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

蛋白质翻译后修饰(PTM)在细胞生物学的许多方面发挥着重要作用，包括细胞生长、分化和生存。由于PTM的性质复杂(例如，各种PTM类型和地点)，迄今在用于PTM识别/量化的技术方面存在局限性。该项目将通过开发生物信息学方法来克服这些挑战，这些方法结合了数据的独特特征，并充分利用了可公开获得的数据，提供了对多年期技术指标的全面描述和量化。这一进展将加强对许多生物学领域的功能蛋白质组学的基本理解，包括植物科学、生物医学和微生物学。从该项目的研究部分产生的生物信息工具将被重新设计为互动教育应用程序。通过使用这款APP的相关推广活动，该项目将为研究生和本科生提供有价值的跨学科培训，包括第一代大学生、低收入家庭和少数族裔学生，并激励高中生攻读STEM专业。本项目的研究目标是发展统计和计算方法，以促进基于质谱学的相变分析领域的发展。具体地说，该项目将建立PTM算法和基础设施，1)利用可公开获得的数据来改进对感兴趣的生物样本的PTM鉴定；2)利用仪器技术特有的数据特征；3)通过适当地测量场地占有率和化学计量学来提供具有生物意义的信息；4)开发一个混合模型框架来执行不同的PTM分析；以及5)为未来的研究提出最佳的实验设计(例如，仪器技术和样品制备、样本量计算)。这一发展不仅将推动各个生物学领域的知识进步，还将帮助科学家使用先前生成的数百万公开可用数据来生成关键假设，而不需要进行PTM浓缩。由此产生的生物信息学工具和基础设施将利用基于质谱学的蛋白质组数据加速科学发现。该项目的结果可以在https://github.com/soyoungryu.This上找到，该奖项反映了国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.