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.

项目概要/摘要 抗体作为分子检测平台，在现代医疗技术中的重要性日益凸显， 从诊断测试到成像，再到治疗。目前抗体及其相关产品的市场规模 产品价值估计约为2000亿美元。对具有定制特异性的抗体的需求不断增长 为工程工作提供了丰富的环境。计算蛋白质设计在以下领域取得了快速进展 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，降低抗体研发和改进的成本 并且可以加快创造新靶向分子的步伐。近年来，实验管道 已被简化，但即便如此，通常需要广泛的图书馆和屏幕活动才能获得初步的 结合信号。一个重大进步是直接从头开始设计绑定器，为 人工进化的潜在优化。然而，目前的计算方法尚未取得领先地位。 由于当前建模方法存在许多缺点，因此无法发挥作用。我们在蛋白质领域拥有丰富的专业知识 近年来，设计并率先使用生成神经网络模型来研究蛋白质结构。 我们观察到神经网络方法相对于现有方法的几个关键优势：即 进行推理、插值、合并拓扑信息和加速采样的能力。这些 优点可以独立开发或与现有方法结合使用，并且可以 显着提高蛋白质设计的性能。该项目旨在利用我们的几项新进展 迄今为止，已经开发出新的策略来应对抗体工程或人工智能的挑战 一般而言，基于蛋白质的设计。我们将开发新工具并设计管道以扩展特殊性 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）。该项目将提供可部署在临床中的计算方法和结构 设置。这项研究的结果将具有很大的影响力。