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

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Name pinyin
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070302 分析化学
Subject category of dissertation
07 理学
Tan Soon Heng
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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.

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References List

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Liao B. Re-analysis of Large-Scale AP-MSMS Data for Discovery and Characterization of Microproteins[D]. 深圳. 南方科技大学,2023.
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