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.

摘要。该补充方案扩大了阿尔茨海默病父母补助金的目标1和2， 开发：预测疾病风险生物标志物的算法的前瞻性基准（目标1）和新的 支持药物重新定位的算法（目标2）。这两个扩展都加强了AD的目标1和2，但也有 根据NOT-AG-20-022，立即申请COVID-19疾病研究。 AIM 1开发了EA-ML，这是一种机器学习（ML）管道，用于比较 有和没有AD的人输出结果是一个基因列表，可以通过这些基因的突变预测AD风险。 虽然父母补助金有多个成功的标准，但考虑到两个项目之间的大量筹备时间， AD发作和症状。补充目标1增加了使用COVID-19的前瞻性测试。这是可能 因为英国生物库已开始用COVID-19状态注释其50，000个公共外显子组 个人，包括谁有严重的发病率或最严重的轻微症状。生物库还将增加15万个 到2020年底，更多的外显子组。因此，我们将EA-ML应用于当前的英国生物库数据，以识别人类 区分严重病例和轻度病例的遗传生物标志物，然后测试COVID-19的EA-ML预测 毒力的前瞻性，对新添加到生物库的外显子组。作为新的基准，我们 还将比较EA-ML与一种新的“EA-小波”算法，该算法也在COVID-19上进行了前瞻性测试。EA- 小波通过在整个人类蛋白质-蛋白质相互作用网络上分解EA来对病例和对照进行分类。 结果将告诉我们EA-ML，EA-Wavelet或其组合中的哪一个在识别方面最好 关键生物标志物和AD的临床风险，同时也对COVID-19做同样的事情。 母基金的目标2通过知识将靶基因和药物联系起来，为AD开发药物重新定位 功能相互作用的地图。在这里，我们提出了一种互补的方法，将基因与药物联系起来， 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地点， 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 过去和现在的进化力量在健身景观，它考虑到目前的序列变化， 以防止针对这些表位的药物发生突变逃逸。输出将是表面 蛋白质的可接近区域，然后可用于（i）小分子的计算对接， 药物再利用、联合治疗和药物设计的先导发现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;以及（iii）CRISPR工程或肽合成， 为更有效的疫苗创造抗原7，8。这些自动映射工具是通用的，此外， SARS-CoV-2将确定一个全新的功能位点结构库，用于AD治疗， 重新利用的毒品