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

基本信息

批准号：
10705666
负责人：
Possu Huang
金额：
$ 32.95万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
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/10705666
关键词：
2019-nCoV 3-Dimensional APLN gene Acceleration Address Adopted Agonist Algorithm Design Antibodies Antivenins Architecture Area Binding Biotechnology CXCR4 gene Cardiac Myocytes Clinical Complex Computational algorithm Computing Methodologies 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 Learning Libraries Marketing 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 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

项目摘要

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.

项目概要/摘要抗体作为分子检测平台，在现代医学中的重要性日益凸显技术，从诊断测试到成像，再到治疗。目前的市场规模为抗体及其相关产品估计价值约2000亿美元。不断成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，计算蛋白质设计取得了快速进展。已经有很多方法为满足抗体工程需求而开发。研究人员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，广泛的图书馆和屏幕活动通常是需要获得初始结合信号。一个重大进步是直接设计活页夹从头开始，为人工进化的潜在优化提供信号。当前的然而，由于许多缺点，计算方法尚未发挥主导作用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，并且近年来率先使用生成神经网络模型来研究蛋白质结构。我们与现有方法相比，我们观察到神经网络方法的几个关键优势：即，它们进行推理、插值、合并拓扑信息的能力，以及加速采样。这些优点可以单独开发，也可以结合使用与现有方法相结合，它们可以显着提高蛋白质设计的性能。这该项目旨在利用我们迄今为止开发的几项新进展来激发新的应对抗体工程或基于人工智能的蛋白质设计挑战的策略一般的。我们将开发新的工具和设计管道，以扩展多方面的特殊性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）。该项目将提供可用于计算的方法和结构部署在临床环境中。这项研究的结果将具有很大的影响力。