Du Peicheng, Angeletti Ruth Hogue
Department of Developmental and Molecular Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.
Anal Chem. 2006 May 15;78(10):3385-92. doi: 10.1021/ac052212q.
We present an algorithm for the deconvolution of isotope-resolved mass spectra of complex peptide mixtures where peaks and isotope series often overlap. The algorithm formulates the problem of mass spectrum deconvolution as a classical statistical problem of variable selection, which aims to interpret the spectrum with the least number of peptides. The LASSO method is used to perform automatic variable selection. The algorithm also makes use of the quantized distribution of peptide masses in the NCBInr database after in silico trypsin digestion as filters to aid the deconvolution process. Errors in the expected isotope pattern are accounted for to avoid spurious isotope series. The effectiveness of the algorithm is demonstrated with annotated ESI spectrum of known peptides for which the peaks and isotope series are highly overlapping. The algorithm successfully finds all correct masses in the experimental spectrum, except for one spectrum where an additional refinement procedure is required to obtain the correct results. Our results compare favorably to those from a widely used commercial program.
我们提出了一种用于复杂肽混合物同位素分辨质谱反卷积的算法，其中峰和同位素系列常常重叠。该算法将质谱反卷积问题表述为一个经典的变量选择统计问题，旨在用最少数量的肽来解释光谱。采用套索（LASSO）方法进行自动变量选择。该算法还利用计算机模拟胰蛋白酶消化后NCBInr数据库中肽质量的量化分布作为过滤器，以辅助反卷积过程。考虑了预期同位素模式中的误差，以避免出现虚假的同位素系列。通过对已知肽的注释电喷雾电离（ESI）光谱进行验证，该光谱中峰和同位素系列高度重叠，证明了该算法的有效性。除了一个光谱需要额外的细化程序才能获得正确结果外，该算法成功地在实验光谱中找到了所有正确的质量。我们的结果与一个广泛使用的商业程序的结果相比更具优势。
