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Abstract：
The in silico prediction of T cell epitopes within any peptide or biologic drug candidate serves as an important first step for assessing immunogenicity. T cell epitopes bind human leukocyte antigen (HLA) by a well-characterized interaction of amino acid side chains and pockets in the HLA molecule binding groove. Immunoinformatics tools, such as the EpiMatrix algorithm, have been developed to screen natural amino acid sequences for peptides that will bind HLA. In addition to commonly occurring in synthetic peptide impurities, unnatural amino acids (UAA) are also often incorporated into novel peptide therapeutics to improve properties of the drug product. To date, the HLA binding properties of peptides containing UAA are not accurately estimated by most algorithms. Both scenarios warrant the need for enhanced predictive tools. The authors developed an in silico method for modeling the impact of a given UAA on a peptide\'s likelihood of binding to HLA and, by extension, its immunogenic potential. In silico assessment of immunogenic potential allows for risk-based selection of best candidate peptides in further confirmatory in vitro, ex vivo and in vivo assays, thereby reducing the overall cost of immunogenicity evaluation. Examples demonstrating in silico immunogenicity prediction for product impurities that are commonly found in formulations of the generic peptides teriparatide and semaglutide are provided. Next, this article discusses how HLA binding studies can be used to estimate the binding potentials of commonly encountered UAA and \"correct\" in silico estimates of binding based on their naturally occurring counterparts. As demonstrated here, these in vitro binding studies are usually performed with known ligands which have been modified to contain UAA in HLA anchor positions. An example using D-amino acids in relative binding position 1 (P1) of the PADRE peptide is presented. As more HLA binding data become available, new predictive models allowing for the direct estimation of HLA binding for peptides containing UAA can be established.
摘要：
任何肽或生物药物候选物中的T细胞表位的计算机模拟预测作为评估免疫原性的重要的第一步。T细胞表位通过氨基酸侧链和HLA分子结合槽中的口袋的充分表征的相互作用结合人白细胞抗原(HLA)。免疫信息学工具，例如EpiMatrix算法，已经开发用于筛选将结合HLA的肽的天然氨基酸序列。除了常见的合成肽杂质，通常还将非天然氨基酸(UAA)掺入新型肽治疗剂中以改善药物产品的性质。迄今为止,大多数算法无法准确估计含UAA的肽的HLA结合特性。这两种情况都需要增强的预测工具。作者开发了一种计算机模拟方法，用于模拟给定UAA对肽与HLA结合的可能性的影响，通过延伸,它的免疫原性潜力。对免疫原性潜力的计算机评估允许在进一步的体外验证中基于风险选择最佳候选肽。离体和体内测定，从而降低免疫原性评价的总成本。提供了证明在通用肽特立帕肽和司马鲁肽的制剂中常见的产品杂质的计算机免疫原性预测的实例。接下来,本文讨论了如何使用HLA结合研究来估计常见UAA的结合潜能,并根据其天然存在的对应物对结合进行"正确的计算机模拟估计.正如这里所证明的，这些体外结合研究通常用已知的配体进行,所述配体已经被修饰以在HLA锚定位置含有UAA。提供了在PADRE肽的相对结合位置1(P1)中使用D-氨基酸的实例。随着更多HLA绑定数据可用，可以建立新的预测模型,该模型允许直接估计含有UAA的肽的HLA结合。
