Collaborative Research: DMREF: Machine Learning and Robotics for the Data-Driven Design of Protein-polymer Hybrid Materials

非技术描述：蛋白质被广泛用作疾病治疗和诊断的试剂，以及商业和工业过程的催化组分。在几乎所有的应用中，聚合物被用作稳定成分，以增加蛋白质在恶劣和外来环境中的耐久性，但由于与蛋白质表面的非特异性相互作用，绝大多数稳定聚合物提供适度的保护。此外，这些制剂中使用的蛋白质的表面性质是在温和的生物环境中工作的进化结果，而不是所需的苛刻的非生物环境。原则上，具有定制化学性质的复杂蛋白质-聚合物杂合体将通过基于工程互补相互作用将聚合物紧密包裹在蛋白质周围来促进上级保护。这种定制的配方即使在非常恶劣的条件下也能稳定蛋白质的天然状态，在无数的工业和军事应用中具有巨大的价值。然而，这些新材料由于其复杂性而非常难以设计。为了应对这一挑战，该项目将联合收割机与机器人技术相结合，利用数据分析和优化工具快速发现新的蛋白质-聚合物混合材料。随着时间的推移，聚合数据将用于训练高级ML模型，这些模型可应用于各种新型材料的功能预测和设计。同样重要的是，这项研究将侧重于年轻数据材料科学家的跨学科培训，他们将准备进入劳动力市场，帮助革命和设计未来的材料。这项研究将采用罗格斯大学，普林斯顿大学和空军研究实验室（AFRL）之间的大规模合作努力。技术描述：目前的方法来设计互补蛋白质-聚合物相互作用依赖于劳动密集型的试错实验，由于缺乏一个可推广的物理化学框架，可以指导同时设计的聚合物和蛋白质成分在多个长度尺度。这项研究将实施一种新的机器学习驱动的，自下而上的材料工程范式，用于设计蛋白质-聚合物混合粒子;然后将这些混合粒子组织成蛋白质-聚合物混合组装体，以增强非生物环境中的稳定性。高通量聚合物和蛋白质生产，表征，多尺度分子模拟和机器学习将以闭环方式结合，以发现新型蛋白质-聚合物组合物并了解增强稳定性的物理化学驱动因素。通过这些反复的设计-构建-测试-学习循环，将确定产生稳健的蛋白质-聚合物杂化材料的基本设计原则，并大大缩短设计定制蛋白质-聚合物杂化材料的交付周期。该奖项反映了NSF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。

Non-technical Description: Proteins are widely employed as agents for disease therapy and diagnosis, as well as catalytic components of commercial and industrial processes. In almost all applications, polymers are used as stabilizing constituents to increase durability of proteins in harsh and foreign environments, but the vast majority of stabilizing polymers provide modest protection due to non-specific interactions with the protein surface. Moreover, the surface properties of proteins used in these formulations are the result of evolution for working in mild, biological environments as opposed to the desired harsh, abiological ones. In principle, complex protein-polymer hybrids with tailored chemistries would facilitate superior protection by tightly wrapping polymer around the protein based on engineered complementary interactions. Such tailored formulations would stabilize the protein in its native state even under remarkably harsh conditions and have tremendous value in myriad industrial and military applications. However, these new materials are extraordinarily difficult to design due to their complexity. To address this challenge, this project will combine machine learning (ML) with robotics to rapidly discover new protein-polymer hybrid materials using data analytics and optimization tools. Over time, aggregated data will be used to train advanced ML models that can be applied to the prediction and design of functionality in a wide variety of novel materials. Equally important, this research will focus on the cross-disciplinary training of young data material scientists who will be prepared to enter the workforce and help revolutionize and engineer future materials. This research will feature a large collaborative effort between Rutgers University, Princeton University, and the Air Force Research Laboratory (AFRL).Technical Description: Current approaches to designing complementary protein-polymer interactions rely on labor intensive trial-and-error experimentation due to the lack a generalizable physicochemical framework that can guide the simultaneous design of both polymer and protein constituents at multiple length scales. This research will implement a novel machine learning-driven, bottom-up materials engineering paradigm for the design of protein-polymer hybrid particles; these hybrid particles will then be organized into protein-polymer hybrid assemblies to enhance stability in abiological environments. High-throughput polymer and protein production, characterization, multi-scale molecular simulation, and machine learning will be combined in a closed-loop fashion to both discover novel protein-polymer compositions and understand the physicochemical drivers for enhanced stability. Using these iterative Design-Build-Test-Learn cycles, underlying design principles for generating robust protein-polymer hybrids will be ascertained and the lead time for designing tailor-made protein-polymer hybrid materials will be substantially shortened.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.