Exploring the Dark Matter of Proteomics

Proteomics, particularly mass spectrometry (MS)-based proteomics, has become an essential tool in understanding biological complexity and function at the molecular level. However, a significant fraction of spectral data generated in these studies, often referred to as the "Dark Matter of Proteomics'', remains unexplored and unidentified, concealing potentially vital biological insights. This dissertation addresses the challenge of uncovering this dark matter through the innovative use of computational techniques to enhance peptide identification and quantification in Kalanchoë fedtschenkoi, a model organism for Crassulacean Acid Metabolism (CAM).

The research employs a quantification-centered approach to MS data analysis, leveraging both MS1 and MS2 spectral data to identify and quantify peptides that traditional methods fail to detect. This study elucidates the methodological advancements necessary for decoding complex proteomic data, focusing on high-quality unidentified spectra that could reveal new aspects of plant biology and stress responses.

Our findings significantly expand the identified proteome of K. fedtschenkoi, demonstrating that many previously unidentified spectra are indeed derived from peptides with modifications or from unexpected biological sources. The dissertation presents a comprehensive bioinformatics framework that combines existing software tools with novel algorithms to improve the identification rate of MS spectra. Moreover, it highlights the critical role of these unidentified spectra in understanding biological processes, suggesting that they contain modifications and variants crucial for plant adaptation to environmental stresses.

This work not only advances our understanding of the proteome of CAM plants but also contributes methodological improvements that can be applied to other proteomics studies, aiming to reduce the volume of unidentified spectral data. The insights gained from this research could lead to new biological discoveries and enhance our understanding of proteomic complexity in other biological systems.