OPTIMIZING SEARCH CONDITIONS FOR THE MASS FINGERPRINT-BASED ID OF PROTEINS

优化基于质量指纹的蛋白质识别的搜索条件

• 批准号: 7954115
• 负责人: David Fenyo
• 金额: $ 0.36万
• 依托单位: ROCKEFELLER UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-03-01 至 2010-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7954115
• 关键词: Albumins; Algorithms; Amino Acid Sequence; Classification; Collection; Computer Retrieval of Information on Scientific Projects Database; Data; Fingerprint; Funding; Grant; Human; Institution; Outcome; Peptide Sequence Determination; Peptides; Plasma; Plasma Proteins; Probability; Procedures; Proteins; Proteome; Reporting; Research; Research Personnel; Resources; Site; Source; Testing; United States National Institutes of Health; base; helix-loop-helix protein differentiation inhibitor; improved; macromolecule; research study

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The two central problems in protein identification by searching a protein sequence collection with MS data are the optimal use of experimental information to allow for identification of low abundance proteins and the accurate assignment of the probability that a result is false. For comprehensive MS-based protein identification, it is necessary to choose an appropriate algorithm and optimal search conditions. We report a systematic study of the quality of PMF-based protein identifications under different sequence collection search conditions using the Probability algorithm, which assigns the statistical significance to each result. We employed 2244 PMFs from 2-DE-separated human blood plasma proteins, and performed identification under various search constraints: mass accuracy (0.01-0.3 Da), maximum number of missed cleavage sites (0-2), and size of the sequence collection searched (5.6 x 10(4)-1.8 x 10(5)). By counting the number of significant results (significance levels 0.05, 0.01, and 0.001) for each condition, we demonstrate the search condition impact on the successful outcome of proteome analysis experiments. A mass correction procedure utilizing mass deviations of albumin matching peptides was tested in an attempt to improve the statistical significance of identifications and iterative searching was employed for identification of multiple proteins from each PMF.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 通过用MS数据搜索蛋白质序列集合进行蛋白质鉴定的两个中心问题是实验信息的最佳使用以允许鉴定低丰度蛋白质和准确分配结果为假的概率。对于基于质谱的蛋白质鉴定，需要选择合适的算法和最优的搜索条件。我们报告了一个系统的研究质量的PMF为基础的蛋白质鉴定在不同的序列收集搜索条件下，使用概率算法，分配的统计意义，每个结果。我们使用了2244个来自2-DE分离的人血浆蛋白的PMF，并在各种搜索约束条件下进行鉴定：质量准确度（0.01-0.3 Da），缺失切割位点的最大数量（0-2），以及搜索的序列集合的大小（5.6 x 10（4）-1.8 x 10（5））。通过计算每个条件下显著结果的数量（显著性水平0.05，0.01和0.001），我们证明了搜索条件对蛋白质组分析实验成功结果的影响。利用白蛋白匹配肽的质量偏差的质量校正程序进行了测试，试图提高鉴定的统计学意义，并采用迭代搜索来鉴定来自每个PMF的多种蛋白质。