Skiadopoulou Dafni, Vašíček Jakub, Kuznetsova Ksenia, Bouyssié David, Käll Lukas, Vaudel Marc
Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, NO-5020 Bergen, Norway.
Computational Biology Unit, Department of Informatics, University of Bergen, NO-5020 Bergen, Norway.
J Proteome Res. 2023 Oct 6;22(10):3190-3199. doi: 10.1021/acs.jproteome.3c00243. Epub 2023 Sep 1.
Precision medicine focuses on adapting care to the individual profile of patients, for example, accounting for their unique genetic makeup. Being able to account for the effect of genetic variation on the proteome holds great promise toward this goal. However, identifying the protein products of genetic variation using mass spectrometry has proven very challenging. Here we show that the identification of variant peptides can be improved by the integration of retention time and fragmentation predictors into a unified proteogenomic pipeline. By combining these intrinsic peptide characteristics using the search-engine post-processor Percolator, we demonstrate improved discrimination power between correct and incorrect peptide-spectrum matches. Our results demonstrate that the drop in performance that is induced when expanding a protein sequence database can be compensated, hence enabling efficient identification of genetic variation products in proteomics data. We anticipate that this enhancement of proteogenomic pipelines can provide a more refined picture of the unique proteome of patients and thereby contribute to improving patient care.
精准医学致力于根据患者的个体特征调整治疗方案，例如，考虑他们独特的基因组成。能够考虑基因变异对蛋白质组的影响对实现这一目标具有巨大潜力。然而，利用质谱法鉴定基因变异的蛋白质产物已被证明极具挑战性。在这里，我们表明，通过将保留时间和碎片预测器整合到一个统一的蛋白质基因组流程中，可以提高变异肽段的鉴定效率。通过使用搜索引擎后处理器Percolator结合这些肽段的内在特征，我们证明了在正确和错误的肽段-谱图匹配之间具有更高的区分能力。我们的结果表明，在扩展蛋白质序列数据库时所导致的性能下降可以得到补偿，从而能够在蛋白质组学数据中高效鉴定基因变异产物。我们预计，这种蛋白质基因组流程的改进能够提供患者独特蛋白质组的更精确图景，从而有助于改善患者护理。
