Illuminating the Druggable Genome by Knowledge Graphs

通过知识图阐明可药物基因组

• 批准号: 10348825
• 负责人: CHRISTOPHER J MUNGALL
• 金额: $ 53.66万
• 依托单位: JACKSON LABORATORY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10348825
• 关键词: Address; Algorithms; Aloral; Amino Acids; Animal Model; Antineoplastic Agents; Area; Binding; Binding Sites; Bioinformatics; Biological; Biological Models; Cancer Model; Catalogs; Categories; Clinical; Code; Computer Analysis; Computer software; Data; Data Sources; Disease; Documentation; Drug Design; Drug Targeting; Emerging Technologies; Enzymes; FDA approved; Future; Gene Targeting; Genes; Genome; Genomics; Goals; Graph; Human; Human Genome; Information Networks; Information Resources Management; Investigation; Knowledge; Libraries; Link; Machine Learning; Medical; Medicine; Molecular Biology; Ontology; Outcome; Outcomes Research; Pathology; Pattern; Pharmaceutical Preparations; Phenotype; Phosphotransferases; Pilot Projects; Process; Protein Kinase; Proteins; Public Health; Pythons; Research; Resources; Scientist; Semantics; Signal Transduction; System; The Jackson Laboratory; Training; Validation; anti-cancer; base; cheminformatics; computer science; computer studies; computing resources; dark matter; deep learning; design; disease phenotype; drug discovery; drug mechanism; drug repurposing; gene function; gene therapy; genome resource; high risk; human disease; improved; inorganic phosphate; knowledge base; knowledge graph; knowledge integration; learning algorithm; machine learning algorithm; machine learning method; mouse model; new therapeutic target; novel; novel drug class; open source; patient derived xenograft model; protein kinase inhibitor; protein kinase modulator; real world application; small molecule; tool; validation studies

PROJECT SUMMARY / ABSTRACT About 1500 of the ~20,000 protein-coding genes of the human genome can bind drug-like molecules, and yet only about 600 are currently targeted by FDA-approved drugs. Therefore, at least 930 proteins are potential drug targets that are not yet being utilized for human medicine and, given our incomplete state of knowledge about the human genome, the actual number could be much higher. There is therefore a substantial unmet need to improve our understanding of this so-called genomic dark matter in order to develop novel classes of drugs to improve treatment of disease. Comprehensive experimental investigation of these proteins in the context of hundreds of thousands of compounds and thousands of diseases would be prohibitively expensive, but computational approaches could significantly refine the list. In this project we will apply two sophisticated computational approaches to the task of predicting the most promising novel drug targets. We will integrate the knowledge bases DrugCentral and other resources with the disease and phenotype knowledge base of the Monarch Initiative into a semantically harmonized knowledge graph (KG). This will result in a KG with comprehensive coverage of diseases, genes, gene functions, phenotypic abnormalities, drugs, drug mechanisms, and drug targets. Machine learning (ML) identifies patterns from training sets and applies the patterns to predict entities and relations in new data. ML using KGs has become a hot new research area in computer science, but remains difficult to use for real-world applications, owing to the lack of adequate software packages. We will therefore implement state-of-the art learning algorithms based on deep learning on KGs by extending and adapting selected algorithms to the task of drug and drug target discovery. We will develop an easy-to-use software library and demonstrate its use by means of notebooks that will be designed to serve as starting points for future computational research by other scientists, since they will contain the analysis workflow along with documentation about each step. The human genome codes more than 500 protein kinases, which are enzymes that add a phosphate group to specific amino acid residues and thereby transmit a biological signal. There are currently 35 FDA approved protein kinase modulators acting on 38 protein kinases, which are thus one of the most important groups of druggable proteins encoded by our genome. We will perform a detailed computational study of this group and experimentally validate our top, novel candidate using a patient-derived xenograft model system.

项目概要/摘要 人类基因组约 20,000 个蛋白质编码基因中，约有 1500 个可以结合药物样分子，但 目前 FDA 批准的药物仅针对约 600 种。因此，至少有930种蛋白质是潜在的药物 尚未用于人类医学的目标，并且鉴于我们对 根据人类基因组，实际数字可能要高得多。因此，存在大量未满足的需求 提高我们对这种所谓的基因组暗物质的理解，以便开发新型药物 改善疾病的治疗。在以下背景下对这些蛋白质进行全面的实验研究 数十万种化合物和数千种疾病的成本将高得令人望而却步，但是 计算方法可以显着完善列表。在这个项目中，我们将应用两个复杂的 预测最有希望的新型药物靶点任务的计算方法。我们将整合 知识库 DrugCentral 和其他资源以及疾病和表型知识库 Monarch Initiative 转化为语义协调的知识图谱 (KG)。这将导致 KG 全面覆盖疾病、基因、基因功能、表型异常、药物、药物 机制和药物靶点。机器学习 (ML) 从训练集中识别模式并应用 预测新数据中的实体和关系的模式。使用知识图谱的机器学习已成为一个热门的新研究领域 计算机科学，但由于缺乏足够的软件，仍然难以用于现实世界的应用 包。因此，我们将通过以下方式在 KG 上实现基于深度学习的最先进的学习算法： 扩展和调整选定的算法以完成药物和药物靶点发现的任务。我们将开发一个 易于使用的软件库，并通过笔记本电脑演示其使用，这些笔记本电脑将被设计为 其他科学家未来计算研究的起点，因为它们将包含分析工作流程 以及有关每个步骤的文档。人类基因组编码超过 500 种蛋白激酶， 是在特定氨基酸残基上添加磷酸基团从而传递生物信号的酶。 目前有 35 种 FDA 批准的蛋白激酶调节剂作用于 38 种蛋白激酶，因此 由我们的基因组编码的最重要的可药物蛋白组之一。我们将进行详细的 该组的计算研究，并使用源自患者的方法通过实验验证我们的顶级新颖候选者 异种移植模型系统。