Pfeifer Nico, Leinenbach Andreas, Huber Christian G, Kohlbacher Oliver
Eberhard Karls University Tubingen, Germany.
J Proteome Res. 2009 Aug;8(8):4109-15. doi: 10.1021/pr900064b.
The combination of a two-dimensional peptide separation scheme based on reversed-phase and ion-pair reversed phase HPLC with a computational method to model and predict retention times in both dimensions is described. The algorithm utilizes statistical learning to establish a retention model from about 200 peptide retention times and their corresponding sequences. The application of retention time prediction to the peptides facilitated an increase in true positive peptide identifications upon lowering mass spectrometric scoring thresholds and concomitantly filtering out false positives on the basis of predicted retention times. An approximately 19% increase in the number of peptide identifications at a q-value of 0.01 was achievable in a whole proteome measurement.
本文描述了一种基于反相和离子对反相高效液相色谱的二维肽分离方案与一种用于模拟和预测二维保留时间的计算方法的结合。该算法利用统计学习从约200个肽的保留时间及其相应序列建立保留模型。将保留时间预测应用于肽，在降低质谱评分阈值时促进了真阳性肽鉴定数量的增加，并同时基于预测的保留时间滤除假阳性。在全蛋白质组测量中，在q值为0.01时，肽鉴定数量可实现约19%的增加。
