Collaborative Research: MFB: Integrating Deep Learning and High-throughput Experimentation to Rapidly Navigate Protein Fitness Landscapes for Non-native Enzyme Catalysis

合作研究：MFB：整合深度学习和高通量实验，快速探索非天然酶催化的蛋白质适应性景观

• 批准号: 2226475
• 负责人: Jared Lewis
• 金额: $ 56.46万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-11-01 至 2025-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226475&HistoricalAwards=false
• 关键词: Collaborative; Research; MFB; Integrating; Deep

Understanding the relationship between protein structure and function remains a major challenge. This knowledge would benefit drug design, recycling, and chemical production. This project is designed to learn how to create proteins that will facilitate reactions seen in nature. Artificial intelligence will interpret the data generated by experiments. Two classes of enzymes will be modified to facilitate novel reactions. To help diversify the STEM workforce, workshops in machine learning will be offered to students interested in protein design. Summer research opportunities will be offered to high school and undergraduate students traditionally underrepresented in STEM fields.In this project, protein engineering is treated as a Bayesian optimization problem, with the objective to explore sequence space for improved specific activity. This approach models both the expected activity and the uncertainty of the prediction made. Training deep learning models is data intensive. A convolution neural net (CNN) using transformer architecture will use simulated sequence-function data to pretrain. The simulated data will be generated using Rosetta. Pretrained CNN will be refined with experimental data generated using combinatorial codon mutagenesis (CCM). Enzyme activity in single bacterial cells will be monitored using GFP expression, FACS-based screening, and next-generation DNA sequencing to determine the corresponding amino acid sequences. Biosensor screening can suffer from crosstalk when multiple cells are present. A picoliter-scale microdroplet screening technology developed in the Romero lab will be utilized to avoid this issue. A simulated annealing algorithm to randomly search over sequence positions and degenerate codons for libraries with high values for the expected batch BO objective will be developed. In addition, a probabilistic program using sampling-based inference to estimate the optimal combination of codons will be designed and implemented.This project is jointly supported by the Division of Chemical, Bioengineering, Environmental and Transport Systems (CBET), the Division of Chemistry (CHE), and the Division of Information and Intelligent Systems (IIS).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.

了解蛋白质结构和功能之间的关系仍然是一个重大挑战。这些知识将使药物设计，回收和化学生产受益。该项目旨在学习如何创建蛋白质，以促进自然界中看到的反应。人工智能将解释实验生成的数据。将修改两类酶以促进新反应。为了帮助多样化的STEM劳动力，将向对蛋白质设计感兴趣的学生提供机器学习的研讨会。夏季的研究机会将为高中提供，传统上的本科生在STEM领域的代表性不足。在该项目中，蛋白质工程被视为贝叶斯优化问题，目的是探索序列空间以改善特定活动。这种方法模拟了预测的预期活动和预测的不确定性。培训深度学习模型是数据密集型的。使用变压器体系结构的卷积神经网（CNN）将使用模拟序列功能数据来预处理。 模拟数据将使用Rosetta生成。预处理的CNN将通过使用组合密码子诱变（CCM）生成的实验数据来完善。 将使用GFP表达，基于FACS的筛选和下一代DNA测序来监测单个细菌细胞中的酶活性，以确定相应的氨基酸序列。 当存在多个细胞时，生物传感器筛选可能会遭受串扰。将利用Romero Lab开发的Picoliter-Scale尺度筛选技术来避免此问题。 将开发一种模拟的退火算法，以随机搜索序列位置和退化密码子，以获取具有高值的预期批处理BO目标值的库。此外，使用基于抽样的推理进行概率计划，以估算密码子的最佳组合将设计和实施。该项目由化学，生物工程，环境和运输系统（CBET），化学划分（CHE），通过信息和智能系统（IIS）的裁决进行了反映，并由NSF的代表进行了反映，该项目由化学，生物工程，环境和运输系统（CBET）共同支持。智力优点和更广泛的影响审查标准。