AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies

项目摘要 由于大规模接种疫苗，冠状病毒病2019年(新冠肺炎)大流行由 严重急性呼吸综合征冠状病毒2(SARS-CoV-2)已得到部分控制。 然而，新出现的传染性变种，如Delta，仍在助长新的感染浪潮 环游世界。疫苗逃逸(或疫苗突破)变种对 我们与新冠肺炎的战斗。了解病毒的突变和进化具有卓越的意义 重要性。通过将基因组分析、人工智能(AI)、计算生物物理学、 高级数学和实验数据，PI已经建立了一个全面的计划 预测SARS-CoV-2的准确度和可信度的实验水平 变异传染性和抗体破坏。预测未来的新兴市场仍然具有挑战性 疫苗逃逸变体，开发下一代疫苗，并设计突变- 证明抗体疗法。这些挑战在拟议的项目中得到了解决。新的 将开发数学工具和人工智能算法，以进一步改善目前的状态- 预测突变诱导的病毒感染性变化、疫苗突破和 抗体破坏。未来新兴变种的重要突变将根据以下情况进行预测 分子机制、自然选择和进化效应。新型抗突变抗体 药物的设计和测试将基于那些经过早期临床试验的抗体 审判。预测模型将被实施到一个具有在线服务器的用户友好平台中 供研究人员设计防突变的新疫苗和抗体疗法。建议数 这些方法将被应用于预测流感的新变种，并提高疫苗的疗效 季节性流感疫苗。

Project Summary Due to massive vaccination, coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been partially under control. However, emerging contagious variants such as Delta are still fueling new waves of infections around the world. Vaccine-escape (or vaccine-breakthrough) variants pose renewed threats to our battle against COVID-19. Understanding viral mutagenesis and evolution is of preeminent importance. By integrating genomic analysis, artificial intelligence (AI), computational biophysics, advanced mathematics, and experimental data, the PIs have built a comprehensive program with the experimental level of accuracy and population-level of reliability for predicting SARS-CoV-2 variant infectivity and antibody disruption. It remains challenging to forecast future emerging vaccine-escape variants, to develop the next-generation of vaccines, and to design mutation- proof antibody therapeutics. These challenges are tackled in the proposed project. New mathematical tools and AI algorithms will be developed to further improve the current state-of- the-art in predicting mutation-induced viral infectivity changes, vaccine breakthroughs, and antibody disruptions. Vital mutations in future emerging variants will be forecasted based on molecular mechanisms, natural selection, and evolutionary effects. New mutation-proof antibody drugs will be designed and tested based on those antibodies that had gone through earlier clinical trials. The predictive models will be implemented into a user-friendly platform with online servers for researchers to design mutation-proof new vaccines and antibody therapies. The proposed methods will be applied to forecast emerging variants in the flu and improve the efficacy of seasonal flu vaccines.