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

这个子项目是利用由NIH/NCRR资助的中心拨款提供的资源的许多研究子项目之一。子项目和调查员(PI)可能从另一个NIH来源获得了主要资金，因此可能会出现在其他CRISE条目中。列出的机构是针对中心的，而不一定是针对调查员的机构。在这篇文章中，我们提出了一种基于强度的概率函数来从串联质谱库和氨基酸序列数据库中识别多肽。该函数是中心极限定理的近似，它显式地依赖于累积乘积离子强度、肽的乘积离子数和累积强度的期望值。我们比较了使用新的评分函数和以前的算法中的评分函数进行数据库搜索的结果，这些算法实现了超几何概率、泊松模型和互相关分。对于标准的蛋白质混合物(由五种已知蛋白质的混合物产生的串联质谱图)，我们用所有评分方案生成接收器操作曲线。接收器工作曲线表明，基于共享峰计数的概率方法(如泊松模型和超几何模型)是匹配高质量串联质谱图的最具体的方法。当匹配低质量串联质谱图时，基于强度的方法(中心极限模型)和强度建模的方法(互相关)更敏感，因为在低质量串联质谱图中，共有峰的数量不足以正确识别多肽。与基于强度的概率方法相比，互相关方法显示出较小的优势。

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.