GENOME DATABASE SEARCHING SOFTWARE FOR IDENTIFYING PROTEINS USING MASS SPECT

使用质谱鉴定蛋白质的基因组数据库搜索软件

• 批准号: 6308821
• 负责人: ALMA L BURLINGAME
• 金额: $ 0.99万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-03-01 至 2002-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6308821
• 关键词: biomedical equipment development; biomedical resource; computers; genome; informatics; proteins; spectrometry; structural biology; technology /technique

The Human Genome Project is rapidly pouring a wealth of DNA sequence data intodatabases at the National Institutes of Health (NIH). Within this vast quantity of data lie the largely not-yet-understood "blueprints" which the individual cells in an organism use to build the array of proteins that serve as the molecular machines for executing the wide variety of biological processes necessary to sustain life. This ever-growing genome database serves as a fundamental resource in accelerating research using mass spectrometry for identification of proteins. The database is much like having the answers to the odd-numbered problems in the backof the book. The difficulty for scientists then becomes how to pose an odd-numbered question and then decipher the answer. Mass spectrometry (MS) techniques produce two types of information from a single sample in a matter of minutes. The first is peptide mass. A so-called "peptide-mass fingerprint" is obtained after using an enzyme to digest a target protein into a mixture of smaller pieces called peptides. The molecular masses of each peptide in the mixture are measured with a mass spectrometer. The resulting set of masses constitutes a "fingerprint." The second is peptide sequence. In a tandem MS experiment, individual peptides in an unseparated mixture can be selectively fragmented. Subsequent measurement of the fragment masses yields data in the form of a"peptide fragment-ion tag" and allows sequence to be nominally derived from the mass differences between adjacent fragments. Because of the complexity of the data produced from these types of experiments and the tremendous sample throughput potential from automation of MS instruments we can develop software for manipulating the data into a form that allows us to posethe question: "Is the sequence of the protein we have just analyzed in the genome database". If so we and our collaborators could then begin to evaluate what is already known about the protein and how it might be important in the particular disease being studied. On those increasingly rare occasions when the particular protein sequence is not in the database, our data could be used to initiate gene-cloning efforts. Moreover, even with weak MS spectra from very low quantities of material, the combination of partly ambiguous mass and partial sequence data thatcan be obtained is of high discriminating power. Hence, genome database searches could leadto unambiguous, high confidence protein identifications because only a minuscule fraction of the enormous number of theoretically possible sequences can exist in the limited genome size of aliving organism.

人类基因组计划正在迅速注入大量 DNA 将数据排序到美国国立卫生研究院的数据库中 （NIH）。 在如此庞大的数据量中，很大程度上隐藏着 尚未理解的“蓝图”，其中的各个细胞 有机体用来构建蛋白质阵列，作为 用于执行各种生物的分子机器 维持生命所必需的过程。 这个不断增长的基因组 数据库是加速研究的基础资源 使用质谱法鉴定蛋白质。 数据库 就像得到奇数问题的答案一样 书的背面。 那么科学家面临的困难就变成了如何 提出一个奇数问题，然后破译答案。 大量的 光谱测定 (MS) 技术可从物质中产生两种类型的信息 只需几分钟即可完成单个样品。 第一个是肽质量。 一个 使用酶后获得所谓的“肽质量指纹” 将目标蛋白质消化成较小碎片的混合物，称为 肽。 混合物中每种肽的分子量为 用质谱仪测量。 得到的质量集 构成“指纹”。 第二个是肽序列。 在一个 串联 MS 实验，未分离混合物中的单个肽 可以选择性地分段。 片段的后续测量 质量产生“肽碎片离子标签”形式的数据，并且 允许名义上从质量差异导出序列 相邻片段之间。 由于数据的复杂性 由这些类型的实验和大量样本产生 我们可以开发的 MS 仪器自动化的吞吐量潜力 用于将数据处理成某种形式的软件，使我们能够 提出问题：“我们刚刚得到的蛋白质序列是 在基因组数据库中进行分析”。如果是这样，我们和我们的合作者 然后可以开始评估对该蛋白质的已知信息 以及它对于正在研究的特定疾病的重要性。 在那些越来越罕见的情况下，当特定的蛋白质 序列不在数据库中，我们的数据可用于启动 基因克隆努力。 此外，即使 MS 谱图很弱， 少量材料，部分模糊质量的组合 且可获得的部分序列数据具有较高的 歧视权力。 因此，基因组数据库搜索可能会导致 明确、高置信度的蛋白质鉴定，因为只有 理论上可能的巨大数量的一小部分 序列可以存在于生物体的有限基因组大小中。