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 作为一个分子检测平台，抗体在现代医疗技术中的重要性越来越重要 从诊断测试，成像到治疗学。当前的抗体市场规模及其相关的 产品估计约为2000亿美元。对具有自定义特异性的抗体的需求日益增加 为工程工作提供了丰富的环境。计算蛋白设计在 近年来。已经开发了许多方法来满足抗体工程需求。研究人员有 希望通过建模和设计，可以降低抗体开发和改进的成本 可以加快创建新靶向分子的速度。近年来，实验管道 已经简化了 结合信号。主要进步是直接从头开始设计活页夹，为 人工进化的潜在优化。但是，当前的计算方法尚未采用领先 由于当前的建模方法的许多缺点而引起的角色。我们在蛋白质方面拥有丰富的专业知识 近年来设计并开创了生成神经网络模型作为蛋白质结构的使用。 我们已经观察到神经网络方法的几个关键优势，而不是现有方法：即它们 进行推断，插值，结合拓扑信息并加速采样的能力。这些 优势可以独立开发或与现有方法结合使用，它们可以 显着提高蛋白质设计的性能。该项目旨在利用我们的几个新进步 迄今为止，已经开发出来激发新战略，以应对抗体工程的挑战或 通常，基于蛋白质设计。我们将开发新的工具和设计管道，以扩大特殊性 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）。该项目将提供可以在临床中部署的计算方法和构造 设置。这项研究的结果将具有很高的影响力。