Dhanda Sandeep Kumar, Grifoni Alba, Pham John, Vaughan Kerrie, Sidney John, Peters Bjoern, Sette Alessandro
Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA.
Immunology. 2018 Jan;153(1):118-132. doi: 10.1111/imm.12816. Epub 2017 Sep 28.
Unwanted immune responses against protein therapeutics can reduce efficacy or lead to adverse reactions. T-cell responses are key in the development of such responses, and are directed against immunodominant regions within the protein sequence, often associated with binding to several allelic variants of HLA class II molecules (promiscuous binders). Herein, we report a novel computational strategy to predict 'de-immunized' peptides, based on previous studies of erythropoietin protein immunogenicity. This algorithm (or method) first predicts promiscuous binding regions within the target protein sequence and then identifies residue substitutions predicted to reduce HLA binding. Further, this method anticipates the effect of any given substitution on flanking peptides, thereby circumventing the creation of nascent HLA-binding regions. As a proof-of-principle, the algorithm was applied to Vatreptacog α, an engineered Factor VII molecule associated with unintended immunogenicity. The algorithm correctly predicted the two immunogenic peptides containing the engineered residues. As a further validation, we selected and evaluated the immunogenicity of seven substitutions predicted to simultaneously reduce HLA binding for both peptides, five control substitutions with no predicted reduction in HLA-binding capacity, and additional flanking region controls. In vitro immunogenicity was detected in 21·4% of the cultures of peptides predicted to have reduced HLA binding and 11·4% of the flanking regions, compared with 46% for the cultures of the peptides predicted to be immunogenic. This method has been implemented as an interactive application, freely available online at http://tools.iedb.org/deimmunization/.
针对蛋白质治疗药物的不良免疫反应会降低疗效或导致不良反应。T细胞反应在此类反应的发生过程中起关键作用，且针对蛋白质序列中的免疫显性区域，这些区域通常与HLA II类分子的几种等位基因变体结合（多反应性结合物）。在此，我们基于先前对促红细胞生成素蛋白免疫原性的研究，报告一种预测“去免疫”肽段的新型计算策略。该算法（或方法）首先预测目标蛋白质序列中的多反应性结合区域，然后识别预计可降低HLA结合的残基替换。此外，此方法还能预测任何给定替换对侧翼肽段的影响，从而避免产生新的HLA结合区域。作为原理验证，该算法应用于Vatreptacog α，这是一种与意外免疫原性相关的工程化因子VII分子。该算法正确预测了包含工程化残基的两个免疫原性肽段。作为进一步验证，我们选择并评估了预计可同时降低两个肽段HLA结合的七个替换、五个预计不会降低HLA结合能力的对照替换以及额外的侧翼区域对照的免疫原性。与预计具有免疫原性的肽段培养物中46%的比例相比，预计HLA结合降低的肽段培养物中有21.4%以及侧翼区域中有11.4%检测到体外免疫原性。此方法已实现为交互式应用程序，可在http://tools.iedb.org/deimmunization/免费在线获取。
