Grant: Research Tools and Instruments

This proposal seeks funding for essential high-powered computing equipment, aiming to harness GraphNovo's capabilities for analyzing DIA (Data Independent Acquisition) datasets. GraphNovo, our cutting-edge solution, has been proved to outperform other state-of-the-art methods in de novo peptide sequencing in DDA (Data Dependent Acquisition) datasets. Given the substantial computational demands of the GraphNovo algorithm, a GPU server with a minimum of 80G GPU memory is the basic computational requirement, which is clarified in the paper and project repository. When adapting GraphNovo to DIA datasets, the requirement is much more strict. This is primarily due to the inherently more complex characteristics of DIA data compared to DDA data. Utilizing shared computing resources, such as Compute Canada or renting computational resources, is not a practical or cost-effective option in this context. Consequently, considering the choices in the current market, upgrading our server with another 3xH100 GPUs (94GB) is an essential component of this project. The first anticipated outcome is an upgraded GraphNovo algorithm which will be proposed for both DDA and DIA methodologies, broadening its applicability in various proteomic studies. The subsequent publication of these discoveries will be shared with the scientific community. Additionally, the advanced GPU server will serve as a valuable resource for upcoming proteomic research endeavors demanding substantial computational resources, including but not limited to glycoproteomics and protein-protein interactions. Protein discovery is the cornerstone in medical advancements but it confronts a major stumbling block: the complexity of analyzing biological samples to accurately identify peptides, the building blocks of proteins. Peptide identification is the foundation of the typical tandem mass spectrometry-based (MS-based) proteomics workflow in a bottom-up or shotgun manner. GraphNovo has proven its mettle in simplifying this task, specifically in DDA datasets. The adaptation of GraphNovo to DIA datasets holds the potential to drive the evolution of peptide identification, as DIA data provides more comprehensive information. The implications are profound, promising significant breakthroughs in areas like glycoproteomics and protein-protein interactions, both vital in cancer treatment and drug discovery.