Potthast Frank, Gerrits Bertran, Häkkinen Jari, Rutishauser Dorothea, Ahrens Christian H, Roschitzki Bernd, Baerenfaller Katja, Munton Richard P, Walther Pascal, Gehrig Peter, Seif Philipp, Seeberger Peter H, Schlapbach Ralph
Functional Genomics Center Zürich, Uni/ETH Zürich, Winterthurerstrasse 190, 8057 Zurich, Switzerland.
J Chromatogr B Analyt Technol Biomed Life Sci. 2007 Jul 1;854(1-2):173-82. doi: 10.1016/j.jchromb.2007.04.020. Epub 2007 Apr 25.
We describe a statistical measure, Mass Distance Fingerprint, for automatic de novo detection of predominant peptide mass distances, i.e., putative protein modifications. The method's focus is to globally detect mass differences, not to assign peptide sequences or modifications to individual spectra. The Mass Distance Fingerprint is calculated from high accuracy measured peptide masses. For the data sets used in this study, known mass differences are detected at electron mass accuracy or better. The proposed method is novel because it works independently of protein sequence databases and without any prior knowledge about modifications. Both modified and unmodified peptides have to be present in the sample to be detected. The method can be used for automated detection of chemical/post-translational modifications, quality control of experiments and labeling approaches, and to control the modification settings of protein identification tools. The algorithm is implemented as a web application and is distributed as open source software.
我们描述了一种统计方法——质量距离指纹图谱，用于自动从头检测主要的肽质量距离，即假定的蛋白质修饰。该方法的重点是全局检测质量差异，而非为各个光谱分配肽序列或修饰。质量距离指纹图谱由高精度测量的肽质量计算得出。对于本研究中使用的数据集，可在电子质量精度或更高精度下检测到已知的质量差异。所提出的方法具有创新性，因为它独立于蛋白质序列数据库工作，且无需任何关于修饰的先验知识。样品中必须同时存在修饰肽和未修饰肽才能被检测到。该方法可用于化学修饰/翻译后修饰的自动检测、实验和标记方法的质量控制，以及控制蛋白质鉴定工具的修饰设置。该算法实现为一个网络应用程序，并作为开源软件发布。
