An Integrated Computational and Experimental Approach to Reveal Design Principles for Responsive Nanomaterials from Lipidated Disordered Proteins

非技术总结。脂质修饰蛋白是一类新兴的生物材料，在材料科学和医疗保健领域有着广泛的应用。然而，为了实现他们的承诺，在一系列溶液条件下，对这些混合生物材料形成和性能的基本原理有一个全面的了解是至关重要的。该合作项目将结合实验和模拟，通过改变脂化蛋白的组成和温度，为设计具有用户自定义特性的纳米颗粒创建蓝图。机器学习算法将用于迭代地整合来自模拟和实验的反馈，并推导出预测规则，指导纳米生物材料的设计，用于从化疗药物的递送到纳米材料的模板合成等特定应用。项目负责人还将为来自不同背景的学生（包括女性和STEM中代表性不足的少数族裔）开发一个综合实验和基于计算队列的研究体验项目。化学、生物学、材料科学和物理学交叉领域的研究培训将使这些受训者能够推进知识前沿，加速材料创新，并为美国在全球生物经济中的领导地位做出贡献。技术总结。尽管在过去十年中取得了进步，但由脂质和蛋白质组成的混合材料的设计在很大程度上仍然是一个临时的过程，阻碍了该领域的进展。这种限制的出现是因为脂质-蛋白质生物材料的设计空间的绝对大小阻碍了设计规则的经验阐明。因此，为了有效地揭示基本的设计原则，需要将实验、模拟和机器学习算法相结合的新方法。该合作项目利用了研究团队在生物合成以及脂化蛋白的计算和实验表征方面的互补专业知识。该项目将研究脂质修饰的内在无序蛋白聚合物（脂质-IDPPs），它将脂质的分层组织与IDPPs的温度响应行为结合起来，形成具有温度依赖特征的纳米和介孔组装体。使用该模型，该团队将开发预测设计规则，用于编程Lipo-IDPPs的分子语法（其构建块的物理化学性质，其主要序列，拓扑结构和两亲性结构）的热响应和分层组装。两个目标将被追求，每一个都使用一个闭环策略的建模，合成，并具有精确的遗传编码语法的一系列lipop - idpp的表征。使用一种综合的方法，明智地结合实验、模拟和机器学习的迭代反馈，该团队将：(1)根据Lipo-IDPPs的分子语法开发其热响应的预测模型；(2)确定一个大分子蓝图，用于根据生理相关温度定制其组装的结构层次。一系列lipop - idpp的材料特性作为温度的函数将使用多尺度实验（光谱学，散射和显微镜）和计算（原子和粗粒度模拟）方法进行表征。机器学习方法将用于将实验和计算结果结合到一个模型中，用于将分子属性映射到观察到的材料属性。优化的模型将提供对这类材料的可编程温度响应组装的分子语法的不同组成部分的生物物理贡献的见解，并可用于制定严格的预测规则，用于在生物相关环境中具有所需性能的lipop - idpp的逆设计。阐明控制Lipo-IDPPs多尺度组织的设计原则将使具有遗传可编程分子语法和性质的响应材料的合理合成成为可能。同时，将来自计算机和实验表征技术的迭代反馈与数据分析技术相结合，这也适用于其他混合材料，将加速生物材料的设计和发现。该奖项反映了美国国家科学基金会的法定使命，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 Summary. Proteins modified with lipids are an emerging class of biomaterials with diverse applications in materials science and healthcare. However, to realize their promise, it is critical to develop a comprehensive understanding of foundational principles governing the formation and properties of these hybrid biomaterials under a range of solution conditions. This collaborative project will integrate experiments and simulations to create a blueprint for the design of nanoparticles with user-defined properties by changing the composition of lipidated proteins and temperature. Machine learning algorithms will be used to iteratively integrate feedback from simulations and experiments and to derive predictive rules that guide the design of nano-biomaterials for specific applications ranging from the delivery of chemotherapeutics to the templated synthesis of nanomaterials. The project leaders also will develop an integrated experimental and computational cohort-based research experience program for students from diverse backgrounds, including women and underrepresented minorities in STEM. Research training at the interface of chemistry, biology, materials science, and physics will enable these trainees to advance the frontiers of knowledge, accelerate materials innovation, and contribute to U.S.’s leadership in the global bioeconomy.Technical Summary. Despite advancements in the past decade, the design of hybrid materials comprising lipid and protein building blocks remains a largely ad hoc process, impeding progress in the field. This limitation arises because the sheer size of the design space of lipid-protein biomaterials prohibits empirical elucidation of design rules. Thus, to efficiently reveal foundational design principles, new approaches that integrate experiments, simulations, and machine learning algorithms are needed. This collaborative project leverages the research team’s complementary expertise in biosynthesis as well as computational and experimental characterization of lipidated proteins. The project will investigate lipid-modified intrinsically disordered protein polymers (Lipo-IDPPs), which combine the hierarchical organization of lipids with temperature-responsive behavior of IDPPs to form nano- and meso-assemblies with temperature-dependent characteristics. Using this model, the team will develop predictive design rules for programming the thermo-response and hierarchical assembly of Lipo-IDPPs in their molecular syntax (the physicochemistry of their building blocks, their primary sequence, topology, and amphiphilic architecture). Two objectives will be pursued, each of which uses a closed-loop strategy of modeling, synthesis, and the characterization of a series of Lipo-IDPPs with precise genetically encoded syntax. Using an integrated approach that judiciously combines iterative feedback from experiments, simulations, and machine learning, the team will: (1) develop a predictive model of Lipo-IDPPs’ thermo-response based on their molecular syntax and (2) identify a macromolecular blueprint for tailoring the structural hierarchy of their assemblies as a function of physiologically relevant temperatures. The material properties of a series of Lipo-IDPPs as a function of temperature will be characterized using multiscale experimental (spectroscopy, scattering, and microscopy) and computational (atomistic and coarse-grained simulations) approaches. Machine learning methods will be used to combine experimental and computational results into a model for mapping molecular attributes to observed material properties. The optimized model will provide insights into the biophysical contribution of different components of molecular syntax to the programmable temperature-responsive assembly of this class of materials and can be used to formulate rigorous and predictive rules for the inverse design of Lipo-IDPPs with desired properties in biologically relevant milieus. Elucidating the design principles governing the multiscale organization of Lipo-IDPPs will enable the rational synthesis of responsive materials with genetically programmable molecular syntax and properties. And the integration of iterative feedback from in silico and experimental characterization techniques with data analytics, which is applicable to other hybrid materials, will accelerate biomaterials’ design and discovery.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.