antiviral and drug discovery, immunoinformatics, structural bioinformatics, molecular dynamics, AI/ML-based prediction, host–pathogen interaction analysis, and computational prioritization of candidates for experimental validation.

My goal is not simply to generate predictions, but to use computation to reduce the experimental search space, explain biological mechanisms, identify stronger candidates, and help move promising vaccines, antibodies, and therapeutics toward experimental validation and eventual application.