Combined molecular simulation and experimental study to discover, predict and control enzyme immobilization in polymeric nanoparticles

结合分子模拟和实验研究来发现、预测和控制聚合物纳米粒子中的酶固定

• 批准号: 1703438
• 负责人: Jim Pfaendtner
• 金额: $ 33.13万
• 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: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1703438&HistoricalAwards=false
• 关键词: Combined; molecular; simulation; experimental; study

Enzymes, protein-based biological catalysts, have enormous potential to revolutionize the way we transform chemicals to useful products, treat disease, or detoxify environmental contaminants. However, the use of enzymes in practice is limited by their tendency to lose their activity through a variety of mechanisms. One common strategy to improve enzyme robustness is to immobilize and encapsulate it in a polymer, a large molecule composed of many repeated subunits. This strategy holds great promise, but the polymer properties, encapsulation technique, and enzyme/polymer interactions all play important roles in determining the success of a particular encapsulation strategy. This research project aims to compare and contrast computer simulations with experimental results to help develop an efficient means of predicting beneficial encapsulation strategies. Working with students through the UW College of Engineering Math Academy, the researchers are providing many opportunities for enriching the training of graduate students, improving education, and engaging undergraduates from a local community college (Bellevue College) in research. To date, successful enzyme encapsulations have been discovered largely via extensive trial and error experimentation and serendipity. This project, a combined study of molecular scale simulations and experimental measurement of enzyme activity and release in various matrices, seeks to build a rational design framework to discover, predict, and control the essential governing driving forces at the enzyme/polymer interface. Specifically, this project is demonstrating a comprehensive strategy, using fast molecular dynamics simulations paired with statistical machine learning, to identify sequence level descriptors of strong and weak binding of enzymes encapsulated in polymer nanoparticles. Complementary experiments are being performed that study enzyme loading, release and activity under a wide range of conditions. The ability to rationally design such interactions would be a transformative advance that could be applied to hydrogels, inorganic surfaces, or other types of enzyme/polymer systems. The strong interplay between the experiments and simulations will ensure that the results are accurate and potentially help to identify areas of improvement for molecular dynamics force fields and high throughput experimental design.

酶，基于蛋白质的生物催化剂，具有巨大的潜力，可以改变我们将化学物质转化为有用的产品，治疗疾病或排毒环境污染物的方式。 但是，实践中酶的使用受到通过多种机制失去活性的趋势的限制。提高酶鲁棒性的一种常见策略是将其固定并封装在聚合物中，这是由许多重复亚基组成的大分子。该策略具有巨大的希望，但是聚合物性能，封装技术和酶/聚合物相互作用都在确定特定封装策略的成功方面起着重要作用。该研究项目旨在将计算机模拟与实验结果进行比较和对比，以帮助开发一种预测有益封装策略的有效方法。研究人员通过大学工程学院数学学院与学生合作，为丰富研究生的培训，改善教育并吸引本地社区学院（Bellevue College）的大学生提供了许多机会。迄今为止，成功的酶封装已在很大程度上通过广泛的试验和误差实验和偶然性发现。该项目是对分子尺度模拟的组合研究以及对各种矩阵中酶活性和释放的实验测量，旨在建立一个合理的设计框架，以发现，预测和控制酶/聚合物界面上必不可少的驱动力。具体而言，该项目使用与统计机器学习配对的快速分子动力学模拟展示了一项综合策略，以识别封装在聚合物纳米颗粒中的酶的强和弱结合的序列水平描述符。正在进行互补实验，以研究酶的负载，释放和活性在广泛的条件下。合理设计这种相互作用的能力将是一种变革性的进步，可以应用于水凝胶，无机表面或其他类型的酶/聚合物系统。实验和模拟之间的强烈相互作用将确保结果准确，并有助于确定分子动力学力场和高吞吐量实验设计的改进领域。