CAREER: Bioinformatic algorithm and infrastructure development for post-translational modification analysis

Protein post translational modifications (PTMs) play important roles in many aspects of cell biology, including cell growth, differentiation, and survival. Due to the complex nature of PTMs (e.g., various PTM types and sites), there have to date been limitations in the technology used for PTM identification/quantification. This project will overcome such challenges by developing bioinformatic methods that incorporate unique characteristics of data and fully utilize publicly available data, providing comprehensive characterization and quantification of PTMs.

This development will enhance fundamental understanding of functional proteomics in many biological areas, including plant science, biomedicine, and microbiology. Bioinformatic tools generated from the research component of this project will be re-designed as an interactive educational app. Through the associated outreach activities using this app, the project will provide valuable interdisciplinary training to both graduate and undergraduate students, including first-generation college, low-income, and minority students, and inspire high school students to pursue STEM majors.

The research goal of this project is to develop statistical and computational methods to advance the field of mass spectrometry-based PTM analysis. Specifically, this project will establish PTM algorithms and infrastructures that 1) utilize publicly available data to improve PTM identification in biological samples of interest; 2) make use of instrumental technique-specific characteristics of data; 3) provide biologically- meaningful information by properly measuring site occupancy rates and stoichiometry; 4) develop a mixed-model framework to perform differential PTM analysis; and 5) suggest an optimal experimental design (e.g., instrumental technique and sample preparation, sample size calculation) for future studies.

This development will not only advance knowledge across various biological fields, but also help scientists generate critical hypotheses using millions of publicly available data generated previously, without PTM enrichment. The resulting bioinformatic tools and infrastructure will accelerate scientific discoveries using mass spectrometry-based proteomic data. The results of the project can be found at https://github.com/soyoungryu.

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.