Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio, USA.
Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA.
Proteomics. 2024 Apr;24(8):e2300088. doi: 10.1002/pmic.202300088. Epub 2023 Dec 24.
Due to their oftentimes ambiguous nature, phosphopeptide positional isomers can present challenges in bottom-up mass spectrometry-based workflows as search engine scores alone are often not enough to confidently distinguish them. Additional scoring algorithms can remedy this by providing confidence metrics in addition to these search results, reducing ambiguity. Here we describe challenges to interpreting phosphoproteomics data and review several different approaches to determine sites of phosphorylation for both data-dependent and data-independent acquisition-based workflows. Finally, we discuss open questions regarding neutral losses, gas-phase rearrangement, and false localization rate estimation experienced by both types of acquisition workflows and best practices for managing ambiguity in phosphosite determination.
由于磷酸肽位置异构体的性质常常不明确，基于下拉式质谱的工作流程在鉴定时可能会面临挑战，因为仅依靠搜索引擎评分往往不足以有把握地对其进行区分。额外的评分算法可以通过在搜索结果之外提供置信度指标来解决这个问题，从而减少歧义。在这里，我们描述了解释磷酸蛋白质组学数据所面临的挑战，并回顾了几种不同的方法，用于确定基于数据依赖和数据独立采集的工作流程中的磷酸化位点。最后，我们讨论了两种采集工作流程都经历的中性损失、气相重排和假定位率估计方面的开放性问题，以及磷酸化位点确定中处理歧义的最佳实践。
