Synergistic integration of topology and machine learning for the predictions of protein-ligand binding affinities and mutation impacts

拓扑和机器学习的协同集成，用于预测蛋白质-配体结合亲和力和突变影响

• 批准号: 10189006
• 负责人: Guowei Wei
• 金额: $ 11.64万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10189006
• 关键词: 2019-nCoV; Address; Adopted; Affinity; Antibodies; Antibody Affinity; Artificial Intelligence; Attention; Award; Binding; Binding Proteins; COVID-19; COVID-19 vaccine; Cells; Common Cold; Conflict (Psychology); Coronavirus; Crystallization; Development; Diagnostic; Diagnostic Reagent; Diagnostic tests; Drug resistance; Drug usage; Evolution; Failure; Future; Genetic Transcription; Genome; Goals; Grant; Heterogeneity; Induced Mutation; Influenza A virus; Machine Learning; Manuscripts; Mathematics; Medicine; Methods; Mission; Modeling; Modification; Molecular; Mutate; Mutation; National Institute of General Medical Sciences; Nucleotides; Peptidyl-Dipeptidase A; Pharmaceutical Preparations; Phase; Preventive measure; Preventive vaccine; Process; Proteins; RNA Viruses; Recurrence; Reporting; Research; Rhinovirus; SARS coronavirus; Severe Acute Respiratory Syndrome; Site; Structure; Therapeutic; Therapeutic antibodies; Time; Vaccines; Viral; Virulence; Virus; Work; antigen antibody binding; autoencoder; base; combat; design; experience; flu; improved; inhibitor/antagonist; news; pandemic disease; protein folding; receptor; receptor binding; reconstruction; resistance mutation; success; vaccine development

Project Summary The success of the ongoing battle with coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) depends crucially on the availability of effective diagnostics, vaccines, antibody therapeutics, and small-molecular drugs. Although SARS-CoV-2 mutates slower than the viruses that cause the flu and the common cold, it has had more than 8300 observed single mutations on its genome of 29,900 nucleotides by June 1, 2020. We show that these mutations might have devastating effects on COVID-19 diagnostics, vaccines, antibody therapeutics, and small-molecular drugs (J. Chem. Inf. Model. In press). We will develop new artificial intelligence (AI) to forecast SARS-CoV-2 future mutations. Leveraging on state-of-art methods developed under the present R01 award, we will design mutation- resistant vaccines, antibody therapeutics, and small-molecular drugs. The CPUs and GPUs requested in this supplement will be essential for my lab to continue the research of the present R01 award and to apply the methods developed in this award to attack fundamental problems in combating COVID-19.

项目摘要 正在进行的与严重冠状病毒病2019年(新冠肺炎)的战斗取得成功 急性呼吸综合征冠状病毒2(SARS-CoV-2)在很大程度上取决于 有效的诊断、疫苗、抗体疗法和小分子药物。虽然 SARS-CoV-2病毒的变异速度比导致流感和普通感冒的病毒慢，它已经 截至2020年6月1日，超过8300人观察到其29,900个核苷酸的基因组单一突变。 我们表明，这些突变可能会对新冠肺炎诊断产生毁灭性的影响， 疫苗、抗体疗法和小分子药物(J.信息模特。在新闻发布会上)。我们 将开发新的人工智能(AI)来预测SARS-CoV-2未来的变异。利用 在目前的R01奖项下开发的最先进的方法，我们将设计突变- 耐药疫苗、抗体疗法和小分子药物。CPU和GPU 对于我的实验室继续目前的研究来说，这是必不可少的 R01奖，并应用在该奖项中开发的方法来解决 对抗新冠肺炎。