CAREER: CDS&E: Protein Structure Prediction from Covalent Labeling Mass Spectrometry Data

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With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Prof. Steffen Lindert and his group at Ohio State University are working to significantly extend the capabilities of mass spectrometry (MS) from simply characterizing the composition of molecules (e.g. metabolites, proteins, small chemical molecules) to helping predict detailed structures (especially of proteins). This is achieved by chemically reacting accessible sites on a protein with specific chemical labels and subsequently using MS to discover which sites on the protein were labeled. Prof. Lindert's "MS-Fold" software then infers the protein structure from this labeling information. Given the importance of proteins in regulating the chemistry of life, better tools for probing protein structure supports better understanding of that chemistry in both healthy and diseased organisms. To help convey these principles to a general audience, the Lindert group is also developing an MS-Fold version of the popular scientific video game Foldit. This work aims to increase public scientific literacy, and to expand interest and engagement in STEM science and technology-related disciplines. The ultimate goal is to increase STEM participation, particularly of underrepresented groups, and to improve STEM education at the undergraduate and graduate level. Sophisticated mass spectrometry (MS) techniques in conjunction with covalently-labeled protein residues can yield important information about protein structure. However, easy and reliable translation of this information into accurate structural models remains particularly challenging. The overall goal of research in the Lindert lab is to develop advanced computational tools that can convert MS-generated covalent labeling data into protein structural models in an automated fashion. Specifically, the research objective is to develop and validate a software tool, termed "MS-Fold", that will allow data generated from covalent labeling MS studies (e.g. solvent-exposed protein residues) to be effectively used to guide protein structure prediction algorithms. MS-Fold is intended to provide the analytical biochemistry community with a user-friendly computational tool with which covalent labeling MS data can be integrated into high-resolution analysis of protein structure and macromolecular interactions. This in turn will dramatically improve interpretability of MS data, constituting a significant advance in the field of structural MS and providing new opportunities for investigators to extract useful information from results of advanced MS experiments. The educational objectives entail novel approaches to interdisciplinary training for complex, joint computational-experimental chemical methods.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.