LOCK-AND-KEY INTERACTIONS BETWEEN CHIRAL NANOPARTICLES AND PROTEINS

纳米尺度的超小颗粒，通常被称为纳米颗粒，是治疗癌症、保存疫苗和抑制原本无法治疗的有害细菌感染的有效新药物。纳米颗粒的几何形状对它们的医疗效果起着重要作用，但了解如何制造复杂形状的颗粒，从而很好地为患者服务，也是至关重要的。这个项目将把生物分子之间的锁和键相互作用的概念扩展到纳米颗粒和蛋白质之间的相互作用。这项研究的关键是纳米颗粒和蛋白质的共同几何性质，即手性。此属性定义复杂形状的几何对象是否可以彼此匹配。手性对于复杂形状之间的几何匹配的重要性可以通过螺旋相互缠绕来例证，这是生物分子的基础。这个项目将建立一种基于手性和机器学习的数学测量方法来设计复杂形状的纳米粒子的一般方法。该项目的首要目标是为医学研究人员提供现成的算法，预测与蛋白质形成关键复合体的纳米颗粒的形状，以及合成它们的化学方法。考虑到锁和键相互作用在生物学中的普遍重要性，基于手性的工具箱将被改造并普遍适用于生物技术、生物催化和生物修复。该项目的教育使命将包括培训范围广泛的年轻科学家，从高中生到博士后研究人员，了解纳米粒子在医疗需求中的应用。这将增加美国在创新医疗技术发展方面的竞争力。首席研究员还将指导来自底特律地区STEM领域传统上代表性不足的群体的年轻同事，宣传手性在生物学和纳米技术中的重要性。密歇根大学的研究小组将利用石墨烯、无定形碳和氧化铈的高度生物相容性纳米粒子，开发一套用于纳米粒子和蛋白质的统一描述符。项目目标是为预测纳米颗粒-蛋白质复合体建立全面的机器学习模型；评估分子和纳米尺度手性作为形成纳米颗粒-蛋白质复合体的一个重要几何参数；以及开发用于生物和生物医学应用的可行纳米候选颗粒的设计方法。不同类型的纳米粒子将被用来在分子、纳米级和亚纳米级引入手性，这些手性将使用新开发的多尺度手性向量方法进行量化。根据合成方法的不同，手性矢量将作为设计复杂形状纳米粒子的一般工程原则。机器学习算法和最先进的神经网络将被用于从扭曲的石墨烯纳米片开始预测合成手性纳米颗粒。对于无定形碳和氧化铈纳米颗粒，将评估设计方法的通用性。手性措施的预测能力将被证明为纳米颗粒和抗生素耐药细菌的淀粉样蛋白之间的锁和钥匙复合体。该项目的实际意义将通过利用淀粉样纤维作为其生物膜的纳米盔甲来抑制细菌感染的效果来评估。该项目的更广泛影响将包括一个强大的外展计划，该计划得到了PI与来自各种背景的年轻科学家合作的广泛历史的支持。具体地说，PI将把来自底特律地区的贫困背景的非裔美国学生纳入这个项目。外展活动将侧重于促进高中生的教学和培训，以及指导底特律地区研究能力有限的大学的年轻同事。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Ultrasmall particles with nanoscale dimensions, often referred to as nanoparticles, are potent new agents for treatment of cancers, preservation of vaccines and inhibition of harmful otherwise untreatable bacterial infections. The geometric shape of the nanoparticles plays a significant role in their medical efficacy but understanding how to make particles with complex shapes, which serve the patients well, is also critical. This project will extend the concept of lock-and-key interactions between biomolecules to the interactions between nanoparticles and proteins. Essential for this study is the common geometric property of nanoparticles and proteins known as chirality. This property defines whether the geometrical objects of complex shapes can fit each other. The importance of chirality for geometrical matches between complex shapes can be exemplified by the helices wrapping around each other, which is foundational for biomolecules. This project will establish a general methodology to design nanoparticles with complex shapes based on mathematical measures of chirality and machine learning. The overarching goal of this project is to provide medical researchers with ready-to-use algorithms predicting the shape of nanoparticles forming lock-and-key complexes with proteins and chemical methods for their synthesis. Considering the universal importance of the lock-and-key interactions in biology, the toolbox based on chirality will be adapted and universally applicable for biotechnology, biocatalysis, and bioremediation. The educational mission of the project will include training a broad range of young scientists from high school students to postdoctoral researchers in the applications of nanoparticles for medical needs. This will increase the competitiveness of the United States in the development of innovative medical technologies. The principal investigator will also mentor young colleagues from groups traditionally under-represented in STEM fields within the Detroit area, communicating the importance of chirality in biology and nanotechnology. The University of Michigan team will develop a set of unifying descriptors for nanoparticles and proteins using highly biocompatible nanoparticles from graphene, amorphous carbon, and cerium oxide. Project goals are to establish comprehensive machine learning models for the prediction of nanoparticle-protein complexes; evaluate molecular and nanoscale chirality as a significant geometric parameter for the formation of nanoparticle-protein complexes; and develop methodologies for designing viable nanoparticle candidates for biological and biomedical applications. Diverse types of nanoparticles will be used to introduce chirality at molecular, nanoscale, and sub-nanoscale levels, which will be quantified using the newly developed approach of multiscale chirality vectors. The chirality vectors will serve as a general engineering principle for designing nanoparticles with complex shapes depending on the synthetic approach. The machine learning algorithms and state-of-the-art neural networks will be evaluated for the predictive synthesis of chiral nanoparticles starting from the twisted graphene nanoplatelets. The generality of the design methodology will be evaluated for amorphous carbon and cerium oxide nanoparticles. The predictive power of chiral measures will be demonstrated for lock-and-key complexes between nanoparticles and amyloid proteins of antibiotic-resistant bacteria. The practical significance of this project will be evaluated by the efficacy of inhibition of bacterial infections utilizing amyloid fibrils as nanoscale armor for their biofilms. The broader impact of the project will include a strong outreach program, supported by the extensive history of the PI in collaborating with young scientists from all backgrounds. Specifically, the PI will incorporate African American students from underprivileged backgrounds from the Detroit area in this project. Outreach activities will be focused on promoting teaching and training of high school students as well as on mentoring young colleagues from universities with limited research capabilities in the Detroit area.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.