EAGER: Collaborative Research: Type II: Data-Driven Characterization and Engineering of Protein Hydrophobicity

EAGER：合作研究：II 类：数据驱动的蛋白质疏水性表征和工程

• 批准号: 1844505
• 负责人: Andrew Ferguson
• 金额: $ 5.3万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1844505&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Type; II

NONTECHNICAL SUMMARYThis EAGER award supports research and education involving a new collaboration kindled at the MATDAT18 Datathon event focused on advancing understanding of how water interacts with proteins and complex molecular assemblies. Oil and water don't mix. Examples of this common wisdom are prevalent in everyday life from the sheen formed on a rain puddle by spilled gasoline, to the separation of oil and vinegar in a bottle of salad dressing. These are large-scale examples of a physical principle known as hydrophobicity - a word derived from Ancient Greek that characterizes the "horror for water" experienced by particular molecules. This physical principle is also active at microscopic scales, with hydrophobicity playing an important role in controlling the structure and function of molecules in water. Of particular interest is the behavior of proteins: a class of molecules that use the hydrophobic effect to perform functions critical to life, serving as - among many other things - enzymes to help break down food, hormones to regulate physiology, and antibodies to protect against infection. Some physical forces can be described by simple and elegant equations, such as Newton's law of gravitation or Coulomb's law of electrostatics, but decades of work have shown that no such simple descriptions seem to exist for hydrophobicity. Instead, the hydrophobic interaction is a very complicated force that depends sensitively on the details of the hydrophobic molecule and its interactions with the water molecules around it. Unraveling the details of this interaction in the context of proteins is important in helping understand the fundamentals of this ubiquitous and important force, and in helping discover and design proteins to serve as new drugs or novel molecular machines.How might one probe and understand the complexities of hydrophobicity? Artificial intelligence techniques are now ubiquitous in modern life, serving as recommendation engines for online shopping, automatically recognizing faces in camera phones, and enabling autonomous and assisted driving. Conventional computer programs work by executing a pre-programmed set of rules to achieve an outcome; modern artificial intelligence techniques are instead provided with a set of examples and automatically learn the rules from the data. This research project will use artificial intelligence techniques to learn the rules of hydrophobicity from computer simulations of water around proteins. Specifically, using sophisticated molecular simulations to model the interactions and dynamics of water molecules, and special tools to measure hydrophobicity, databases of the "horror for water" of different regions of the protein surface will be compiled. Artificial intelligence tools will then analyze these databases to find a mathematical model between hydrophobicity and the chemical composition and shape of the protein surface. The models learned in this way will help untangle the complexities of hydrophobicity, and can be used to quickly predict how proteins in water will behave. The tools will also be adapted and analyzed to provide human-interpretable explanations that help provide new understanding of hydrophobicity rather than just furnishing a complicated mathematical model.These research activities will provide new models and understanding of protein hydrophobicity that can be used to search for new drug molecules and engineer proteins with new structures and functions. The simulation and artificial intelligence tools will be made broadly available to the scientific community and general public through open source molecular simulation packages, free software libraries, and through online code sharing sites. Undergraduate students will be involved in the research projects through 10-week paid summer internships to be offered in