Kolmogorov complexity and algorithms for immunopeptidomics

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I wish to provide an ultimate understanding of what is information, and continue to invent tools to enable the revolution of immunopeptidomics and immunotherapy. Thus, this proposal is in two parts: 1) Kolmogorov complexity 2) Algorithms for immunopeptidomics. Part 1. Kolmogorov complexity. I have been doing research in Kolmogorov complexity for 35 years. We have written the authoritative and award-winning book on the subject "An introduction to Kolmogorov complexity and its applications". I will continue my previous work in average-case analysis of algorithms and information distance. In particular, the simplest type of zero-shot learning maybe formulated by information distance. I am also interested in analyzing deep neural networks using Kolmogorov complexity. Part 2. Algorithms for Immunopeptidomics. I will focus more on this part of the proposal. In 2017, Nature Biotechnology editorial appeals "Personalized immunotherapy is all the rage but the neoantigen discovery and validation remains a daunting problem." Over the past 4 years, I and my students have intensely worked on this problem, using deep learning to solve a series of open questions. Neoantigens are short protein peptides presented by HLA molecules on the surface of cells. They are direct drug targets. Due to their low abundance, and limited amount tissue samples in case of personalized cancer immunotherapy, it is imperative that we improve sensitivity when processing mass spectrometry data. The problem requires us to make significant improvements in all fronts of proteomics methods (database search, library search, and de novo sequencing) and all solutions together gives rise to a potential new industry of neoantigen discovery especially for personalized cancer immunotherapy, where each patient requires personalized neoantigen discovery. As a long-term goal, we will continue to improve peptide de novo sequencing from mass spectrometry data. Over the past 4 years, we have introduced DeepNovo in PNAS [22], and improved it for data-independent acquisition (DIA) data in Nature Methods [21]. We then introduced personalized DeepNovo trained by HLA peptides for individual patients [21] and machine precision independent DeepNovo [17]. Along this line, we wish to continue to improve DeepNovo to handle more post=translation modifications, longer peptides with reinforcement learning, and glyco peptides. Glycan de novo sequencing is hard, as glycans are trees, not linear chains. We plan to explore several deep learning models, such as graph neural networks. Beyond de novo sequencing, we will work on the following problems: 1) Efficient deep learning solutions for end-to-end library/database search; 2) Deep learning models for immunogenecity; 3) Improve monoclonal and polyclonal antibody sequencing methods; 4) Apply our methods to discover neoantigens optimize the design of an HLA dependent T cell vaccine for COVID-19.