A knowledge map to find Alzheimer's disease drugs

抽象的。本补充资料将阿尔茨海默病(AD)父母补助金的AIMS 1和AIMS 2延长了 正在开发：预测疾病风险生物标记物的算法的预期基准(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 立即申请新冠肺炎病研究符合NOT-AG-20-022。 父母拨款的目标1开发了EA-ML，这是一种机器学习(ML)管道，用于比较 患有和不患有AD的个体。结果是一份基因清单，可以用来预测其突变带来的AD风险。 虽然家长拨款有多个成功标准，但考虑到两个标准之间的巨大提前期，没有一个标准是预期的 AD的发病和症状。补充的AIM 1增加了前瞻性测试，使用新冠肺炎。这是可能的 因为英国生物库已经开始用新冠肺炎的状态来注释它的50,000个公开exome 个人，包括那些有严重发病率或最严重的轻微症状的人。生物库还将增加15万 到2020年底，会有更多的外星人。因此，我们将把EA-ML应用于当前的英国生物库数据，以识别人类 区分重症和轻型患者然后检验EA-ML预测新冠肺炎的遗传生物标记物 对新加入生物库的外显子有预期的毒力。作为进一步的新基准，我们 还将把EA-ML与一种新的“EA-小波”算法进行比较，该算法也将在新冠肺炎上进行预期测试。EA- 小波通过将EA分解到整个人类蛋白质-蛋白质相互作用网络来对病例进行分类。 结果将告诉我们EA-ML、EA-Wavelet或它们的组合中哪一个在识别方面是最好的 关键生物标记物和AD的临床风险，同时也对新冠肺炎进行了同样的研究。 父母资助的目标2通过知识将目标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重要地点的地图 在与AD相关的基因的结构蛋白质组中。该方法利用EA理论来度量 适应度格局中过去和现在的进化力量，并考虑了当前序列的变化 以防止针对这些表位的药物的任何可能的突变逃逸。输出将是表面 蛋白质的可及区域，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；(2)工程模拟多肽 或其他可以抑制正常相互作用的分子6；以及(Iii)CRISPR工程或多肽合成 为更有效的疫苗创建抗原7、8。这些自动化绘图工具是通用的，而且在 SARS-CoV-2，将确定一个全新的功能部位结构库，用于AD治疗 改变用途的毒品。

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.