SBIR Phase I: Empowering Discovery of COVID-19 Vaccine through Large Scale Unsupervised Deeplearning for Electron Microscopy Data

SBIR 第一阶段：通过电子显微镜数据的大规模无监督深度学习促进 COVID-19 疫苗的发现

• 批准号: 2032548
• 负责人: Vinay Rao
• 金额: $ 25.6万
• 依托单位: RocketML Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2032548&HistoricalAwards=false
• 关键词: SBIR; Phase; Empowering; Discovery; COVID

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to empower researchers to make bio-structure discoveries by eliminating computational bottlenecks to shorten drug discovery cycles and time-to-market, such as those needed during the COVID-19 pandemic. Most modern drug discovery projects start with protein target identification and verification to obtain a 'verified drug target'. Structural vaccinology is a rational approach to generate an effective vaccine. For instance, knowing the structures of the proteins on the surface of the virus can guide vaccine design, and knowing the structures of viral enzymes can lead directly to drug treatments that inhibit replication. Cryo-electron microscopy (cryo-EM) is a method to image important structures. The proposed innovation enables fast classification of images and creation of 3-dimensional structures by using advanced deep learning techniques. This will accelerate analysis and discovery by more than 50%. This Small Business Innovation Research (SBIR) Phase I project delivers an autonomous image processing pipeline for Cryo-EM data sets in real time at benchmark accuracies. On average, each cryo-EM microscope produces up to 2 TB of data daily. High-resolution image sizes from Cryo-EM are typically on the order of 10k x 10k pixels, requiring specialized software to process, classify and convert these images into 3D structures. Traditional fully supervised deep learning methods fail due to diversity of biomolecules and lack of labeled images. The proposed approach utilizes large-scale “deep unsupervised” learning methods that can train AI systems and deliver accurate results without labeled data on full resolution image sizes by use of High Performance Computing (HPC) clusters. The project involves development of cloud infrastructure software that eliminates GPU memory limitations. Further, the proposed machine learning approach dramatically accelerates and improves cryo-EM particle picking, both in 2-D images from single particle reconstruction workflows and in 3-D images of intact cells in electron tomography workflows at benchmark accuracies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项小企业创新研究（SBIR）第一阶段项目的更广泛影响将是，通过消除计算瓶颈，使研究人员能够进行生物结构发现，从而缩短药物发现周期和上市时间，如COVID-19大流行期间所需的时间。大多数现代药物发现项目从蛋白质靶点鉴定和验证开始，以获得“已验证的药物靶点”。结构疫苗学是产生有效疫苗的一种合理方法。例如，了解病毒表面蛋白质的结构可以指导疫苗设计，了解病毒酶的结构可以直接导致抑制复制的药物治疗。低温电子显微镜（cryo-EM）是一种成像重要结构的方法。提出的创新通过使用先进的深度学习技术实现图像的快速分类和三维结构的创建。这将使分析和发现的速度提高50%以上。这项小型企业创新研究（SBIR）第一阶段项目为Cryo-EM数据集提供了一个自主图像处理管道，实时达到基准精度。平均而言，每台冷冻电镜每天产生高达2tb的数据。Cryo-EM的高分辨率图像尺寸通常为10k x 10k像素，需要专门的软件来处理、分类并将这些图像转换为3D结构。传统的全监督深度学习方法由于生物分子的多样性和缺乏标记图像而失败。所提出的方法利用大规模的“深度无监督”学习方法，可以训练人工智能系统，并通过使用高性能计算（HPC）集群，在没有标记数据的情况下，提供全分辨率图像尺寸的准确结果。该项目涉及云基础设施软件的开发，消除了GPU内存的限制。此外，所提出的机器学习方法大大加速和改善了低温电镜粒子拾取，无论是在单粒子重建工作流程的二维图像中，还是在电子断层扫描工作流程中完整细胞的三维图像中，都具有基准精度。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。