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

稳定酶以改善其在工业过程中的性能的方法已经追求了几十年。一种很有前途的方法是将酶与合成聚合物结合起来。将酶附着在合成材料上已被证明可以提高其可回收性。这种方法也被证明可以减少极端环境中的酶变性。然而，很少有人知道为什么某些材料稳定某些酶，而不是其他酶。总体目标是了解和开发关于如何通过固定化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。 项目结果将被纳入年度数据科学顶点项目。蛋白质稳定性可以通过调整蛋白质附着的无规共聚物刷的组成来调节。详细了解这种方法的分子基础是至关重要的。这种理解将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，展开和再折叠速率）和原子分子动力学模拟。使用这种方法，假设刷层和酶表面的化学性质应该是很好的相关性。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Förster共振能量转移和模拟将用于区分可能的稳定机制。通过调节酶-刷界面来评估的机制将包括增强的重折叠（即，类伴侣效应）和减少的解折叠。此外，刷层的显着的化学特征，有助于酶的稳定将被确定。这项工作将利用一种新型算法，使用无监督机器学习来建模和识别蛋白质表面上的疏水性原子簇。这项工作的结果预计将导致工业生物催化的变革性进展。该奖项反映了NSF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。

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.