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首页> 外文会议>Institution of Engineering and Technology Seminar on Signal Processing for Genomics >Bioinformatic Approaches to Improve the Identification of Peptides from Proteomics Experiments (PPT)
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Bioinformatic Approaches to Improve the Identification of Peptides from Proteomics Experiments (PPT)

机译：从蛋白质组学实验中改善肽鉴定的生物信息化方法（PPT）

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The accurate analysis of the proteome using mass spectrometry plays an important role in the understanding of many of the physiological processes that occur in an organism and has become a standard tool used in the identification of proteins. This identification of proteins is a challenging one and relies upon bioinformatics tools to characterize proteins via their proteolytic peptides which are identified via characteristic mass spectra generated after their ions undergo fragmentation in the gas phase within the mass spectrometer. An important problem associated with the accurate identification of peptides from mass spectrometry is whether or not a particular peptide is likely to be detected in a standard proteomics experiment, this can be dependant on a number of factors including the physiochemical properties of the peptide itself as well as the mass spectrometer used in the experiment. A machine learning approach was applied to find peptide fragmentation patterns based on different properties of the peptide sequence and we are able to predict which peptide(s) are likely to be detected in a standard proteomics experiment. The task of protein identification is made even more challenging by the occurrence of partial enzymatic protein cleavage, resulting in peptides with internal missed cleavage sites, as proteases frequently fail to digest proteins to their limit peptides. Typically, up to 1 of these "missed cleavages" are considered by the bioinformatics search tools, usually after digestion of the in silico proteome by trypsin. Using rules derived from information theory, we were able to "mask" candidate protein databases so that confident missed cleavage sites need not be considered for in silico digestion. We show that that this leads to an improvement in database searching, with two different search engines.
机译：使用质谱法的准确分析在理解生物体中发生的许多生理过程中起重要作用，并且已成为用于鉴定蛋白质的标准工具。这种蛋白质的这种鉴定是一个具有挑战性的蛋白质，并且依赖于生物信息学工具，以通过其蛋白水解肽表征蛋白质，该蛋白水解肽通过在它们的离子在质谱仪内的气相中经历碎片后产生的特征质谱而鉴定。与质谱法精确鉴定肽的准确鉴定的重要问题是在标准蛋白质组学实验中可能检测到特定肽，这可以取决于许多因素，包括肽本身的生理化学性质作为实验中使用的质谱仪。应用了一种机器学习方法，用于基于肽序列的不同性质去寻找肽片段化模式，并且我们能够预测在标准蛋白质组学实验中可能检测到哪种肽。由于部分酶促蛋白质切割的发生，蛋白质鉴定的任务更具挑战性，导致内部错过的裂解位点的肽，因为蛋白酶经常未能消化蛋白质到它们的极限肽。通常，在通过胰蛋白酶消化硅蛋白质中的消化后，这些“错过的切割”中最多的这些“错过的裂缝”中最多审议。利用来自信息理论的规则，我们能够“掩盖”候选蛋白质数据库，以便在硅消化中不需要考虑充满耐受的错过的裂解位点。我们表明这导致数据库搜索的改进，两个不同的搜索引擎。

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