InstaDeepAI/PXD062859
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为了评估InstaNovo-P模型在磷酸化肽段上的性能，我们使用了一个来自T47D乳腺癌细胞系实验的数据集，该细胞系在生长因子处理后表达或不表达Fibroblast Growth Factor Receptor 2 (FGFR2)。磷酸化是一种重要的翻译后修饰(PTM)，在细胞信号传导和疾病机制中起着核心作用。基于质谱的磷酸化蛋白质组学被广泛用于系统范围内磷酸化事件的表征。然而，传统方法在准确的磷酸化位点定位、复杂的搜索空间以及检测参考数据库之外的序列方面存在困难。InstaNovo-P是我们基于Transformer的InstaNovo模型的磷酸化特异性版本，经过大量磷酸化蛋白质组学数据集的微调，显著超越了现有方法在磷酸化肽段检测和磷酸化位点定位准确性方面的表现。我们的模型能够稳健地识别具有单磷酸化和多磷酸化位点的肽段，并有效地定位丝氨酸、苏氨酸和酪氨酸残基上的磷酸化事件。通过FGFR2信号数据的实验验证，进一步证明InstaNovo-P能够发现传统数据库搜索遗漏的许多磷酸化位点，这些位点与关键的生物过程一致，证实了模型在提供有价值的生物学见解方面的能力。InstaNovo-P通过无需先验信息即可有效识别生物学相关的磷酸化事件，为信号通路的解析提供了强大的分析工具。
To assess the model performance of `InstaNovo-P` on phosphorylated peptides, we used a dataset from an in-house experiment using T47D breast cancer cell line expressing or not Fibroblast Growth Factor Receptor 2 (FGFR2) upon growth factor treatment. Phosphorylation, a vital post-translational modification (PTM), plays a central role in cellular signaling and disease mechanisms. Mass spectrometry-based phosphoproteomics is widely used for system-wide characterization of phosphorylation events. However, traditional methods struggle with accurate phosphosite localization, complex search spaces, and detecting sequences outside the reference database. Advances in de novo peptide sequencing offer opportunities to address these limitations, but have yet to be integrated and adapted for phosphoproteomics experiments. Here, we present InstaNovo-P, a phospho-specific version of our transformer-based InstaNovo model, fine-tuned on extensive phosphoproteomics datasets. InstaNovo-P significantly surpasses existing methods in phosphopeptide detection and phosphosite localization accuracy across multiple datasets, including complex experimental scenarios. Our model robustly identifies peptides with single and multiple phosphorylation sites, effectively localizing phosphorylation events on serine, threonine, and tyrosine residues. Experimental validation with FGFR2 signaling data further demonstrated that InstaNovo-P uncovers numerous phosphosites previously missed by traditional database searches, which align with critical biological processes, confirming the model’s capacity to yield valuable biological insights. InstaNovo-P adds value to phosphoproteomics experiments by effectively identifying biologically relevant phosphorylation events without prior information, providing a powerful analytical tool for the dissection of signaling pathways.
