CAREER: Algorithm and Software Development for MS-Based Glycomics

印第安纳州大学授予唐海旭博士职业生涯奖，以从事基于质谱的糖组学的算法和软件开发，该研究旨在识别和表征细胞内的所有功能性寡糖。目前有三种典型的方案来分析糖蛋白：寡糖从蛋白质中释放，然后通过串联或甚至更高的MS分析，以阐明其结构;糖蛋白富集，然后通过串联MS使用典型的蛋白质组学方法鉴定，而不明确地解决聚糖;和糖蛋白纯化和通过MS/MS分析，以表征和分析位点特异性糖基化的微观异质性。糖组学项目需要对MS谱进行密集的计算分析。该提案的目的是开发计算方法，以解决基于MS的糖组学项目中出现的几个基本问题，并开发急需的课程和教程，用于教育学生和研究人员在糖信息学。它包括三个具体目标：（1）继续开发用于从串联质谱中表征寡糖结构（包括其序列、分支和连接）的算法和软件;（2）开发用于从LC/MS/MS分析中表征糖蛋白中位点特异性糖基化的算法和软件;以及（3）开发糖信息学和蛋白质组信息学的新课程。这些目标的成功将为糖组学社区提供有用的软件工具和教材。此外，它还将实现复杂样品糖蛋白组学分析的自动化。拟议的研究与PI在印第安纳州大学信息学院对研究生进行多学科培训的努力高度相关，特别是在糖组和蛋白质组信息学领域。

Indiana University is given a CAREER award for Dr. Haixu Tang to pursue algorithm and software development for Mass-Spectra-based glycomics, the study of identifying and characterizing all functional oligosaccharides inside a cell. Currently there are three typical protocols to analyze glycoproteins: oligosaccharides are released from proteins and then are analyzed by tandem or even higher MS to elucidate their structures; glycoproteins are enriched and then identified by tandem MS using typical proteomics methodologies without addressing glycans explicitly; and glycoproteins are purified and analyzed by MS/MS to characterize and profile the microheterogeneity of site-specific glycosylations. Glycomics projects require intensive computational analysis of the MS spectra. The objective of this proposal is to develop computational methods for solving several fundamental problems emerging from MS-based glycomics projects, and to develop urgently needed courses and tutorials for educating students and researchers in glycoinformatics. It consists of three specific aims: (1) to continue developing algorithms and software for characterizing oligosaccharide structures, including their sequences, branchings and linkages, from tandem mass spectra; (2) to develop algorithms and software for characterizing site-specific glycosylations in glycoproteins from their LC/MS/MS analyses; and (3) to develop new courses in glycoinformatics and proteome informatics. The success of these goals will provide useful software tools and teaching materials to the glycomics community. Furthermore, it will enable the automation of glycoproteomics analysis of complex sample. The proposed research is highly related to the PI's efforts for the multidisciplinary training for graduate students, particularly in the areas of glycome and proteome informatics, in the School of Informatics at Indiana University.