Collaborative Research: Mechanisms of Catalytic Enhancement of Immobilized Lipases by Tunable Polymer Materials

合作研究：可调高分子材料增强固定化脂肪酶的催化机制

• 批准号: 2103613
• 负责人: Jim Pfaendtner
• 金额: $ 32.84万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103613&HistoricalAwards=false
• 关键词: Collaborative; Research; Mechanisms; Catalytic; Enhancement

Methods to stabilize enzymes to improve their performance in industrial processes have been pursued for decades. A promising approach combines enzymes with synthetic polymers. Attaching enzymes to synthetic materials has been shown to enhance their recyclability. This approach has also been shown to decrease enzyme denaturation in extreme environments. However, little is understood about why certain materials stabilize some enzymes but not others. The overall goal is to understand and develop design rules on how to stabilize enzymes via immobilization to complex synthetic materials. This project will also provide multi-disciplinary training for graduate, undergraduate, and high school students. Project results will feed into an annual data science capstone project.Protein stabilization can be regulated by tuning the composition of random copolymer brushes to which the protein is attached. A detailed understanding of the molecular basis of this approach is critical. This understanding will be achieved by combining functional stability measurements, single-molecule methods to quantify conformational dynamics (e.g., unfolding and re-folding rates), and atomistic molecular dynamics simulations. Using this approach, the hypothesis that the chemical properties of the brush layer and enzyme surface should be well-correlated. To systematically test this hypothesis, several closely related, but structurally diverse lipases will be used. Single-molecule Förster resonance energy transfer and simulations will be used to distinguish between possible mechanisms of stabilization. Mechanisms to be evaluated via tuning the enzyme-brush interface, will include enhanced re-folding (i.e., a chaperone-like effect) and reduced unfolding. Additionally, the salient chemical features of the brush layer that contribute to the stabilization of enzymes will be identified. This work will leverage a novel algorithm to model and identify clusters of hydrophobic atoms on protein surfaces using unsupervised machine learning. The results of this work are expected to lead to transformational advances in industrial biocatalysis. The impact may extend to other fields, including biosensing, bioremediation, and smart materials.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.

数十年来，已经采用了稳定酶以提高工业过程中的绩效的方法。诺言方法将酶与合成聚合物结合在一起。已显示将酶附加到合成材料上，以增强其可回收性。该方法还被证明可以减少极端环境中的酶变性。但是，关于某些材料为何稳定某些酶而不是其他材料的原因很少。总体目标是了解和制定有关如何通过固定在复杂合成材料中稳定酶的设计规则。该项目还将为研究生，本科和高中生提供多学科培训。项目结果将进食年度数据科学底石项目。可以通过调节附着蛋白的随机共聚物刷的组成来调节蛋白质稳定。对这种方法的分子基础的详细理解至关重要。通过结合功能稳定性测量，单分子方法来量化构象动力学（例如，展开和重新折叠速率）以及原子分子动力学模拟来实现这种理解。使用这种方法，应良好相关的刷层和酶表面的化学特性的假设。为了系统地检验这一假设，将使用几种密切相关的，但结构上不同的脂肪酶将被使用。单分子Förster共振能量传递和模拟将用于区分可能的稳定机制。通过调整酶 - 刷子界面进行评估的机制将包括增强的重新折叠（即类似伴侣的效应）和减少的展开。此外，将鉴定出有助于酶稳定的刷层的显着化学特征。这项工作将利用一种新颖的算法来建模并使用无监督的机器学习在蛋白质表面上识别疏水原子簇。这项工作的结果有望导致工业生物催化的变革性进步。这种影响可能会扩展到其他领域，包括生物传感，生物修复和智能材料。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被视为通过评估而被视为珍贵的支持。