each year of the award. These research experiences will be designed according to best practices in providing authentic and valuable training experiences, and special efforts will be made to recruit students from groups traditionally underrepresented in science, technology, engineering, and math fields.TECHNICAL SUMMARYThis EAGER award supports research and education involving a new collaboration kindled at the MATDAT18 Datathon event focused on integrating sophisticated molecular simulation tools with machine learning techniques to understand hydrophobicity at the nanoscale. The hydrophobic effect - the tendency for non-polar moieties to cluster together and exclude water molecules in aqueous solvent - plays an important role in the interactions and assemblies of complex molecules, such as cavitands, dendrimers, and proteins. However, quantifying the hydrophobicity of such molecules, which display complex chemical and topographical patterns at the nanoscale, has proven to be an enduring and open challenge. Recent work has illuminated the failure of additive approaches that attempt to break down molecular hydrophobicity as a sum of the hydrophobicities of the constituent surface groups, and demonstrated that hydrophobicity at the nanoscale is a complex, collective, many-body response of hydration waters to chemical and topographical surface cues. This complexity not only frustrates a fundamental molecular understanding of hydrophobicity at the nanoscale, but also has important practical consequences, such as the inability to accurately screen ligands for drug discovery.The overall goal of this research project is to conduct enhanced sampling molecular simulations to accurately quantify the hydrophobicity of an extensive library of nanostructured surfaces through the free energy of cavity formation, and to deploy supervised machine learning techniques to unveil new understanding of the physical, chemical, and topographical cues governing surface hydrophobicity. The central hypothesis of this project is that the application of data-centric tools can provide new understanding of the molecular determinants of hydrophobicity beyond what is possible with simple conceptual models and human intuition. The first objective of this work is to quantify the hydrophobicity of an extensive library of patterned self-assembled monolayer surfaces and proteins by estimating the free energy of interfacial cavity formation using enhanced sampling techniques. The second objective is to conduct supervised learning over the hydrophobicity libraries to construct quantitative structure property relationship models relating chemical composition and physical structure to the free energy of interfacial cavity formation. A number of machine learning techniques will be explored, including support vector machines, random forests, partial least squares regression, and artificial neural networks. The techniques will be adapted to be physics-aware by "baking in" the physics of hydrophobicity into the model, and to be explainable by furnishing human-interpretable understanding of their behaviors.Successful completion of this research will have impacts in both materials and data science. From a materials science perspective, unveiling the molecular determinants of protein hydrophobicity - the relation between the topographical and chemical patterns displayed by the protein and the free energy of cavity formation - will shed new light on the driving force behind protein interactions and assembly, furnish precepts for rational engineering of protein structure and function, and open up applications in virtual high-throughput screening for the computational discovery of drugs, ligands, bioseparation agents, and co-solutes to modulate protein solubility. From a data science perspective, this work will establish new physics/chemistry-aware machine learning tools whose behaviors are more interpretable and comprehensible in the analysis of molecular behaviors than generic off-the-shelf techniques.The Division of Materials Research and the Chemistry Division contribute funds to this award.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.

