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.

项目总结/文摘