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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：
10252011
负责人：
Nigel F Reuel
金额：
$ 36.4万
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/10252011
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 Electronics Engineering Funding Future Generations Genetic Template In Vitro Iowa Label Libraries Machine Learning Measurement Membrane Methods Microfluidics Optics Patients Pattern Penetration Pharmaceutical Preparations Phenotype Property Protein Biosynthesis Protein Engineering Proteins Research Small Business Innovation Research Grant Speed Stream Surface System Techniques Technology Testing Therapeutic Uses Time TimeLine Training Translating United States National Institutes of Health Universities Vision Work base cost deep learning deep neural network design efficacious treatment falls improved in silico learning strategy nanosensors new technology novel strategies programs prototype radio frequency sensor side effect synthetic biology therapeutic protein 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.

项目总结