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

点击翻译按钮获取中文摘要

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.