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Unsupervised optimization of protein therapeutics using closed-loop in vitro synthesis, nanosensing, and deep-learning

使用闭环体外合成、纳米传感和深度学习对蛋白质疗法进行无监督优化

基本信息

批准号：
10840698
负责人：
Nigel F Reuel
金额：
$ 16.27万
依托单位：
IOWA STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-05 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10840698
关键词：
Affinity Algorithms Big Data Binding Binding Proteins Biological Products Biological Response Modifier Therapy Biomanufacturing Biosensor Cell membrane Cells Clinic Coupled Custom DNA biosynthesis Data Set Development Devices Disease Engineering Funding Future Generations Genetic Template In Vitro Iowa Label Libraries Machine Learning Marketing Measurement Membrane Methods Microfluidics Optics Patients Pattern Penetration Phenotype Property Protein Biosynthesis Protein Engineering Proteins Research Small Business Innovation Research Grant Speed Stream Surface System Techniques Technology Testing Therapeutic Uses Time Training Translating United States National Institutes of Health Universities Vision Work bioelectronics cost deep learning deep neural network design design,build,test efficacious treatment falls improved in silico learning strategy manufacture nanosensors new technology novel strategies programs prototype radio frequency sensor side effect synthetic biology therapeutic protein timeline tool treatment optimization water quality wound healing

项目摘要

Project Summary Overview of research: The Reuel Group at Iowa State University (founded Fall 2016) seeks to develop new materials, methods, and measurement devices for biomanufacturing, biotherapeutics, and biosensors. We have active work-streams in 1) optical nanosensors for protein binding, enzymatic activity, and cell membrane disruption, 2) scalable and reliable cell free protein synthesis (CFPS) methods for protein prototyping (extract and genetic template improvements), 3) microfluidics for droplet generation and measurement of CFPS products, interrogated by nanosensors, 4) algorithms for `big data' generated from nanosensors (machine learning and deep learning methods), 5) engineered endospores for time-delayed synthetic biology circuits, and 6) resonant radio frequency sensors for biomanufacturing, wound healing, and water quality. The overall vision of the research program is to simplify and improve the design and manufacturing of biological products (cells and proteins) for applications in therapies, advanced materials, and bio-electronics. Protein based therapies have demonstrated in clinic to be a potent tool in the treatment of many diseases. In recent years, the design, build, and test cycle to find therapies for new disease targets has improved dramatically using techniques such as surface display coupled to evolutionary selection. However, these mutagenic approaches have a few limitations, namely: 1) they require a suitable, naturally occurring sequence as a starting point, 2) they frequently optimize solely on a single desired feature, and 3) they operate as a `black box', meaning that generalizable design rules for in silico prediction of future products is not possible. It is the purpose of this MIRA for ESI research plan to design a closed-loop system that allows for unsupervised design and discovery of protein therapeutics that overcomes these limitations. Over the next five years we will build and integrate the system components which include enzymatic DNA synthesis coupled to cell free protein synthesis to rapidly prototype libraries of custom proteins in micro-droplet reactors. These proteins will then be characterized in the micro-droplets using optical nanosensors, to test for desired features such as stability, binding affinity, selectivity, hydrolytic activity, and/or membrane penetration. This will produce a large labeled data set (tying sequence to phenotypic properties) that can be used to train a deep learning neural network to self-determine sequence patterns for specific properties. Once the tuning coefficients of the network are found, the algorithm will then predict next best sequences which will be synthesized, tested, etc. such that the design loop progresses unsupervised until optimization criteria are met. This new approach will result in faster development of protein therapies that are optimized based on multiple criteria and not tied to existing, natural sequences. For patients this translates to more efficacious therapies with less side effects and a potential for reduced cost (due to shortened design timeline). At the end of the five-year project we will seek to translate this technology, via NIH SBIR funding, such that the new technology can make an impact on actual therapies.

项目摘要研究综述：爱荷华州立大学鲁伊尔小组(成立于2016年秋季)寻求开发新的用于生物制造、生物疗法和生物传感器的材料、方法和测量设备。我们在1)用于蛋白质结合、酶活性和细胞膜的光学纳米传感器中有活跃的工作流中断，2)可扩展和可靠的蛋白质原型的无细胞蛋白质合成(CFPS)方法(摘录和遗传模板改进)，3)用于液滴产生和CFPS测量的微流体产品，由纳米传感器询问，4)由纳米传感器(机器)产生的大数据的算法学习和深度学习方法)，5)用于延时合成生物电路的工程内孢子，以及6)用于生物制造、伤口愈合和水质的谐振式射频传感器。整体而言该研究计划的愿景是简化和改进生物制品的设计和制造 (细胞和蛋白质)在治疗、先进材料和生物电子学中的应用。基于蛋白质的临床证明，治疗方法是治疗许多疾病的有力工具。近年来，为新的疾病靶点寻找治疗方法的设计、构建和测试周期已经显著改进，使用与进化选择相结合的表面显示等技术。然而，这些诱变的方法有几个限制，即：1)它们需要一个合适的、自然发生的序列作为起点，2) 它们经常只针对一个所需的功能进行优化，以及3)它们作为“黑匣子”运行，这意味着对未来产品进行电子预测的通用设计规则是不可能的。这就是这件事的目的 Mira for ESI研究计划设计一个闭环系统，允许无人监督的设计和发现克服这些限制的蛋白质疗法。在接下来的五年里，我们将建立和整合包括酶DNA合成和游离蛋白质合成在内的系统组件微滴反应器中定制蛋白质的快速原型文库。然后这些蛋白质就会被使用光学纳米传感器来表征微滴，以测试所需的特征，例如稳定性，结合亲和力、选择性、水解性和/或膜穿透性。这将产生一个很大的标签数据集(将序列与表型属性捆绑在一起)，可用于训练深度学习神经网络自行确定特定属性的序列模式。一旦网络的调谐系数为然后，该算法将预测将被合成、测试等的下一个最佳序列，以便设计循环在无人监督的情况下进行，直到满足优化标准。这种新的方法将导致更快的基于多个标准优化的蛋白质疗法的开发，不受现有的、自然的序列。对于患者来说，这意味着更有效的治疗方法，副作用更少，并有可能降低成本(由于缩短了设计时间)。在为期五年的项目结束时，我们将寻求翻译这项技术通过NIH SBIR资助，使新技术能够对实际治疗产生影响。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Single-Walled Carbon Nanotube Probes for the Characterization of Biofilm-Degrading Enzymes Demonstrated against Pseudomonas aeruginosa Extracellular Matrices.

DOI：
10.1021/acs.analchem.1c03633
发表时间：
2022-01-18
期刊：
ANALYTICAL CHEMISTRY
影响因子：
7.4
作者：
Agarwal, Sparsh;Kallmyer, Nathaniel E.;Vang, Dua X.;Ramirez, Alma, V;Islam, Md Monirul;Hillier, Andrew C.;Halverson, Larry J.;Reuel, Nigel F.
通讯作者：
Reuel, Nigel F.

Anaerobic Conditioning of E. coli Cell Lysate for Enhanced In Vitro Protein Synthesis.