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。本补充将阿尔茨海默病（AD）父母补助金的目标1和目标2延长了