CDS&E: Simulation- and Data-driven Peptide Antibody Design Targeting RBD and non-RBD Epitopes of SARS-CoV-2 Spike Protein

药物与蛋白质相互作用，破坏细菌和病毒感染。有效的药物通常是被发现的，而不是被设计出来的。抗体是由免疫系统产生的蛋白质复合物，用于结合和消灭病毒。肽是被设计用于模拟抗体的蛋白质结合活性的短串氨基酸。蛋白质-蛋白质和蛋白质-肽相互作用的许多方面还不清楚。该项目将应用人工智能方法来理解这些交互。SARS-CoV-2刺突蛋白将作为研究的模型系统。由此产生的治疗肽设计模型将在各种软件平台上提供给研究团体。该项目还将支持向K-12学生宣传SARS-CoV-2病毒和病毒感染。总体目标是开发一个混合机器学习-模拟（MLSim）平台，使我们能够更好地了解肽类药物和病毒蛋白之间的分子相互作用。模型病毒蛋白系统将是SARS-CoV-2受体结合域（RBD）和非RBD的刺突蛋白。将实施针对蛋白质-肽相互作用的现有数据模型的迁移学习技术。在线学习技术将允许使用新的可用数据及时更新预测模型。多尺度模拟组件通过提供高保真输入数据和交叉验证预测来帮助机器学习部分。这些努力应该会导致对病毒蛋白-抗体相互作用的分子水平的洞察。预计该项目将产生两个关键成果。首先，一个支持模拟和数据的平台，集成了高通量、可定制的机器学习管道，用于快速筛选和过滤候选肽，并具有高保真全原子显式溶剂分子动力学模拟和自由能计算。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Drugs interact with proteins to disrupt bacterial and viral infections. Effective drugs are usually discovered rather than designed. Antibodies are protein complexes generated by the immune system to bind to and inactivate viruses. Peptides are short strings of amino acids that are being designed to mimic the protein binding activity of antibodies. Many aspects of protein-protein and protein-peptide interactions are not clearly understood. This project will apply an artificial intelligence approach to understand those interactions. The SARS-CoV-2 spike protein will be the model system for study. The resulting model for therapeutic peptide design will be provided to the research community on a variety of software platforms. The project will also support outreach to K-12 students regarding the SARS-CoV-2 virus and viral infections. The overall objective is to develop a hybrid machine learning-simulation (MLSim) platform that allows us to better understand the molecular interaction between peptide drugs and viral proteins. The model viral protein system will be the SARS-CoV-2 spike proteins at both the receptor-binding domain (RBD) and the non-RBD. Transfer learning techniques for existing data models for protein-peptide interactions will be implemented. Online learning techniques will allow for the timely update of the predictive models with newly available data. The multiscale simulation component aids the machine learning part by supplying high-fidelity input data and cross-validating the predictions These efforts should result in molecular-level insight into viral protein-antibody interactions. There are two key outcomes anticipated from this project. First, a simulation- and data-enabled platform that integrates a high-throughput, customizable machine learning pipeline for fast screening and filtering peptide candidates, with high-fidelity all-atom explicit-solvent molecular dynamics simulation and free energy calculations. The second is fundamental insight into viral protein-peptide interactions and how those influence the design of neutralizing peptides.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.