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.

人类基因组项目正在迅速倾泻大量DNA 国立卫生研究院的序列数据拓扑库 （NIH）。 在这大量数据中，很大程度上存在 尚未被单个单元格在一个单个细胞中的“蓝图” 有机体用来建立一系列蛋白质 用于执行各种生物学的分子机器 维持生命所需的过程。 这个不断增长的基因组 数据库是加速研究的基本资源 使用质谱法以鉴定蛋白质。 数据库 就像在奇怪的问题中有答案 这本书。 然后，科学家的困难变成了如何 提出一个奇数的问题，然后解密答案。 大量的 光谱法（MS）技术从A产生两种类型的信息 单个样本在几分钟内。 第一个是肽质量。 一个 使用酶后获得所谓的“肽质量指纹” 将靶蛋白消化成较小碎片的混合物 肽。 混合物中每个肽的分子质量是 用质谱仪测量。 由此产生的群众 构成“指纹”。 第二个是肽序列。 在 串联MS实验，未分离混合物中的单个肽 可以选择性碎片。 随后测量片段 质量以“肽片段-ion标签”的形式产生数据，并且 允许序列名义上源自质量差异 在相邻的碎片之间。 由于数据的复杂性 从这些类型的实验和巨大样本中产生 MS仪器自动化的吞吐量潜力我们可以开发 将数据操纵成形式的软件，使我们能够 Posethe问题：“我们只有蛋白质的顺序 在基因组数据库中进行了分析。 然后可以开始评估已经知道的蛋白质 以及在研究的特定疾病中可能很重要。 在那些越来越罕见的特定蛋白质的情况下 序列不在数据库中，我们的数据可用于启动 基因克隆的努力。 而且，即使是非常弱的MS光谱 低量的材料，部分模棱两可的质量组合 可以获得的部分序列数据很高 区分力量。 因此，基因组数据库搜索可能会导致 明确的高置信蛋白鉴定，因为只有 理论上可能的巨大数量的小部分 序列可以存在于有限的生物体基因组大小中。