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

本子项目是利用由NIH/NCRR资助的中心赠款提供的资源的众多研究子项目之一。子项目和研究者（PI）可能已经从另一个NIH来源获得了主要资金，因此可以在其他CRISP条目中表示。列出的机构是中心的，不一定是研究者的机构。淀粉样蛋白前体蛋白（APP）的蛋白水解过程失调与阿尔茨海默病的发病机制有关，VIA组磷脂酶A2 （iPLA2B）是中枢神经系统中主要的PLA2酶，受蛋白水解过程的调节。我们在蛋白水解酶解的LC/MS/MS分析中，通过自动识别特征肽，发现了iPLA2B的新n端变体和APP结构中以前未被识别的带有c端6-myc标签的蛋白水解位点。我们开发了一个特征发现（SD）程序，通过识别体内蛋白质水解过程中产生的特征肽来表征蛋白质异构体。该程序分析来自蛋白质混合物的蛋白水解消化的LC分析的MS/MS数据，这些蛋白质混合物可能包括生物样品中不完全溶解的成分。这减少了对净化的要求，从而最大限度地减少了样品处理过程中的人为修改。利用模型蛋白对一种新的生成理论特征肽集和计算预测质谱与观测质谱相似度的算法进行了测试和优化。该程序已被应用于鉴定具有生物学意义的蛋白质变体，包括APP裂解产物和iPLA2B，这些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.