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.

酶是一种基于蛋白质的生物催化剂，具有巨大的潜力，可以彻底改变我们将化学物质转化为有用产品、治疗疾病或消除环境污染物的方式。 然而，酶在实践中的使用受到其通过各种机制失去活性的倾向的限制。一种提高酶鲁棒性的常用策略是将其包裹并封装在聚合物中，聚合物是由许多重复亚基组成的大分子。这种策略有很大的希望，但聚合物的性质，封装技术，和酶/聚合物的相互作用都发挥重要作用，在确定一个特定的封装策略的成功。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，以帮助开发预测有益封装策略的有效方法。研究人员通过UW工程数学学院与学生合作，为丰富研究生的培训，改善教育，并让当地社区学院（贝尔维尤学院）的本科生参与研究提供了许多机会。迄今为止，成功的酶解主要是通过大量的试验和错误实验和偶然发现发现的。该项目结合了分子尺度模拟和各种基质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/聚合物界面的基本控制驱动力。具体而言，该项目正在展示一种综合策略，使用快速分子动力学模拟与统计机器学习相结合，以识别封装在聚合物纳米颗粒中的酶的强结合和弱结合的序列水平描述符。正在进行补充实验，研究在各种条件下的酶加载，释放和活性。合理设计这种相互作用的能力将是一个变革性的进步，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的准确性，并可能有助于确定分子动力学力场和高通量实验设计的改进领域。