Re-analysis of Large-Scale AP-MSMS Data for Discovery and Characterization of Microproteins

Translatomics, represented by ribosome profiling sequencing, has revealed the potential for protein translation in non-coding regions. These newly discovered non-canonical proteins are usually shorter than 100 amino acids in length and hence are named as microproteins. Despite their short sequences and simple structures, which distinguish themselves from canonical proteins, microproteins play critical roles in cell growth, development and differentiation, and thus have gradually gained much attention. Mass spectrometry (MS)-based proteomics provides a powerful approach for the discovery, identification and functional study of microproteins. With proteomics, microprotein interactions can be effectively studied to elucidate their potential functions and characterizations. However, there is still a lack of large-scale analysis of microprotein interactions via proteomic methods for functional characterizations. The relatively sparse data on microprotein interactions have hindered the exploration of microprotein functions. Therefore this thesis focuses on the re-analysis of existing MS data on large-scale protein interactome, offering a solution to the problem of mining and characterizing underlying microproteins and their functional information.

Through re-analysis of BioPlex datasets, the largest human protein affinity purification mass spectrometry (AP-MS) database to date, a workflow for the study of microprotein interactions was established. A total of 2,136 microproteins active in 25,723 interactions were identified. On the basis of the homology between microproteins and their interacting bait proteins, microprotein interactions were grouped into three types, namely A, B and C. Various differences in terms of transcription location and other characteristics were observed between three types of interactions. Bioinformatics analysis further revealed the potential functions of microproteins such as ribosome assembly and signal peptides in terms of both their interacting proteins and their own structures.

On the basis of the microprotein interactome above, in this thesis, TPCA (thermal proximity co-aggregation) were first applied to the research on microprotein interactions, and revealed that microproteins had a consistent trend of temperature-dependent thermal melting curves similar to those of canonical proteins. In terms of the prediction of microprotein interactions, TPCA showed excellent predictive performance for type A and type B interactions. And microprotein interactions in type A and B showed high consistency across the four cell lines. Unlike the microprotein interaction network constructed by BioPlex, TPCA is able to build up a weighted interaction network based on the thermal melting curve between proteins. Furthermore, in protein complexes in which interacting proteins of microproteins participate, TPCA can speculate that microproteins may also be involved and play a functional role in this protein complex.

In conclusion, by re-processing of existing proteomics interactome MS data, this project provides a rich resource of microprotein interactome, expands the knowledge of microprotein characterizations and functions, and suggests a feasible analysis strategy for the study of microprotein interactions and their functional characterizations.

以核糖体印迹测序为代表的翻译组学揭示了非编码区编译蛋白的潜力。这类新发现的非典型蛋白的长度通常短于100个氨基酸，因而它们被称为微蛋白。尽管微蛋白有着区别于典型蛋白的简短序列和简单结构，却在细胞的生长发育和分化等方面发挥重要作用。基于质谱的蛋白质组学为微蛋白的发现，鉴定和功能研究提供了强有力的依据。借助蛋白质组学，可以有效研究微蛋白的相互作用，进而阐明其潜在的功能和特征。然而，目前仍缺乏在蛋白质组学层面上大规模分析微蛋白相互作用，解析其功能特征的研究。相对稀少的微蛋白相互作用数据一定程度上阻滞了对微蛋白功能的探究。因此本论文专注于对已有的大规模蛋白相互作用的质谱数据重新分析，为挖掘和表征潜藏的微蛋白及其功能信息提供了一种解决思路。

通过重新分析迄今为止最大规模的人类蛋白亲和纯化质谱（AP-MS）数据库BioPlex，本论文建立了一套适用于微蛋白相互作用研究和分析的工作流程。本论文共鉴定到了2,136个微蛋白，其参与了25,723个相互作用。基于微蛋白与其相互作用的诱饵蛋白之间的同源性，本论文将微蛋白相互作用分成三类，即类型A，B和C，并观察到它们在转录本定位等方面的差异。生物信息学分析从微蛋白的相互作用蛋白与其自身结构两个维度进一步揭示了微蛋白潜在的功能，如核糖体组装和信号肽等。

基于上述微蛋白相互作用组数据，本论文首次将热邻近共聚集（TPCA）技术应用于微蛋白的研究，发现微蛋白与典型蛋白相似，具有随温度变化的趋势一致的热熔解曲线。研究表明，TPCA对类型A和B的相互作用展现出了良好的预测性能，其相互作用在四种细胞系中具有高度一致性。更进一步，TPCA能够依据蛋白间热熔解曲线的距离形成加权的相互作用网络。此外，本论文依据与微蛋白相互作用的典型蛋白参与的蛋白复合物，基于TPCA可以推测微蛋白或许也参与到这一蛋白复合物中并发挥功能。

总之，基于对现有蛋白质组学质谱数据的重新分析，本论文提供了丰富的微蛋白相互作用组资源，拓展了对微蛋白特点和功能的认知，为微蛋白相互作用及其功能特点的研究提出了可行的分析策略。

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