N-TERMINAL PROCESSING OF IPLA2B AND OF POT CLEAVAGE SITES OF APP CONSTRUCTS

该子项目是利用NIH/NCRR资助的中心赠款提供的资源的许多研究子项目之一。子项目和研究者（PI）可能从另一个NIH来源获得了主要资金，因此可以在其他CRISP条目中表示。所列机构为中心，不一定是研究者所在机构。淀粉样蛋白前体蛋白（APP）的蛋白水解加工的失调有助于阿尔茨海默病的发病机制，并且VIA族磷脂酶A2（iPLA 2B）是中枢神经系统中的主要PLA 2酶，并且受到调节性蛋白水解加工。我们已经通过在蛋白水解酶的LC/MS/MS分析中自动鉴定特征肽，鉴定了新的iPLA 2B的N-末端变体和先前未识别的具有C-末端6-myc标签的APP构建体中的蛋白水解位点。我们已经开发了一个签名发现（SD）程序，通过识别在体内蛋白水解过程中产生的签名肽来表征蛋白质亚型。该程序分析来自蛋白质混合物的蛋白水解酶的LC分析的MS/MS数据，所述蛋白质混合物可以包括生物样品中不完全分离的组分。这降低了对纯化的要求，从而最大限度地减少了样品处理过程中的人为修改。一种新的算法，以产生理论的签名肽集，并计算预测和观察到的质谱之间的相似性得分进行了测试和优化与模型蛋白质。该程序已被应用于鉴定生物学感兴趣的蛋白质的变体，包括APP切割产物和iPLA 2B，并且这样的应用证明了该方法的实用性。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Dysregulation of proteolytic processing of the amyloid precursor protein (APP) contributes to the pathogenesis of Alzheimer's Disease, and the Group VIA phospholipase A2 (iPLA2B) is the dominant PLA2 enzyme in the central nervous system and is subject to regulatory proteolytic processing. We have identified novel N-terminal variants of iPLA2B and previously unrecognized proteolysis sites in APP constructs with a C-terminal 6-myc tag by automated identification of signature peptides in LC/MS/MS analyses of proteolytic digests. We have developed a Signature-Discovery (SD) program to characterize protein isoforms by identifying signature peptides that arise from proteolytic processing in vivo. This program analyzes MS/MS data from LC analyses of proteolytic digests of protein mixtures that can include incompletely resolved components in biological samples. This reduces requirements for purification and thereby minimizes artifactual modifications during sample processing. A new algorithm to generate the theoretical signature peptide set and to calculate similarity scores between predicted and observed mass spectra has been tested and optimized with model proteins. The program has been applied to the identification of variants of proteins of biological interest, including APP cleavage products and iPLA2B, and such applications demonstrate the utility of this approach.