Klammer Aaron A, Wu Christine C, MacCoss Michael J, Noble William Stafford
Department of Genome Sciences, Seattle, WA 98195-7730, USA.
Proc IEEE Comput Syst Bioinform Conf. 2005:175-85. doi: 10.1109/csb.2005.44.
Mass spectrometry is a particularly useful technology for the rapid and robust identification of peptides and proteins in complex mixtures. Peptide sequences can be identified by correlating their observed tandem mass spectra (MS/MS) with theoretical spectra of peptides from a sequence database. Unfortunately, to perform this search the charge of the peptide must be known, and current chargestate- determination algorithms only discriminate singlyfrom multiply-charged spectra: distinguishing +2 from +3, for example, is unreliable. Thus, search software is forced to search multiply-charged spectra multiple times. To minimize this inefficiency, we present a support vector machine (SVM) that quickly and reliably classifies multiplycharged spectra as having either a +2 or +3 precursor peptide ion. By classifying multiply-charged spectra, we obtain a 40% reduction in search time while maintaining an average of 99% of peptide and 99% of protein identifications originally obtained from these spectra.
质谱分析法是一种特别有用的技术，可用于快速、可靠地鉴定复杂混合物中的肽和蛋白质。肽序列可通过将其观察到的串联质谱（MS/MS）与来自序列数据库的肽的理论谱进行关联来鉴定。不幸的是，要进行这种搜索，必须知道肽的电荷，而当前的电荷状态确定算法只能区分单电荷和多电荷谱：例如，区分 +2 和 +3 是不可靠的。因此，搜索软件被迫对多电荷谱进行多次搜索。为了将这种低效率降至最低，我们提出了一种支持向量机（SVM），它可以快速、可靠地将多电荷谱分类为具有 +2 或 +3 前体肽离子。通过对多电荷谱进行分类，我们在保持最初从这些谱中获得的肽和蛋白质鉴定平均 99% 的同时，搜索时间减少了 40%。
