A knowledge map to find Alzheimer's disease drugs

一张知识图谱寻找阿尔茨海默病药物

• 批准号: 10198233
• 负责人: OLIVIER LICHTARGE
• 金额: $ 38.66万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10198233
• 关键词: 2019-nCoV; Address; Algorithms; Alzheimer' s Disease; Alzheimer' s disease risk; Alzheimer' s disease therapy; Antigens; Benchmarking; Binding; Biological Markers; COVID-19; COVID-19 pandemic; Calculi; Catalysis; Clinical; Clustered Regularly Interspaced Short Palindromic Repeats; Code; Combined Modality Therapy; Data; Disease; Disease Surveillance; Docking; Drug Targeting; Emergency Situation; Engineering; Epitopes; Evolution; Funding; Genes; Genetic Code; Genetic Markers; Graph; Human; Human Genetics; Immunotherapy; Individual; Infection; Knowledge; Lead; Libraries; Life; Link; Machine Learning; Maps; Measures; Methods; Mission; Morbidity - disease rate; Mutation; Onset of illness; Output; Patient risk; Peptide Synthesis; Peptides; Pharmaceutical Preparations; Protein Region; Proteins; Proteome; Research; Risk; Risk Assessment; Site; Solvents; Speed; Structure; Surface; Symptoms; Testing; Time; Training; Update; Variant; Viral Genes; Virulence; base; biobank; case control; clinical risk; disorder risk; exome; fitness; genome-wide; improved; knowledge graph; mimetics; mortality; novel; novel strategies; parent grant; peptidomimetics; prediction algorithm; predictive marker; prevent; prospective; prospective test; protein function; protein protein interaction; protein structure; prototype; small molecule; success; support tools; theories; tool; vaccine development; web site

ABSTRACT. This Supplement extends Aims 1 and 2 of the parent grant on Alzheimer’s Disease (AD) by developing: prospective benchmarks for algorithms that predict biomarkers of disease risk (Aim 1) and new algorithms to support drug repositioning (Aim 2). Both extensions strengthen Aims 1 and 2 for AD but also have immediate applications for research on COVID-19 disease in keeping with NOT-AG-20-022. AIM 1 of the parent grant develops EA-ML, a Machine Learning (ML) pipeline to compare coding mutations in individuals with and without AD. The output is a list of genes with which to predict AD risk from their mutations. While the parent grant has multiple criteria for success, none are prospective given the vast lead-time between AD onset and symptoms. Supplemental Aim 1 adds prospective testing, using COVID-19. This is possible because the UK Biobank has begun to annotate its 50,000 public exomes with the COVID-19 status of individuals, including who had severe morbidity or mild symptoms at worst. The biobank will also add 150,000 more exomes by end 2020. Accordingly, we will apply EA-ML to the current UK biobank data to identify human genetic biomarkers that distinguish severe from mild cases and then test EA-ML predictions of COVID-19 virulence prospectively, on the exomes that are newly added to the biobank. As a further new benchmark, we will also compare EA-ML to a novel “EA-Wavelet” algorithm, also tested prospectively on COVID-19. EA- Wavelet sorts cases from controls by factoring EA over the entire network of human protein-protein interactions. The results will tell us which of EA-ML, EA-Wavelet, or combination thereof is the best at identifying critical biomarkers and clinical risk of AD, while also doing the same for COVID-19. Aim 2 of the parent grant develops drug repositioning for AD by linking target genes and drugs via knowledge maps of functional interactions. Here, we propose a complementary approach that connect genes to drugs via structural maps of binding epitopes. For this we will comprehensively map evolutionarily important sites in the structural proteome of genes that are associated with AD. The approach exploits EA theory to measure past and present evolutionary forces in fitness landscapes, and it takes into account current sequence variations to guard against any possible mutational escape from drugs that target these epitopes. The output will be surface accessible regions of proteins that can then be used for (i) computational docking of small molecules towards drug repurposing, combination therapy, and lead discovery for drug design3-5; (ii) engineering mimetic peptides or other molecules that can inhibit normal interactions6; and (iii) CRISPR engineering or peptide synthesis that create antigens for more effective vaccines7, 8. These automated mapping tools are general, and besides in SARS-CoV-2, will identify an entire new structural library of functional sites to target for AD therapy with repurposed drugs.

抽象的。该补充剂扩展了父母对阿尔茨海默氏病（AD）的目标1和2的目标。 发展：预测疾病风险生物标志物（AIM 1）和新的算法的前瞻性基准 支持药物重新定位的算法（AIM 2）。两种扩展都加强了广告的目标1和2 与NOT-AG-20-022有关COVID-19疾病的研究的直接应用。 AIM 1的授予授予开发项目EA-ML，一种机器学习（ML）管道，以比较中的编码突变 有和没有广告的人。输出是可以从其突变中预测AD风险的基因列表。 虽然父母赠款有多个成功标准，但鉴于在 广告发作和符号。补充目标1使用Covid-19添加了前瞻性测试。这是可能的 因为英国生物库已开始注释其50,000个公共外来，并以19的状态为 个人，包括严重发病率或轻度症状的个人。生物库还将增加150,000 到2020年底，更多的例子。根据，我们将向当前的英国生物银行数据应用EA-ML以识别人类 遗传生物标志物将严重的严重病例与轻度病例区分开，然后测试Covid-19的EA-ML预测 病毒可能是在新添加到生物库中的外体上。作为进一步的新基准，我们 还将将EA-ML与一种新颖的“ EA-Wavelet”算法进行比较，该算法也对Covid-19进行了前瞻性测试。伊 通过将EA考虑到整个人类蛋白质 - 蛋白质相互作用的网络中，小波从对照组中分类。 结果将告诉我们哪种EA-ML，EA波浪或其组合是最好的识别 关键的生物标志物和AD的临床风险，同时也为Covid-19做同样的事情。 父母赠款开发的目标2通过知识将目标基因和药物联系起来为AD重新定位 功能相互作用的地图。在这里，我们提出了一种将基因与药物联系起来的完整方法 通过结合表位的结构图。为此，我们将全面绘制进化上重要的站点 在与AD相关的基因的结构蛋白质组中。该方法利用EA理论来衡量 在健身景观中的过去和现在进化力，并考虑到当前序列变化 防止任何可能的突变逃脱从靶向这些表位的药物中。输出将是表面 然后可用于（i）小分子的计算对接的蛋白质可访问区域 药物重新利用，联合疗法和药物设计的铅发现3-5； （ii）工程模拟肽 或其他可以抑制正常相互作用的分子6； （iii）CRISPR工程或肽合成 为更有效的疫苗创建抗原7，8。这些自动映射工具是一般的，此外 SARS-COV-2将确定功能部位的全新结构库，以针对广告疗法 重新利用的药物。