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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要与2019年冠状病毒病（COVID-19）的持续斗争取得了成功急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 的有效性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 的突变速度比引起流感和普通感冒的病毒慢，截至 2020 年 6 月 1 日，其 29,900 个核苷酸的基因组上观察到了超过 8300 个单突变。我们证明这些突变可能对 COVID-19 诊断产生毁灭性影响，疫苗、抗体疗法和小分子药物（J. Chem. Inf. Model. 出版中）。我们将开发新的人工智能（AI）来预测 SARS-CoV-2 未来的突变。杠杆作用根据目前 R01 奖开发的最先进方法，我们将设计突变- 耐药疫苗、抗体疗法和小分子药物。 CPU 和 GPU 本补充中的要求对于我的实验室继续当前的研究至关重要 R01 奖并应用该奖项中开发的方法来解决抗击新冠肺炎 (COVID-19)。