CAREER: Algorithm and Software Development for MS-Based Glycomics

印第安纳大学授予唐海旭博士职业奖，表彰他致力于基于质谱学的糖组学的算法和软件开发，这是一种识别和表征细胞内所有功能性低聚糖的研究。目前有三种典型的糖蛋白分析方法：从蛋白质中释放低聚糖，然后通过串联或更高级别的MS分析其结构；利用典型的蛋白质组学方法富集糖蛋白，然后通过串联MS鉴定糖蛋白，而不明确寻址糖链；通过MS/MS纯化和分析糖蛋白，以表征和描述位点特异性糖基化的微观不均一性。糖组分项目需要对MS谱进行密集的计算分析。这项建议的目标是开发计算方法来解决基于MS的糖组学项目中出现的几个基本问题，并开发迫切需要的课程和教程，以教育学生和研究人员糖类信息学。它包括三个具体目标：(1)继续开发从串联质谱学表征寡糖结构的算法和软件，包括它们的序列、分支和连接；(2)开发根据LC/MS/MS分析表征糖蛋白中特定位点糖基化的算法和软件；(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学材料。此外，它还将使复杂样本的糖蛋白组学分析自动化。这项拟议的研究与印第安纳大学信息学学院对研究生进行多学科培训的努力高度相关，特别是在糖类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.