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.

数十年来人们一直在寻求稳定酶以提高其在工业过程中的性能的方法。一种有前景的方法是将酶与合成聚合物结合起来。事实证明，将酶附着在合成材料上可以提高其可回收性。这种方法还被证明可以减少极端环境中酶的变性。然而，人们对为什么某些材料可以稳定某些酶而不稳定其他酶的原因知之甚少。总体目标是了解和开发如何通过固定到复杂合成材料来稳定酶的设计规则。该项目还将为研究生、本科生和高中生提供多学科培训。项目结果将纳入年度数据科学顶点项目。可以通过调整蛋白质附着的随机共聚物刷的成分来调节蛋白质稳定性。详细了解这种方法的分子基础至关重要。这种理解将通过结合功能稳定性测量、量化构象动力学（例如，展开和重折叠速率）的单分子方法以及原子分子动力学模拟来实现。使用这种方法，假设刷层和酶表面的化学性质应该是良好相关的。为了系统地检验这一假设，将使用几种密切相关但结构不同的脂肪酶。单分子福斯特共振能量转移和模拟将用于区分可能的稳定机制。通过调整酶刷界面来评估的机制将包括增强的重折叠（即类似伴侣的效应）和减少的展开。此外，还将确定刷层有助于酶稳定的显着化学特征。这项工作将利用一种新颖的算法，利用无监督的机器学习来建模和识别蛋白质表面上的疏水原子簇。这项工作的结果预计将带来工业生物催化领域的变革性进展。该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。