Interpreting Negative Data on Antipeptide Paratope Binding to Support Development of B-Cell Epitope Prediction for Vaccine Design and Other Translational Applications

S. Caoili
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Abstract

The design of synthetic vaccine peptides and other constructs (e.g., for developing immunodiagnostics) is informed by B-cell epitope prediction for antipeptide paratopes, which crucially depends on physicochemically and biologically meaningful interpretation of pertinent experimental data as regards paratope-epitope binding, with negative data being particularly problematic as they may be due to artefacts of immunization and immunoassays. Yet, the problem posed by negative data remains to be comprehensively addressed in a manner that clearly defines their role in the further development of B-cell epitope prediction. Hence, published negative data were surveyed and analyzed herein to identify key issues impacting on B-cell epitope prediction. Data were retrieved via searches using the Immune Epitope Database (IEDB) and review of underlying primary sources in literature to identify said issues, which include (1) inherent tendency toward false-negative data with use of solid-phase immunoassays and/or monoclonal paratopes, (2) equivocal data (i.e., both positive and negative data obtained from similar experiments), and (3) failure of antipeptide paratopes to cross-react with antigens of covalent structure and/or conformation different from that of the peptide immunogens despite apparent identity between curated epitope sequences. Analysis of experimental details thus focused on negative data from fluid-phase (e.g., immunoprecipitation) assays for detection of polyclonal paratope-epitope binding. Underlying literature references were reviewed to confirm the identification of negative data included for analysis. Furthermore, data from assays to detect cross-reaction of antipeptide antibody with protein antigen were included only if supported by positive data on either the corresponding reaction of the same antibody with peptide antigen or cross-reaction of said antibody with denatured protein antigen, to exclude the possibility that negative data on cross-reaction were due to absence of antipeptide paratopes in the first place (e.g., because of failed immunization due to insufficient immunogenicity and/or immune tolerance). Among currently available negative binding data on antipeptide antibodies, very few are on polyclonal responses yet also clearly attributable to conformational differences between peptide immunogens and native cognate proteins thereof. This dearth of negative data suitable for benchmarking B-cell epitope prediction conceivably could be addressed by generating positive data on binding of polyclonal antipeptide antibodies to cognate-protein sequences (e.g., in solid-phase immunoassays using unfolded protein antigen) to complement negative data on failure of the same antibodies to cross-react with native protein (e.g., in fluid-phase immunoassays, without artefactual covalent modification of antigens that tends to produce false-negative results). As regards cross-reactive binding of native cognate proteins by antipeptide antibodies (e.g., as mechanistic basis for novel vaccines and immunotherapeutics), negative data are most informative where attributable to conformational differences between peptide immunogens and target proteins. This is favored by careful peptide-immunogen design (e.g., avoiding covalent backbone and sidechain differences vis-a-vis target protein sequence) and positive data on antibody binding of the target protein sequence (e.g., in unfolded protein) paired with negative data on the same antibody using native protein antigen (e.g., from fluid- rather than solid-phase assays).
解释抗肽副表位结合的阴性数据以支持疫苗设计和其他转化应用的b细胞表位预测的发展
合成疫苗多肽和其他结构的设计(例如,用于开发免疫诊断)是根据b细胞抗原旁位的表位预测来进行的,这在很大程度上取决于对有关副位-表位结合的相关实验数据的物理化学和生物学意义的解释,阴性数据尤其成问题,因为它们可能是由于免疫和免疫分析的人工产物。然而,阴性数据带来的问题仍然需要以一种明确定义它们在b细胞表位预测的进一步发展中的作用的方式来全面解决。因此,本文对已发表的负面数据进行了调查和分析,以确定影响b细胞表位预测的关键问题。通过使用免疫表位数据库(IEDB)检索数据,并回顾文献中潜在的主要来源,以确定所述问题,其中包括:(1)使用固相免疫测定和/或单克隆异位时固有的假阴性数据倾向,(2)模棱两可的数据(即从类似实验中获得的阳性和阴性数据),(3)尽管抗原表位序列与肽免疫原的共价结构和/或构象不同,但抗肽旁位不能与抗原发生交叉反应。因此,对实验细节的分析主要集中在液相(例如免疫沉淀)检测多克隆副表位结合的阴性数据上。我们回顾了相关文献,以确认纳入分析的负面数据。此外,检测抗多肽抗体与蛋白抗原交叉反应的数据只有在同一抗体与肽抗原对应反应或该抗体与变性蛋白抗原交叉反应的阳性数据支持的情况下才被纳入,以排除交叉反应阴性数据最初是由于缺乏抗多肽的可能性(例如,由于免疫原性不足和/或免疫耐受而导致免疫失败。在目前可用的抗多肽抗体的负结合数据中,很少有多克隆反应,但也清楚地归因于肽免疫原与其天然同源蛋白之间的构象差异。可以想象,这种阴性数据的缺乏可以通过产生多克隆抗肽抗体与同源蛋白序列结合的阳性数据(例如,在使用未折叠蛋白抗原的固相免疫测定中)来弥补相同抗体与天然蛋白交叉反应失败的阴性数据(例如,在液相免疫测定中)来解决。没有人为的抗原共价修饰,往往产生假阴性结果)。关于抗多肽抗体与天然同源蛋白的交叉反应性结合(例如,作为新型疫苗和免疫疗法的机制基础),由于多肽免疫原和靶蛋白之间的构象差异,阴性数据提供的信息量最大。这得益于仔细的肽免疫原设计(例如,避免与靶蛋白序列相对的共价主链和侧链差异)和靶蛋白序列抗体结合的阳性数据(例如,未折叠的蛋白质)与使用天然蛋白抗原的同一抗体的阴性数据(例如,来自流体而不是固相测定)配对。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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