Department of Computer Science, University of Toronto, Toronto, Canada.
Bioinformatics. 2011 Mar 15;27(6):797-806. doi: 10.1093/bioinformatics/btr017. Epub 2011 Jan 22.
A post-translational modification (PTM) is a chemical modification of a protein that occurs naturally. Many of these modifications, such as phosphorylation, are known to play pivotal roles in the regulation of protein function. Henceforth, PTM perturbations have been linked to diverse diseases like Parkinson's, Alzheimer's, diabetes and cancer. To discover PTMs on a genome-wide scale, there is a recent surge of interest in analyzing tandem mass spectrometry data, and several unrestrictive (so-called 'blind') PTM search methods have been reported. However, these approaches are subject to noise in mass measurements and in the predicted modification site (amino acid position) within peptides, which can result in false PTM assignments.
To address these issues, we devised a machine learning algorithm, PTMClust, that can be applied to the output of blind PTM search methods to improve prediction quality, by suppressing noise in the data and clustering peptides with the same underlying modification to form PTM groups. We show that our technique outperforms two standard clustering algorithms on a simulated dataset. Additionally, we show that our algorithm significantly improves sensitivity and specificity when applied to the output of three different blind PTM search engines, SIMS, InsPecT and MODmap. Additionally, PTMClust markedly outperforms another PTM refinement algorithm, PTMFinder. We demonstrate that our technique is able to reduce false PTM assignments, improve overall detection coverage and facilitate novel PTM discovery, including terminus modifications. We applied our technique to a large-scale yeast MS/MS proteome profiling dataset and found numerous known and novel PTMs. Accurately identifying modifications in protein sequences is a critical first step for PTM profiling, and thus our approach may benefit routine proteomic analysis.
Our algorithm is implemented in Matlab and is freely available for academic use. The software is available online from http://genes.toronto.edu.
翻译后修饰 (PTM) 是蛋白质的一种化学修饰，它是自然发生的。许多这样的修饰，如磷酸化，已知在蛋白质功能的调节中起着关键作用。因此，PTM 干扰与帕金森病、阿尔茨海默病、糖尿病和癌症等多种疾病有关。为了在全基因组范围内发现 PTM，人们最近对分析串联质谱数据产生了浓厚的兴趣，并且已经报道了几种无限制（所谓的“盲目”）PTM 搜索方法。然而，这些方法受到质谱测量和肽中预测修饰位点（氨基酸位置）的噪声的影响，这可能导致错误的 PTM 分配。
为了解决这些问题，我们设计了一种机器学习算法 PTMClust，它可以应用于盲目 PTM 搜索方法的输出，通过抑制数据中的噪声并将具有相同潜在修饰的肽聚类形成 PTM 组，从而提高预测质量。我们表明，我们的技术在模拟数据集上优于两种标准聚类算法。此外，当应用于三个不同的盲目 PTM 搜索引擎 SIMS、InsPecT 和 MODmap 的输出时，我们的算法显著提高了灵敏度和特异性。此外，PTMClust 明显优于另一种 PTM 精炼算法 PTMFinder。我们证明我们的技术能够减少错误的 PTM 分配，提高整体检测覆盖率并促进新的 PTM 发现，包括末端修饰。我们将我们的技术应用于大规模酵母 MS/MS 蛋白质组谱数据集，发现了许多已知和新的 PTM。准确识别蛋白质序列中的修饰是 PTM 分析的关键第一步，因此我们的方法可能有益于常规蛋白质组学分析。
我们的算法是用 Matlab 实现的，可供学术使用。该软件可从 http://genes.toronto.edu 在线获得。
