CENTRAL LIMIT THEOREM AS AN APPROXIMATION FOR INTENSITY-BASED SCORING FUNCTION

作为基于强度的评分函数的近似的中心极限定理

• 批准号: 7420812
• 负责人: John R Yates III
• 金额: $ 2.01万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-20 至 2007-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7420812
• 关键词: information systems; ions; model; peptides; proteins

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. In this paper, we present an intensity-based probability function to identify peptides from tandem mass spectra and amino acid sequence databases. The function is an approximation to the central limiting theorem, and it explicitly depends on the cumulative product ion intensities, number of product ions of a peptide, and expectation value of the cumulative intensity. We compare the results of database searches using the new scoring function and scoring functions from earlier algorithms, which implement hypergeometric probability, Poisson's model, and cross-correlation scores. For a standard protein mixture (tandem mass spectra generated from the mixture of five known proteins), we generate receiver operating curves with all scoring schemes. The receiver operating curves show that the shared peaks count-based probability methods (like Poisson and hypergeometric models) are the most specific for matching high-quality tandem mass spectra. The intensity-based (central limit model) and intensity-modeled (cross-correlation) methods are more sensitive when matching low-quality tandem mass spectra, where the number of shared peaks is insufficient to correctly identify a peptide. Cross-correlation methods show a small advantage over the intensity-based probability method.

该子项目是利用NIH/NCRR资助的中心赠款提供的资源的许多研究子项目之一。子项目和研究者（PI）可能从另一个NIH来源获得主要资金，因此可以在其他CRISP条目中表示。所列机构为中心，不一定是研究者所在机构。在本文中，我们提出了一个基于强度的概率函数来识别肽串联质谱和氨基酸序列数据库。该函数是对中心极限定理的近似，并且它明确地取决于累积产物离子强度、肽的产物离子的数目和累积强度的期望值。我们比较数据库搜索的结果，使用新的评分函数和评分函数从早期的算法，实现超几何概率，泊松模型，和互相关分数。对于标准蛋白质混合物（从五种已知蛋白质的混合物产生的串联质谱），我们生成具有所有评分方案的接收器操作曲线。接收器工作曲线表明，共享峰计数为基础的概率方法（如泊松和超几何模型）是最具体的匹配高质量的串联质谱。当匹配低质量串联质谱时，基于强度（中心极限模型）和强度建模（互相关）的方法更灵敏，其中共享峰的数量不足以正确识别肽。互相关方法显示出比基于强度的概率方法小的优势。