A neural network approach to the identification of b-/y-ions in MS/MS spectra

J. P. Cleveland, J. Rose
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引用次数: 4

Abstract

The effectiveness of de novo peptide sequencing algorithms depends on the quality of MS/MS spectra. Since most of the peaks in a spectrum are uninterpretable `noise' peaks it is necessary to carefully pre-filter the spectra to identify the `signal' peaks that likely correspond to b-/y-ions. Selecting the optimal set of peaks for candidate peptide generation is essential for obtaining accurate results. A careful balance must be maintained between the precision and recall of peaks that are selected for further processing and candidate peptide generation. If too many peaks are selected the search space will be too large and the problem becomes intractable. If too few peaks are selected cleavage sites will be missed, the resulting candidate peptides will have large gaps, and sequencing results will be poor. For this reason pre-filtering of MS/MS spectra and accurate selection of peaks for peptide candidate generation is essential to any de novo peptide sequencing algorithm. We present a novel neural network approach for the selection of b-/y-ions using known fragmentation characteristics, and leveraging neural network probability estimates of flanking and complementary ions. We show a significant improvement in precision and recall of peaks corresponding to b-/y-ions and a reduction in search space over approaches used by other de novo peptide sequencing algorithms.
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MS/MS光谱中b-/y-离子识别的神经网络方法
从头开始的肽测序算法的有效性取决于质谱/质谱的质量。由于光谱中的大多数峰是不可解释的“噪声”峰,因此有必要仔细地对光谱进行预滤波,以识别可能对应于b-/y-离子的“信号”峰。为候选肽生成选择最佳的峰集对于获得准确的结果至关重要。必须在选择用于进一步处理和候选肽生成的峰的精度和召回率之间保持仔细的平衡。如果选择的峰值太多,则搜索空间太大,问题变得难以处理。如果选择的峰过少,则会错过选择的裂解位点,得到的候选肽将有很大的间隙,测序结果将很差。因此,MS/MS谱的预滤波和候选肽生成峰的准确选择对于任何新的肽测序算法都是必不可少的。我们提出了一种新的神经网络方法,利用已知的碎片特征来选择b-/y离子,并利用神经网络对侧翼和互补离子的概率估计。与其他从头开始的肽测序算法相比,我们在b-/y-离子对应的峰的精度和召回率方面有了显着提高,并且减少了搜索空间。
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