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

Roberts/Robinson1264329/1264554非天然聚集指的是一组分子过程，通过这些过程，折叠的生物活性蛋白质有效地锁定在错误折叠的？蛋白质的集合或聚集体。这些聚集体的形成是生物技术和生物制药行业成功过度表达、纯化和储存重组蛋白的普遍障碍。这尤其适用于多结构域蛋白质，如天然抗体和设计抗体，以及其他以β为主的蛋白质。这些更复杂的蛋白质的预测设计受到对控制多域蛋白质聚集的序列和结构的关键特征的机械理解的限制。本项目旨在提高对多结构域蛋白质聚集机制(S)的基本理解，从而为将聚集抗性工程到治疗性蛋白质和其他生物技术产品中提供通用的设计规则。这是安进、杜兰大学和特拉华大学通过国家科学基金？S工业-大学目标计划建立的合作伙伴关系。这项工作将集中在计算设计新的抗体分子，保留其已知的功能(例如，配体结合)，同时大幅降低其聚集率，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常工作。从更广泛的角度来看，该项目专注于创造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制药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，并缩短上市时间和医疗应用中与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维持国际竞争力。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的潜在领导者，以及开发新的教育工具，供整个技术界更广泛地使用，并有可能直接用于高中科学课程的演示，如化学、生物化学、生物和物理。

Roberts/Robinson1264329/1264554Non-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.