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

几十年来，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。一种有希望的方法是将酶与合成聚合物相结合。将酶连接到合成材料上已被证明可以提高它们的可回收性。这种方法也被证明可以减少极端环境中的酶变性。然而，人们对某些材料为什么能稳定一些酶而不能稳定其他酶的原因知之甚少。总的目标是了解和开发如何通过将酶固定到复杂的合成材料上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。项目结果将提供给年度数据科学顶峰项目。蛋白质的稳定性可以通过调节蛋白质附着的随机共聚物刷子的组成来调节。对这种方法的分子基础的详细理解是至关重要的。这一理解将通过结合功能稳定性测量、量化构象动力学的单分子方法(例如，展开和重新折叠速率)和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有很好的相关性。为了系统地验证这一假设，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。通过调整酶刷子界面来评估机制，将包括增强的重新折叠(即，类似伴侣的效应)和减少展开。此外，还将确定有助于酶稳定的刷层的显著化学特征。这项工作将利用一种新的算法来建模和识别蛋白质表面的疏水原子簇，使用无监督机器学习。这项工作的结果有望导致工业生物催化的变革性进展。这一奖项反映了NSF的法定使命，通过使用基金会的智力优势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.