Clasen Milan A, Santos Marlon D M, Kurt Louise Ulrich, Fischer Juliana, Camillo-Andrade Amanda C, Sales Lucas Albuquerque, de Arruda Campos Brasil de Souza Tatiana, Lima Diogo Borges, Gozzo Fabio C, Valente Richard Hemmi, Duran Rosario, Barbosa Valmir C, Carvalho Paulo C
Laboratory for Structural and Computational Proteomics, Carlos Chagas Institute, Fiocruz-Parana 81310-020, Brazil.
Department of Structural Biology, Leibniz─Forschungsinstitut für Molekulare Pharmakologie (FMP), Berlin 13125, Germany.
J Am Soc Mass Spectrom. 2023 Apr 5;34(4):794-796. doi: 10.1021/jasms.3c00063. Epub 2023 Mar 22.
Complex protein mixtures typically generate many tandem mass spectra produced by different peptides coisolated in the gas phase. Widely adopted proteomic data analysis environments usually fail to identify most of these spectra, succeeding at best in identifying only one of the multiple cofragmenting peptides. We present PatternLab V (PLV), an updated version of PatternLab that integrates the YADA 3 deconvolution algorithm to handle such cases efficiently. In general, we expect an increase of 10% in spectral identifications when dealing with complex proteomic samples. PLV is freely available at http://patternlabforproteomics.org.
复杂的蛋白质混合物通常会产生许多串联质谱，这些质谱由在气相中共分离的不同肽段产生。广泛采用的蛋白质组学数据分析环境通常无法识别这些质谱中的大多数，最多只能成功识别多个共同碎裂肽段中的一个。我们展示了PatternLab V（PLV），它是PatternLab的更新版本，集成了YADA 3去卷积算法以有效处理此类情况。一般来说，我们预计在处理复杂蛋白质组样本时，光谱鉴定的数量会增加10%。PLV可在http://patternlabforproteomics.org免费获取。
