Generative neural networks for structure-based antibody design

用于基于结构的抗体设计的生成神经网络

• 批准号: 10799445
• 负责人: Possu Huang
• 金额: $ 13.28万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-17 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10799445
• 关键词: 3-Dimensional; Acceleration; Address; Antibodies; Area; Binding; Biotechnology; CXCR4 gene; Clinical; Computing Methodologies; Data Science; Development; Diagnostic tests; Engineering; Environment; Epitopes; Evolution; Face; Goals; Image; Libraries; Marketing; Medical; Medical Technology; Methods; Modeling; Modernization; Molecular; Neural Network Simulation; Performance; Protein Engineering; Research; Research Personnel; Role; Sampling; Signal Transduction; Snake Venoms; Specificity; Structure; Technology; Therapeutic; antibody engineering; artificial neural network; cost; design; detection platform; generative adversarial network; improved; neural network; protein structure; response; tool

PROJECT SUMMARY/ABSTRACT As a molecular detection platform, antibodies have growing importance in modern medical technology, ranging from diagnostic tests, to imaging, to therapeutics. The current market size for antibodies and their related products is estimated to be around $200 billion USD. The growing need for antibodies with customized specificity provides a rich environment for engineering efforts. Computational protein design has seen rapid progress in recent years. Many methods have been developed to address antibody engineering needs. Researchers have hoped that, through modeling and design, the cost for antibody development and improvements can be reduced and the pace for creating new targeting molecules can be expedited. In recent years, the experimental pipeline has been streamlined, but even so, extensive libraries and screen campaigns are usually required to get an initial binding signal. A major advancement would be to directly design a binder from scratch, providing a signal for potential optimization by artificial evolution. Current computational methods, however, have not taken a leading role due to a number of shortcomings with the current modeling approach. We have extensive expertise in protein design and have pioneered the use of generative neural network models for protein structures in recent years. We have observed several key advantages in neural network approaches over existing methods: namely, their ability to make inferences, interpolate, incorporate topological information, and accelerate sampling. These advantages can be developed independently or used in conjunction with existing methods, and they can significantly boost the performance of protein design. This project aims at leveraging several new advances we have developed to date to inspire new strategies in response to the challenges in antibody engineering, or AI- based protein design in general. We will develop new tools and design pipelines for expanding the specificities for multispecific antibodies and customizing epitope-specific antibodies (using snake venoms and CXCR4 as targets). This project will deliver both computational methods and constructs that can be deployed in clinical settings. The results from this research will be highly impactful.

项目摘要/摘要 抗体作为一种分子检测平台，在现代医学技术、测距技术中具有越来越重要的作用 从诊断测试，到成像，再到治疗学。目前抗体及其相关产品的市场规模 预计产品价值约2000亿美元。对具有个性化特异性的抗体的需求日益增长 为工程工作提供了丰富的环境。计算蛋白质设计在以下方面取得了快速进展 最近几年。已经开发了许多方法来满足抗体工程的需求。研究人员已经 希望通过建模和设计，可以降低抗体研发和改进的成本 而且创造新的靶向分子的步伐可以加快。近年来，试验性管道 已经简化了，但即便如此，通常也需要大量的库和屏幕宣传活动才能获得初始的 绑定信号。一个主要的进步将是直接从头开始设计活页夹，为 通过人工进化实现潜在的优化。然而，目前的计算方法并没有取得领先的进展。 由于当前的建模方法存在许多缺点。我们在蛋白质方面有广泛的专业知识 设计并在最近几年率先使用生成神经网络模型来研究蛋白质结构。 我们观察到，与现有方法相比，神经网络方法具有几个关键优势：即 能够进行推断、内插、合并拓扑信息和加速采样。这些 优势可以独立开发，也可以与现有方法结合使用，并且它们可以 显著提升了蛋白质设计的性能。该项目旨在利用我们的几个新进展 迄今已开发出新的策略，以应对抗体工程或人工智能的挑战- 一般的基于蛋白质的设计。我们将开发新的工具和设计管道，以扩大特性 用于多特异性抗体和定制表位特异性抗体(使用蛇毒和CXCR4作为 目标)。该项目将提供可应用于临床的计算方法和构造 设置。这项研究的结果将产生很大的影响。