Abstract
The adaptive immune system is a crucial component of the immune response, providing specific defense against a wide range of pathogens and contributing to the development of immunological memory. Immunopeptidomics is a rapidly evolving field that uses mass spectrometry-based approaches to identify and quantify immunopeptides, which play a vital role in the recognition and elimination of infected or malignant cells by T cells. However, the annotation rate of immunopeptides from mass spectrometry data is currently severely limited, resulting in a significant loss of biological information. To overcome this challenge, we will develop specialized bioinformatics tools for analyzing mass spectrometry immunopeptidomics data. Specifically, we will develop an efficient and sensitive open modification search engine to identify immunopeptides that have undergone post-translational modifications. Furthermore, we will develop a deep learning-based de novo peptide sequencing approach optimized for the analysis of immunopeptidomics data. The tools developed in this project have the potential to significantly expand the amount of biological information that can be obtained from immunopeptidomics experiments, leading to transformational breakthroughs in the field.
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