NSF Award
2403561
With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Professor Michael Marty and his group at the University of Arizona are collaborating with Genentech, Inc. to develop new algorithms and experimental methods for analysis of complex mass spectrometry data. Analysis of intact proteins by mass spectrometry is widely used in a variety of industries. It is especially important for quality control of proteins produced as biopharmaceutical products. However, data analysis for these large proteins is a key bottleneck. To overcome this challenge, the Marty team is developing new computational tools and integrating these tools into a unified open-source software project, which will enable widespread use by academic researchers, core facilities, and industry labs. These research objectives are complemented by educational activities to provide online resources to teach general coding skills and how to use the new software developed by this project. By solving key bottlenecks in data analysis and enhancing professional development, this project will enhance the national infrastructure for research, improve economic competitiveness, and foster partnerships with academic, industrial, and governmental researchers. <br/><br/>Data analysis is a critical bottleneck limiting more widespread use of intact mass spectrometry (MS) in industry applications. To address these data analysis challenges, under this GOALI (Grant Opportunities for Academic Liaisons with Industry) award, the Marty group at the University of Arizona and industrial collaborators at Genentech, Inc. are developing novel algorithms for analysis of mixed resolution spectra and two-dimensional data sets generated by coupling online chromatography with MS. They also will develop multiplexed injection strategies, both experimental and computational, to couple these online separations with charge detection-MS. Each of these objectives is expected to advance knowledge by providing new types of data analysis not currently possible, and each integrates dimensions of the data that have been previously inaccessible. To enable practical implementation of these new tools in a unified package, the algorithms are being integrated into an open-source software package with a user interface. These research objectives are accompanied by educational objectives to develop online resources to teach the use of these new software workflows and to teach fundamental skills related to MS science.<br/><br/>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.
