Functional Deimmunization of Therapeutic Proteins

描述(申请人提供)：治疗性蛋白质的进步代表了临床实践中的一场革命，但蛋白质药物的使用需要考虑它们的免疫原性和在人类患者中引发抗生物治疗性免疫反应(ABIR)的潜力。这类生物免疫反应可表现出一系列并发症，从疗效丧失到危及生命的过敏性休克，减轻免疫原性是生物治疗发展的一个关键方面。虽然影响蛋白质免疫原性的因素很多，但一个关键的特征是来源，即非人类来源的蛋白质具有不成比例的免疫原性。鉴于外源蛋白的巨大治疗潜力，人们考虑了各种去免疫策略。一些方法，如抗体人源化，是非常有效的，但仅限于蛋白质类的狭窄子集。其他方法，如聚乙二醇偶联法，应用广泛，但通常会导致蛋白质功能的大量丧失。现代蛋白质工程已经实现了通过定点突变来删除免疫原性表位的遗传方法，但目前的方法昂贵、时间和劳动力密集，并且显示出有限的成功。相比之下，蛋白质分析的计算工具速度快、效率高，而且越来越准确。在这个提案中，假设可以利用新的优化算法来设计蛋白质变体，在保持高水平功能的同时降低蛋白质的免疫原性。目标1将测试假设，即对于给定的治疗性蛋白质，在降低免疫原性和高水平功能的竞争目标之间存在可预测和可优化的权衡范围。针对两个目标函数(去免疫与功能性)，我们将设计10种工程变种的候选内酰胺酶(P99？L)。将生产P99？L变异体，并对其活性、热稳定性和免疫原性进行检测。目标2将验证这样一个假设，即组合蛋白库可以针对功能治疗候选对象进行计算优化。优化算法将被扩展，以实现去免疫组合蛋白库的设计。将构建五个文库，并用高通量功能试验进行筛选，这些文库具有一系列的去免疫与功能性的相对权重。将定量确定具有高水平功能的图书馆成员的比例，并将根据原始图书馆设计参数对结果进行基准测试。目的3寻求评估和改进免疫原性和功能性模型，这些模型是设计算法的目标函数的基础。一个增强的蛋白质结构模型将被集成到设计算法中，并将构建一个新的P99？L蛋白质组并进行实验评估。这些分析的结果将随后用于更新优化目标和算法，并产生新的变种，从而结束计算和实验之间的循环。成功地实现这些目标将产生广泛适用的算法来设计强大的和免疫耐受的治疗性蛋白质。

DESCRIPTION (provided by applicant): The advance of therapeutic proteins represents a revolution in clinical practice, but use of protein drugs requires consideration of their immunogenicity and potential to elicit an anti-biotherapeutic immune response (aBIR) in human patients. Such aBIRs can manifest a range of complications ranging from loss of efficacy to life-threatening anaphylactic shock, and mitigating immunogenicity is a key aspect of biotherapeutic development. While numerous factors influence protein immunogenicity, one critical feature is the source, i.e. proteins of non-human origin are disproportionately immunogenic. Given the immense therapeutic potential of foreign proteins, a variety of deimmunization strategies have been considered. Some methods, such as antibody humanization, are highly effective but limited to a narrow subset of protein classes. Others, such as conjugation to polyethylene glycol, are widely applicable but typically lead to a substantial loss of protein function. Modern protein engineering has enabled genetic approaches wherein immunogenic epitopes are deleted by site-directed mutagenesis, but current methods are costly, time and labor intensive, and have shown limited success. In contrast, computational tools for protein analysis are fast, efficient, and increasingly accurate. In this proposal, it is hypothesized that novel optimization algorithms can be leveraged to design protein variants that simultaneously reduce protein immunogenicity while maintaining high level functionality. Aim 1 will test the hypothesis that for a given therapeutic protein, there exists a predictable and optimizable spectrum of trade-offs between the competing goals of reduced immunogenicity and high-level functionality. Ten engineered variants of a ß-lactamase therapeutic candidate (P99ßL) will be designed with a range of weights on the two objective functions: deimmunization vs. functionality. The P99ßL variants will be produced and assayed for activity, thermostability and immunogenicity. Aim 2 will test the hypothesis that combinatorial protein libraries can be computationally optimized for functional therapeutic candidates. The optimization algorithms will be extended to enable design of deimmunized combinatorial protein libraries. Five libraries, having a range of relative weights for deimmunization vs. functionality, will be constructed and screened with a high throughput functional assay. The proportion of library members exhibiting high level functionality will be quantitatively determined, and the results will be benchmarked against the original library design parameters. Aim 3 seeks to evaluate and refine the models of immunogenicity and functionality that underlie the objective functions of the design algorithm. An enhanced model of protein structure will be integrated into the design algorithm, and a new panel of P99ßL proteins will be constructed and experimentally evaluated. The results of these analyses will subsequently be used to update the optimization objectives and algorithm, and produce new variants, thereby closing the loop between computation and experiment. Successfully achieving these aims will yield broadly applicable algorithms for engineering powerful and immune-tolerant therapeutic proteins.