Functional Deimmunization of Therapeutic Proteins

描述（由申请人提供）：治疗性蛋白质的进步代表了临床实践中的一场革命，但使用蛋白质药物需要考虑其免疫原性和在人类患者中引发抗生物治疗免疫反应（aBIR）的潜力。这类abir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。虽然有许多因素影响蛋白质的免疫原性，但一个关键特征是来源，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，各种脱免疫策略已被考虑。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一小部分。其他的，如偶联聚乙二醇，是广泛适用的，但通常会导致蛋白质功能的大量损失。现代蛋白质工程使基因方法成为可能，其中免疫原性表位通过位点定向诱变被删除，但目前的方法是昂贵的，时间和劳动密集型的，并且已经显示出有限的成功。相比之下，用于蛋白质分析的计算工具快速、高效，而且越来越准确。在本提案中，假设可以利用新的优化算法来设计蛋白质变体，同时降低蛋白质的免疫原性，同时保持高水平的功能。目的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谱。将设计10种ß-内酰胺酶候选治疗药物（P99ßL）的工程变体，在两个目标函数上具有一定的权重：去免疫与功能。将生产P99ßL变体并检测其活性、热稳定性和免疫原性。目的2将检验组合蛋白文库可以通过计算优化功能治疗候选物的假设。优化算法将被扩展到能够设计去免疫组合蛋白库。将构建5个具有去免疫与功能性相对权重范围的文库，并使用高通量功能测定法进行筛选。将定量地确定展示高级功能的图书馆成员的比例，并根据原始图书馆设计参数对结果进行基准测试。目标3旨在评估和完善免疫原性和功能模型，这些模型是设计算法目标函数的基础。将蛋白质结构的增强模型整合到设计算法中，构建新的P99ßL蛋白面板并进行实验评估。这些分析的结果随后将用于更新优化目标和算法，并产生新的变体，从而关闭计算和实验之间的循环。成功实现这些目标将产生广泛适用的算法，用于工程强大和免疫耐受治疗蛋白。

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.