Generative neural networks for structure-based antibody design

项目总结/摘要 作为分子检测平台，抗体在现代医学技术中的重要性日益增加， 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 产品估计约为2000亿美元。对定制特异性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： 近年已经开发了许多方法来解决抗体工程的需要。研究人员 希望通过建模和设计，降低抗体开发和改进的成本 并且可以加快创建新靶向分子的步伐。近年来，实验管道 已经被简化，但即使如此，通常需要大量的图书馆和屏幕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一个活页夹， 人工进化的潜在优化。然而，目前的计算方法还没有采取领先的 由于目前的建模方法存在一些缺点，我们在蛋白质领域拥有丰富的专业知识 近年来，他设计并率先使用蛋白质结构的生成神经网络模型。 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：即， 能够进行推断、插值、整合拓扑信息和加速采样。这些 这些优点可以单独开发，也可以与现有方法结合使用，它们可以 大大提高了蛋白质设计的性能。该项目旨在利用我们的几项新进展， 迄今为止，已经发展到激发新的战略，以应对抗体工程或AI的挑战， 蛋白质的设计。我们将开发新的工具和设计管道，以扩大具体性 用于多特异性抗体和定制表位特异性抗体（使用蛇毒和CXCR 4作为 目标）。这个项目将提供计算方法和结构，可以部署在临床 设置.这项研究的结果将具有很大的影响力。

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.