肽碎片离子强度统计建模算法与应用

The first principle in mass spectrometry-based proteomics is the fragmentation mechanisms of protonated peptides under certain dissociation conditions. Statistical modeling of peptide fragment ion intensities can bring us a comprehensive understanding of peptide fragmentation mechanisms, which is critically essential to annotation of mass spectral peaks, scoring of peptide-spectrum matches, and prediction of theoretical mass spectra. By transforming the problem of intensity pattern modeling into the problem of sequence tagging, we are able to adopt a supervised structured learning model—conditional random fields—to derive intensity models from large annotated mass spectrometry data sets. This structured model has the ability to incorporate the dependence between neighbouring fragment peaks and the influence of various peptide physio-chemical features, which are not considered by existing peptide fragmentation models. Thus, the above three problems can be solved under this single universal computational framework by applying the forward-backward algorithm, the forward or backward algorithm, and the Viterbi algorithm separately. As a result, the confidence of peak annotation, the quality of peptide-spectrum matches and the accuracy of prediction of theoretical mass spectrum are expected to be remarkably improved. Our ultimate goal is to develop a new generation of software system based on the new algorithms to help researchers interpret their proteomic data more accurately, confidently, and efficiently.

质子化肽在特定裂解条件下的碎裂机理是基于质谱蛋白质组学的“第一性原理”问题。对肽碎片离子强度进行统计建模，可以获得对肽碎裂机理的深刻认识，这对解决实验谱峰标注、肽-谱匹配打分、理论质谱预测三个数据分析基本问题来说至关重要。通过将碎片离子强度建模问题转化为序列标注问题，我们得以利用一个有监督结构化学习模型——条件随机场来从海量标注质谱数据中学习碎片离子强度模型。通过此结构化模型，相邻碎片离子间的依赖关系和各种肽理化特征对强度的影响被纳入进来，这是现有肽碎裂模型所不具备的考量因素。由此，在这个统一的计算框架下，上述三个基本问题可以分别通过前向-后向算法、前向或后向算法、Viterbi算法来一一解决。这个计算框架预期能够显著提高谱峰标注的可信度、肽-谱匹配的品质标准和理论质谱预测的准确度。最终目标是基于新算法开发新一代的数据分析软件系统，以帮助研究者更加精确、可靠、高效地解析蛋白质组数据。