High-throughput annotation of glycan mass spectra

聚糖质谱的高通量注释

• 批准号: 6916805
• 负责人: David Goldberg
• 金额: $ 35.31万
• 依托单位: PALO ALTO RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-06-01 至 2010-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6916805
• 关键词: artificial intelligence; bioinformatics; biomedical automation; chemical structure; chemical synthesis; computational biology; computer program /software; computer system design /evaluation; glycosylation; high throughput technology; mass spectrometry; mathematics; matrix assisted laser desorption ionization; polysaccharides; structural biology; technology /technique development

DESCRIPTION (provided by applicant): The correct functioning of many proteins depends on glycosylation, the addition of sugar molecules (glycans) to selected amino acids in the protein. For example, cancer cells have different glycosylation patterns than ordinary cells, and there is strong evidence that glycoproteins on the surface of egg cells play an essential role in sperm binding. Despite the importance of glycosylation, there are as yet no reliable, high-throughput methods for determining the identity and location of glycans. Glycan identification is currently a manual procedure for experts, involving a combination of chemical assays and mass spectrometry. The automation of the process would have a significant impact on our understanding of this important biological process. The proposed project aims to invent chemical procedures, algorithms, and software for high-throughput analysis of glycan mass spectrometry data. The goal is to bring glycan analysis up to the level of peptide analysis within 3 years. In contrast to peptide analysis, which can leverage genomics data, glycan analysis requires the incorporation of expert knowledge of synthetic pathways, in order to limit the huge number of theoretical combinations of monosaccharides to the much smaller number that are actually synthesized in nature. The project will have to develop novel representations for the evolving expert knowledge, because an exhaustive list- analogous to the human genome- is not currently known. Along with expert knowledge, the project will develop and validate machine learning and statistical techniques for glycan identification. In particular, the project will develop methods for internally calibrating spectra, and will learn fragmentation patterns that can statistically distinguish different types of glycosidic linkages.

描述（由申请人提供）：许多蛋白质的正确功能取决于糖基化，将糖分子（聚糖）添加到蛋白质中选定的氨基酸中。例如，癌细胞的糖基化模式与普通细胞不同，并且有强有力的证据表明，卵细胞表面上的糖蛋白在精子结合中起着至关重要的作用。尽管糖基化的重要性，但尚无可靠的高通量方法来确定糖的身份和位置。聚糖识别目前是专家的手动程序，涉及化学测定和质谱法的组合。该过程的自动化将对我们对这一重要生物学过程的理解产生重大影响。拟议的项目旨在发明化学程序，算法和软件，用于对聚糖质谱数据进行高通量分析。目的是在3年内将聚糖分析提高到肽分析的水平。与可以利用基因组数据的肽分析相反，聚糖分析需要纳入合成途径的专家知识，以便将单糖的大量理论组合限制为实际上合成的数量较小的数量。该项目将不得不为不断发展的专家知识开发新的表现，因为目前尚不清楚与人类基因组相似的详尽列表。除了专家知识外，该项目将开发和验证机器学习和统计技术，以识别聚糖。特别是，该项目将开发用于内部校准光谱的方法，并将学习可以从统计上区分不同类型的糖苷链接的碎片化模式。