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Mohammad Shahbazy, PhD

Postdoctoral Associate
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Postdoctoral Associate

Biography

Moh is passionate about proteomics, HLA peptidomics, bioinformatics, mass spectrometry, cancer immunotherapy, and antigen discovery research. To pursue these research interests, he obtained an MSc in analytical chemistry and bioinformatics (machine learning) strategies for biomedical data analysis toward early cancer diagnosis. Through a postgraduate research program, he received a PhD in immunology and analytical biochemistry in the Immunoproteomics Lab led by Prof Anthony (Tony) Purcell at the Biomedicine Discovery Institute, Monash University (Australia, 2023). During his PhD candidature, he endeavored to develop high-throughput mass spectrometry-based proteomics and immunopeptidomics platforms to quantitatively analyze stability profiles and PTMs in MHC/HLA-bound peptides. He developed immunoinformatics software tools for unsupervised data visualization and clustering analysis of MHC peptides to mine immunopeptidomes and explore antigen landscapes. Moreover, he developed machine learning models to predict peptide immunogenicity profiles and T-cell responses for virus-derived MHC peptides with physicochemical features and thermostability predictors to shortlist potential targets for more efficient T-cell immunotherapies and vaccines. In 2024, Moh moved to Yale University School of Medicine to join Dr Etienne Caron's Lab and continue his journey in exploring tumor-specific neoantigens by integrating cancer immunology, immunopeptidomics, quantitative proteomics workflows, proteogenomics, multi-omics strategies, and thermal immunopeptidome profiling.

Last Updated on May 14, 2025.

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Education & Training

PhD
Monash University, Department of Biochemistry and Molecular Biology (2023)

Research

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Research interests: Antigen discovery, Cancer immunology, Cancer immunotherapy, Antigen processing and presentation mechanisms, T cells, Immunogenicity, Proteomics, Mass spectrometry, Immunopeptidomics, HLA peptidomics, Proteogenomics, Multi-omics, PTMs, Glycoproteomics, Tumor-specific neoantigens, Non-small cell lung cancer, Thermal immunopeptidome profiling, Bioinformatics, and Machine learning.

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