William Fondrie

Will is a computational biologist whose research generally revolves around developing methods to interpret data from proteomics and mass spectrometry experiments. In any proteomics experiment, a fundamental task is to match a mass spectrum to a peptide. Will’s postdoctoral research has focused applying machine learning strategies to enhance the detection of peptides experiments in two settings: chemical cross-linking and small-scale proteomics experiments.

These methods aid in real-world biological applications. Chemical cross-linking is used in proteomics to gain details into 3D protein structure. Improving cross-linked peptide detection offers opportunities for improved drug design and understanding of protein-protein interactions. Likewise, the development of methods for small-scale experiments impacts the analysis of clinical assays and data from single-cell proteomics technologies.

The methods that Will develops serve to advance biological understanding from mass spectrometry data and are available as open-source tools for the broader community.