Kolmogorov complexity and algorithms for immunopeptidomics

免疫肽组学的柯尔莫哥洛夫复杂性和算法

• 批准号: RGPIN-2022-02942
• 负责人: Li, Ming
• 金额: $ 4.01万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750212
• 关键词: Kolmogorov; complexity; algorithms; immunopeptidomics

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.

我希望提供对信息的最终理解，并继续发明工具，以实现免疫肽组学和免疫疗法的革命。因此，这个建议分为两部分：1）Kolmogorov复杂度2）免疫肽组学算法。部分1. Kolmogorov复杂度我研究柯尔莫哥洛夫复杂性已经35年了。我们已经写了权威和获奖的书上的主题“介绍Kolmogorov复杂性及其应用”。我将继续我以前的工作，在平均情况下分析算法和信息距离。特别是，最简单的零射击学习类型可以用信息距离来表示。我还对使用Kolmogorov复杂度分析深度神经网络感兴趣。部分2.免疫肽组学算法。我将更侧重于建议的这一部分。在2017年，《自然生物技术》的社论呼吁“个性化免疫疗法风靡一时，但新抗原的发现和验证仍然是一个令人生畏的问题。“在过去的4年里，我和我的学生们一直在研究这个问题，使用深度学习来解决一系列悬而未决的问题。新生抗原是由HLA分子呈递到细胞表面的短蛋白肽。它们是药物的直接靶点。由于它们的丰度低，并且在个性化癌症免疫治疗的情况下组织样品的量有限，因此我们在处理质谱数据时必须提高灵敏度。这个问题要求我们在蛋白质组学方法的各个方面（数据库搜索、库搜索和从头测序）做出重大改进，所有解决方案共同催生了新抗原发现的潜在新产业，特别是对于个性化癌症免疫治疗，其中每个患者都需要个性化的新抗原发现。作为一个长期目标，我们将继续改进质谱数据的肽从头测序。在过去的4年里，我们在PNAS中引入了DeepNovo [22]，并在Nature Methods [21]中针对数据独立采集（DIA）数据进行了改进。然后，我们为个体患者引入了由HLA肽训练的个性化DeepNovo [21]和机器精度独立的DeepNovo [17]。沿着这条路线，我们希望继续改进DeepNovo，以处理更多的翻译后修饰、具有强化学习的更长肽和糖肽。聚糖从头测序是困难的，因为聚糖是树，而不是线性链。我们计划探索几种深度学习模型，如图神经网络。除了从头测序，我们还将研究以下问题：1）用于端到端文库/数据库搜索的高效深度学习解决方案; 2）用于免疫原性的深度学习模型; 3）改进单克隆和多克隆抗体测序方法; 4）应用我们的方法发现新抗原，优化COVID-19的HLA依赖性T细胞疫苗的设计。