DeepRescore: Leveraging Deep Learning to Improve Peptide Identification in Immunopeptidomics.
DeepRescore: Leveraging Deep Learning to Improve Peptide Identification in Immunopeptidomics.
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DOI：
10.1002/pmic.201900334
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发表时间：
2020-11
期刊：
影响因子：
3.4
通讯作者：
Zhang B
中科院分区：
文献类型：
--
作者：
Li K;Jain A;Malovannaya A;Wen B;Zhang B
在基于质谱（MS）的免疫肽组学中鉴定主要组织相容性复合体（MHC）结合肽在很大程度上依赖于为蛋白质组学数据分析而开发的数据库搜索引擎。然而，由于免疫肽组学实验不涉及特定残基的酶消化，膨胀的搜索空间导致肽鉴定中的高假阳性率和低灵敏度。为了提高肽识别的灵敏度和可靠性，我们开发了DeepRescore，这是一种后处理工具，它将来自深度学习预测的肽特征（即准确的保留时间和MS/MS谱预测）与先前使用的特征结合起来，以重新评分肽谱匹配。使用两个公共免疫肽组学数据集，我们表明，与现有方法相比，DeepRescore的重新评分提高了MHC结合肽和新抗原鉴定的灵敏度和可靠性。我们还表明，性能的提高在很大程度上是由深度学习衍生的功能驱动的。DeepRescore使用NextFlow和Docker开发，可在https://github.com/bzhanglab/DeepRescore上获得。
The identification of major histocompatibility complex (MHC)-binding peptides in mass spectrometry (MS)-based immunopeptideomics relies largely on database search engines developed for proteomics data analysis. However, because immunopeptidomics experiments do not involve enzymatic digestion at specific residues, an inflated search space leads to a high false positive rate and low sensitivity in peptide identification. In order to improve the sensitivity and reliability of peptide identification, we developed DeepRescore, a post-processing tool that combines peptide features derived from deep learning predictions, namely accurate retention time and MS/MS spectra predictions, with previously used features to rescore peptide-spectrum matches. Using two public immunopeptidomics datasets, we showed that rescoring by DeepRescore increased both the sensitivity and reliability of MHC-binding peptide and neoantigen identifications compared to existing methods. We also showed that the performance improvement was, to a large extent, driven by the deep learning-derived features. DeepRescore was developed using NextFlow and Docker and is available at https://github.com/bzhanglab/DeepRescore.
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DOI：
10.4049/jimmunol.1700893
发表时间：
2017-11-01
期刊：
Journal of immunology (Baltimore, Md. : 1950)
影响因子：
--
作者：
Jurtz V;Paul S;Andreatta M;Marcatili P;Peters B;Nielsen M
通讯作者：
Nielsen M
影响因子：
3.4
作者：
Eng, Jimmy K.;Jahan, Tahmina A.;Hoopmann, Michael R.
通讯作者：
Hoopmann, Michael R.
影响因子：
5.8
作者：
Craig, R;Beavis, RC
通讯作者：
Beavis, RC
影响因子：
14.9
作者：
Andreatta, Massimo;Alvarez, Bruno;Nielsen, Morten
通讯作者：
Nielsen, Morten
影响因子：
5.8
作者：
Li, Kai;Vaudel, Marc;Wen, Bo
通讯作者：
Wen, Bo