非技术摘要该 EAGER 奖项支持研究和教育，涉及在 MATDAT18 Datathon 活动中发起的一项新合作，重点是增进对水如何与蛋白质和复杂分子组件相互作用的理解。油和水不相溶。这种常识的例子在日常生活中很普遍，从溢出的汽油在雨坑中形成的光泽，到沙拉酱瓶中油和醋的分离。这些都是被称为“疏水性”的物理原理的大规模例子，该词源自古希腊语，描述了特定分子所经历的“对水的恐惧”。这一物理原理在微观尺度上也很活跃，疏水性在控制水中分子的结构和功能方面发挥着重要作用。特别令人感兴趣的是蛋白质的行为：一类利用疏水效应执行对生命至关重要的功能的分子，其中包括帮助分解食物的酶、调节生理机能的激素以及防止感染的抗体。感染。一些物理力可以用简单而优雅的方程来描述，例如牛顿万有引力定律或库仑静电定律，但数十年的工作表明，疏水性似乎不存在这样简单的描述。相反，疏水相互作用是一种非常复杂的力，它敏感地取决于疏水分子的细节及其与周围水分子的相互作用。揭示蛋白质背景下这种相互作用的细节对于帮助理解这种普遍存在的重要力量的基本原理以及帮助发现和设计蛋白质以用作新药物或新型分子机器非常重要。人们如何探索和理解其复杂性疏水性？人工智能技术如今在现代生活中无处不在，作为在线购物的推荐引擎、自动识别拍照手机中的面孔以及实现自动驾驶和辅助驾驶。传统的计算机程序通过执行一组预先编程的规则来实现结果。相反，现代人工智能技术提供了一组示例，并自动从数据中学习规则。该研究项目将利用人工智能技术从蛋白质周围水的计算机模拟中学习疏水性规则。具体来说，使用复杂的分子模拟来模拟水分子的相互作用和动力学，并使用特殊工具来测量疏水性，将编译蛋白质表面不同区域的“水恐惧”数据库。然后人工智能工具将分析这些数据库，以找到疏水性与蛋白质表面的化学成分和形状之间的数学模型。以这种方式学习的模型将有助于解开疏水性的复杂性，并可用于快速预测水中蛋白质的行为。这些工具还将进行调整和分析，以提供人类可解释的解释，有助于提供对疏水性的新理解，而不仅仅是提供复杂的数学模型。这些研究活动将提供对蛋白质疏水性的新模型和理解，可用于寻找新的蛋白质疏水性。药物分子和工程蛋白质具有新的结构和功能。模拟和人工智能工具将通过开源分子模拟包、免费软件库和在线代码共享网站广泛向科学界和公众开放。本科生将通过该奖项每年提供的为期 10 周的带薪暑期实习参与研究项目。这些研究经验将根据提供真实和有价值的培训经验的最佳实践进行设计，并将特别努力从传统上在科学、技术、工程和数学领域代表性不足的群体中招收学生。技术摘要该 EAGER 奖项支持研究和教育涉及在 MATDAT18 Datathon 活动中引发的新合作，重点是将复杂的分子模拟工具与机器学习技术相结合，以了解纳米尺度的疏水性。疏水效应（非极性部分聚集在一起并排除水性溶剂中的水分子的趋势）在复杂分子（例如空配体、树枝状聚合物和蛋白质）的相互作用和组装中发挥着重要作用。然而，量化这些在纳米尺度上表现出复杂的化学和形貌模式的分子的疏水性已被证明是一个持久且开放的挑战。最近的工作阐明了试图将分子疏水性分解为组成表面基团疏水性总和的添加剂方法的失败，并证明纳米尺度的疏水性是水合水对化学物质的复杂的、集体的、多体的反应。和地形表面线索。这种复杂性不仅阻碍了对纳米尺度疏水性的基本分子理解，而且还产生了重要的实际后果，例如无法准确筛选用于药物发现的配体。该研究项目的总体目标是进行增强采样分子模拟，以准确地筛选配体。通过空腔形成的自由能量化广泛的纳米结构表面库的疏水性，并部署监督机器学习技术来揭示对控制表面疏水性的物理、化学和地形线索的新理解。该项目的中心假设是以数据为中心的工具的应用可以提供对疏水性分子决定因素的新理解，超出了简单概念模型和人类直觉所能实现的范围。这项工作的第一个目标是通过使用增强采样技术估计界面空腔形成的自由能来量化图案化自组装单层表面和蛋白质的广泛库的疏水性。第二个目标是对疏水性库进行监督学习，以构建将化学成分和物理结构与界面空腔形成的自由能相关的定量结构特性关系模型。将探索许多机器学习技术，包括支持向量机、随机森林、偏最小二乘回归和人工神经网络。这些技术将通过将疏水性物理学“烘焙”到模型中来适应物理感知，并通过提供人类对其行为的可解释的理解来进行解释。这项研究的成功完成将对材料和数据产生影响科学。从材料科学的角度来看，揭示蛋白质疏水性的分子决定因素——蛋白质显示的拓扑和化学模式与空腔形成的自由能之间的关系——将为蛋白质相互作用和组装背后的驱动力提供新的线索，提供规则用于蛋白质结构和功能的合理工程，并开辟虚拟高通量筛选的应用，以计算发现药物、配体、生物分离剂和共溶质以调节蛋白质溶解度。从数据科学的角度来看，这项工作将建立新的物理/化学感知机器学习工具，其行为在分子行为分析中比通用的现成技术更容易解释和理解。材料研究部和化学部该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Learning the relationship between nanoscale chemical patterning and hydrophobicity

了解纳米级化学图案与疏水性之间的关系

• DOI: 10.1073/pnas.2200018119
• 发表时间: 2022-11
• 期刊: Proceedings of the National Academy of Sciences
• 作者: Rego, Nicholas B.;Ferguson, Andrew L.;Patel, Amish J.
• 通讯作者: Patel, Amish J.