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首页> 外文会议>International conference on applications of evolutionary computation;Evostar conferences >GA-Novo: De Novo Peptide Sequencing via Tandem Mass Spectrometry Using Genetic Algorithm
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GA-Novo: De Novo Peptide Sequencing via Tandem Mass Spectrometry Using Genetic Algorithm

机译：GA-Novo：使用遗传算法通过串联质谱进行从头进行肽测序

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摘要

Proteomics is the large-scale analysis of the proteins. The common method for identifying proteins and characterising their amino acid sequences is to digest the proteins into peptides, analyse the peptides using mass spectrometry and assign the resulting tandem mass spectra (MS/MS) to peptides using database search tools. However, database search algorithms are highly dependent on a reference protein database and they cannot identify peptides and proteins not included in the database. Therefore, de novo sequencing algorithms are developed to overcome the problem by directly reconstructing the peptide sequence of an MS/MS spectrum without using any protein database. Current de novo sequencing algorithms often fail to construct the completely matched sequences, and produce partial matches. In this study, we propose a genetic algorithm based method, GA-Novo, to solve the complex optimisation task of de novo peptide sequencing, aiming at constructing full length sequences. Given an MS/MS spectrum, GA-Novo optimises the amino acid sequences to best fit the input spectrum. On the testing dataset, GA-Novo outperforms PEAKS, the most commonly used software for this task, by constructing 8% higher number of fully matched peptide sequences, and 4% higher recall at partially matched sequences.
机译：蛋白质组学是对蛋白质的大规模分析。鉴定蛋白质和表征其氨基酸序列的常用方法是将蛋白质消化成肽，使用质谱分析肽，并使用数据库搜索工具将所得的串联质谱（MS / MS）分配给肽。但是，数据库搜索算法高度依赖于参考蛋白质数据库，因此无法识别数据库中未包含的肽和蛋白质。因此，开发了从头测序算法以通过不使用任何蛋白质数据库直接重建MS / MS谱图的肽序列来克服该问题。当前的从头测序算法经常无法构建完全匹配的序列，并产生部分匹配。在这项研究中，我们提出了一种基于遗传算法的方法，即GA-Novo，以解决从头肽测序的复杂优化任务，旨在构建全长序列。给定MS / MS谱图，GA-Novo可以优化氨基酸序列，使其最适合输入谱图。在测试数据集上，GA-Novo通过构建比完全匹配的肽序列高8％的数量，并在部分匹配的序列上提高4％的查全率，胜过了该任务最常用的软件PEAKS。

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