BACKGROUND Liquid chromatography combined with tandem mass spectrometry is an important tool in proteomics for peptide identification. Liquid chromatography temporally separates the peptides in a sample. The peptides that elute one after another are analyzed via tandem mass spectrometry by measuring the mass-to-charge ratio of a peptide and its fragments. De novo peptide sequencing is the problem of reconstructing the amino acid sequences of a peptide from this measurement data. Past de novo sequencing algorithms solely consider the mass spectrum of the fragments for reconstructing a sequence. RESULTS We propose to additionally exploit the information obtained from liquid chromatography. We study the problem of computing a sequence that is not only in accordance with the experimental mass spectrum, but also with the chromatographic retention time. We consider three models for predicting the retention time and develop algorithms for de novo sequencing for each model. CONCLUSIONS Based on an evaluation for two prediction models on experimental data from synthesized peptides we conclude that the identification rates are improved by exploiting the chromatographic information. In our evaluation, we compare our algorithms using the retention time information with algorithms using the same scoring model, but not the retention time.

中文翻译：

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使用 LC 保留时间信息改进了从头肽测序。

背景液相色谱与串联质谱相结合是蛋白质组学中用于肽鉴定的重要工具。液相色谱在时间上分离样品中的肽。通过串联质谱法通过测量肽及其片段的质荷比来分析一个接一个洗脱的肽。从头肽测序是从该测量数据重建肽的氨基酸序列的问题。过去的从头测序算法仅考虑片段的质谱来重建序列。结果 我们建议额外利用从液相色谱中获得的信息。我们研究了计算一个不仅符合实验质谱而且符合色谱保留时间的序列的问题。我们考虑了三个模型来预测保留时间，并为每个模型开发从头测序的算法。结论基于对来自合成肽的实验数据的两个预测模型的评估，我们得出结论，通过利用色谱信息可以提高识别率。在我们的评估中，我们将使用保留时间信息的算法与使用相同评分模型的算法进行比较，但不比较保留时间。