High-Coverage Stereoproteome Mapping Uncovers Pervasive Protein Stereoisomerization Associated with Neurodegeneration

Protein asymmetry, while crucial for life, can arise from subtle stereoisomerization. However, a comprehensive understanding of the breadth and specificity of the whole stereoproteome (STEP) has been hindered by insufficient stereoisomeric resolution. Here, we introduce an untargeted, de novo STEP discovery protocol for comprehensive STEP profiling and relative quantification. This method employs multidimensional isomeric separation, advanced algorithms, and stereoisomer-specific retention time shifts. STEP mapping identifies 182 neurodegenerative disease-linked, putative stereoisomeric proteins with stereoisomeric ratios of up to 70% in a cell model. A machine learning-derived scoring model achieves high confidence in endogenous stereoisomeric data assessment, achieving an average score of over 0.97 and a modeling accuracy exceeding 98%, with a false discovery rate of less than 5%. Validation experiments using synthetic STEP peptide standards and additional enzymatic localization of D-sites with aminopeptidase M confirm the putative STEP list and their relative abundances. This work advances protein stereoisomer analysis to a proteome scale, connecting protein molecular asymmetry with potential cellular functions and disease mechanisms.

蛋白质不对称性虽对生命活动至关重要，却可源于细微的立体异构化过程。然而，受限于立体异构分辨率不足，学界对全立体蛋白质组（stereoproteome, STEP）的覆盖范围与特异性的全面认知仍存在阻滞。本研究提出一种非靶向、从头式的STEP发现方案，用于全面的STEP分析与相对定量。该方法整合了多维异构分离技术、先进算法以及立体异构体特异性保留时间偏移特性。通过STEP图谱分析，我们在细胞模型中鉴定出182种与神经退行性疾病相关的潜在立体异构蛋白质，其立体异构比例最高可达70%。基于机器学习构建的评分模型可对内源性立体异构数据实现高置信度评估，平均得分超0.97，建模准确率超过98%，假阳性率低于5%。借助合成STEP肽标准品以及利用氨肽酶M对D位点进行额外酶促定位的验证实验，证实了所推定的STEP列表及其相对丰度。本研究将蛋白质立体异构分析推进至蛋白质组规模，将蛋白质分子不对称性与潜在细胞功能及疾病机制建立了关联。