Choi Yoonjoo, Verma Deeptak, Griswold Karl E, Bailey-Kellogg Chris
Department of Computer Science, Dartmouth, Hanover, NH, USA.
Thayer School of Engineering, Dartmouth, Hanover, NH, USA.
Methods Mol Biol. 2017;1529:375-398. doi: 10.1007/978-1-4939-6637-0_20.
Therapeutic proteins are yielding ever more advanced and efficacious new drugs, but the biological origins of these highly effective therapeutics render them subject to immune surveillance within the patient's body. When recognized by the immune system as a foreign agent, protein drugs elicit a coordinated response that can manifest a range of clinical complications including rapid drug clearance, loss of functionality and efficacy, delayed infusion-like allergic reactions, more serious anaphylactic shock, and even induced auto-immunity. It is thus often necessary to deimmunize an exogenous protein in order to enable its clinical application; critically, the deimmunization process must also maintain the desired therapeutic activity.To meet the growing need for effective, efficient, and broadly applicable protein deimmunization technologies, we have developed the EpiSweep suite of protein design algorithms. EpiSweep seamlessly integrates computational prediction of immunogenic T cell epitopes with sequence- or structure-based assessment of the impacts of mutations on protein stability and function, in order to select combinations of mutations that make Pareto optimal trade-offs between the competing goals of low immunogenicity and high-level function. The methods are applicable both to the design of individual functionally deimmunized variants as well as the design of combinatorial libraries enriched in functionally deimmunized variants. After validating EpiSweep in a series of retrospective case studies providing comparisons to conventional approaches to T cell epitope deletion, we have experimentally demonstrated it to be highly effective in prospective application to deimmunization of a number of different therapeutic candidates. We conclude that our broadly applicable computational protein design algorithms guide the engineer towards the most promising deimmunized therapeutic candidates, and thereby have the potential to accelerate development of new protein drugs by shortening time frames and improving hit rates.
治疗性蛋白质正在催生出越来越先进、有效的新药,但这些高效治疗药物的生物学来源使其在患者体内会受到免疫监测。当被免疫系统识别为外来物质时,蛋白质药物会引发一系列协同反应,可能表现出一系列临床并发症,包括药物快速清除、功能和疗效丧失、类似输液延迟的过敏反应、更严重的过敏性休克,甚至诱发自身免疫。因此,为了使其能够临床应用,通常有必要对一种外源蛋白质进行去免疫处理;关键的是,去免疫过程还必须保持所需的治疗活性。为了满足对有效、高效且广泛适用的蛋白质去免疫技术日益增长的需求,我们开发了EpiSweep蛋白质设计算法套件。EpiSweep将免疫原性T细胞表位的计算预测与基于序列或结构的突变对蛋白质稳定性和功能影响的评估无缝集成,以便选择在低免疫原性和高水平功能这两个相互竞争的目标之间做出帕累托最优权衡的突变组合。这些方法既适用于设计单个功能去免疫变体,也适用于设计富含功能去免疫变体的组合文库。在一系列回顾性案例研究中对EpiSweep进行验证并与传统的T细胞表位缺失方法进行比较后,我们通过实验证明它在对多种不同治疗候选物进行去免疫的前瞻性应用中非常有效。我们得出结论,我们广泛适用的计算蛋白质设计算法能引导工程师找到最有前景的去免疫治疗候选物,从而有可能通过缩短时间框架和提高命中率来加速新蛋白质药物的开发。