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: 10505034
• 负责人: Possu Huang
• 金额: $ 41.6万
• 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/10505034
• 关键词: 2019-nCoV; 3-Dimensional; APLN gene; Address; Adopted; Agonist; Algorithm Design; Antibodies; Antivenins; Architecture; Area; Binding; Biotechnology; CXCR4 gene; Cardiac Myocytes; Clinical; Complex; Computational algorithm; Computing Methodologies; Custom; Data; Data Science; Development; Diagnostic tests; Docking; Engineering; Environment; Epitopes; Evolution; Experimental Designs; Face; Fingers; G-Protein-Coupled Receptors; Generations; Goals; Home; Image; Immune; Immunoglobulins; Libraries; Medical; Medical Technology; Methods; Modeling; Modernization; Molecular; Molecular Conformation; Myocardial Ischemia; Neural Network Simulation; Outcome; Performance; Play; Positioning Attribute; Probability; Process; Protein Engineering; Proteins; Recording of previous events; Reporting; Research; Research Personnel; Role; Sampling; Scheme; Side; Signal Transduction; Snake Venoms; Snakes; Specificity; Speed; Structure; Technology; Testing; Therapeutic; Time; Toxin; Training; Variant; Vertebral column; antibody engineering; artificial neural network; base; chemokine receptor; cluster computing; combinatorial; computational platform; cost; deep learning; deep neural network; design; detection platform; flexibility; generative adversarial network; improved; interest; interfacial; knowledge base; loss of function; method development; model design; molecular recognition; nanobodies; neural network; novel; programs; protein structure; receptor; receptor binding; response; scaffold; screening; tool

PROGRAM 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 multi- specific 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.

节目摘要/摘要 抗体作为分子检测平台，在现代医学中发挥着越来越重要的作用 技术，从诊断测试到成像，再到治疗学。目前的市场规模 抗体及其相关产品估计价值约2000亿美元。不断增长的 对具有定制特异性的抗体的需求为工程工作提供了丰富的环境。 近年来，蛋白质计算设计取得了长足的进步。许多方法已经被 为满足抗体工程需求而开发。研究人员曾希望，通过 建模和设计，可以降低抗体开发和改进的成本，并 创造新的靶向分子的步伐可以加快。近年来，试验性的 流水线已经简化，但即便如此，广泛的库和屏幕宣传活动通常是 获得初始绑定信号所需的。一个主要的进步是直接设计一个活页夹 从零开始，通过人工进化为潜在的优化提供了信号。当前 然而，由于一些缺点，计算方法并没有占据主导地位。 使用当前的建模方法。我们在蛋白质设计方面拥有丰富的专业知识， 近年来，人们率先使用生成神经网络模型来研究蛋白质结构。我们 已经观察到神经网络方法相对于现有方法的几个关键优势： 也就是说，它们进行推理、内插、合并拓扑信息以及 加快采样速度。这些优势可以单独开发，也可以结合使用 与现有的方法相结合，它们可以显著提高蛋白质设计的性能。这 该项目旨在利用我们迄今开发的几个新进展来激发新的灵感 应对抗体工程或基于人工智能的蛋白质设计中的挑战的策略 将军。我们将开发新的工具和设计管道，以扩大多个 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)。该项目将提供计算方法和构造，可以 部署在临床环境中。这项研究的结果将产生很大的影响。