GOALI: Collaborative Proposal: Mechanistic Design of Aggregation Resistance in Multi-Domain Proteins

非天然聚集是指一组分子过程，通过这些过程，折叠的、具有生物活性的蛋白质被有效地锁定在一个错误折叠的蛋白质中。蛋白质的组装或聚集在整个生物技术和生物制药行业中，这些聚集体的形成是成功过表达、纯化和储存重组蛋白的普遍障碍。对于多结构域蛋白，如天然抗体和设计抗体，以及其他主要的β蛋白，情况尤其如此。由于对控制多结构域蛋白聚集的序列和结构的关键特征的机制理解的限制，这些更复杂蛋白质的预测设计受到阻碍。本项目旨在提高对多结构域蛋白质聚集机制的基本理解，从而为治疗性蛋白质和其他生物技术产品的聚集抗性工程提供一般设计规则。这是安进、杜兰大学和特拉华大学通过国家科学基金会的合作？我们的产学研GOALI项目。这项工作将集中在新抗体分子的计算设计上，这些分子保留了它们已知的功能（例如，配体结合），同时大大降低了它们的聚集率，然后通过实验表征聚集途径，以确保设计预测在分子水平上正确工作。从更广泛的角度来看，该项目侧重于创建新的实验和计算工具，以更好地预测和控制甚至消除蛋白质药物和其他生物技术产品中的聚集体。这样做将降低工业科学家和工程师的开发成本，也减少了上市时间和与医疗应用中蛋白质聚集体相关的任何安全风险。生物制药行业是美国就业市场增长最快、最具吸引力的行业之一，需要训练有素的劳动力来保持国际竞争力。该项目的一个间接但同样重要的成果是培训下一代工业科学家和该行业潜在的领导者，以及开发新的教育工具，以便在整个技术社区更广泛地使用，并可能直接用于高中科学课程的演示，如化学，生物化学，生物学和物理学。

Roberts/Robinson1264329/1264554 Non-native aggregation denotes a set of molecular processes by which a folded, biologically active protein becomes effectively locked into a ?misfolded? assembly or aggregate of proteins. Formation of these aggregates is a ubiquitous hurdle to successful over-expression, purification, and storage of recombinant proteins throughout the biotechnology and biopharmaceutical industries. This is particularly the case for multi-domain proteins, such as natural and designed antibodies, as well as for other predominantly-beta proteins. Predictive design of these more complex proteins is handicapped by limitations in the mechanistic understanding of key features of sequence and structure that control aggregation of multi-domain proteins. This project seeks to improve fundamental understanding of the mechanism(s) of multi-domain protein aggregation, so as to provide general design rules to engineer aggregation resistance into therapeutic proteins and other biotechnology products. This is a partnership between Amgen, Tulane University, and the University of Delaware through the National Science Foundation?s industry-university GOALI program. The work will focus on computational design of new antibody molecules that retain their known function (e.g., ligand binding) while greatly reducing their aggregation rates, and then experimentally characterizing the aggregation pathways to assure that the design predictions worked properly at a molecular level. From a broader perspective, the project focuses on creating new experimental and computational tools to better predict and control or even eliminate aggregates in protein pharmaceuticals and other biotechnology products. Doing so will decrease the cost of development for industrial scientists and engineers, and also decrease the time to market and any safety risks associated with protein aggregates in medical applications. The biopharmaceutical industry is one the fastest growing and attractive industries for growth of the U.S. job market requiring a highly trained workforce that will maintain international competitiveness. An indirect but equally important outcome of this project is the training of the next generation of industrial scientists and potential leaders for the industry, as well as development of new educational tools for more broad use throughout the technical community, and potentially for direct use in demonstrations in high school science courses such as chemistry, biochemistry, biology, and physics.