Biomedical Innovations Research for Translational Health Science (BIRTHS) Laboratory, Department of Biochemistry and Molecular Biology, College of Medicine, University of the Philippines Manila, Manila, Philippines.
Protein Pept Lett. 2021;28(8):953-962. doi: 10.2174/0929866528666210218215624.
B-cell epitope prediction is a computational approach originally developed to support the design of peptide-based vaccines for inducing protective antibody-mediated immunity, as exemplified by neutralization of biological activity (e.g., pathogen infectivity). Said approach is benchmarked against experimentally obtained data on paratope-epitope binding; but such data are curated primarily on the basis of immune-complex structure, obscuring the role of antigen conformational disorder in the underlying immune recognition process.
This work aimed to critically analyze the curation of epitope-paratope binding data that are relevant to B-cell epitope prediction for peptide-based vaccine design.
Database records on neutralizing monoclonal antipeptide antibody immune-complex structure were retrieved from the Immune Epitope Database (IEDB) and analyzed in relation to other data from both IEDB and external sources including the Protein Data Bank (PDB) and published literature, with special attention to data on conformational disorder among paratope-bound and unbound peptidic antigens.
Data analysis revealed key examples of antipeptide antibodies that recognize conformationally disordered B-cell epitopes and thereby neutralize the biological activity of cognate targets (e.g., proteins and pathogens), with inconsistency noted in the mapping of some epitopes due to reliance on immune-complex structural details, which vary even among experiments utilizing the same paratope-epitope combination (e.g., with the epitope forming part of a peptide or a protein).
The results suggest an alternative approach to curating paratope-epitope binding data based on neutralization of biological activity by polyclonal antipeptide antibodies, with reference to immunogenic peptide sequences and their conformational disorder in unbound antigen structures.
B 细胞表位预测是一种计算方法，最初是为了支持基于肽的疫苗的设计而开发的，该疫苗能够诱导保护性抗体介导的免疫，例如中和生物活性（例如病原体感染性）。该方法是针对针对抗体结合表位的实验获得的数据进行基准测试的；但是，此类数据主要是根据免疫复合物结构进行整理的，掩盖了抗原构象无序在潜在免疫识别过程中的作用。
这项工作旨在批判性地分析与基于肽的疫苗设计的 B 细胞表位预测相关的表位-抗体结合数据的整理。
从免疫表位数据库（IEDB）中检索了中和单克隆抗肽抗体免疫复合物结构的数据库记录，并与来自 IEDB 和外部来源（包括蛋白质数据库（PDB）和已发表的文献）的其他数据进行了分析，特别注意与结合和未结合的肽抗原的抗体结合表位有关的构象无序数据。
数据分析揭示了识别构象无序 B 细胞表位并中和同源靶标（例如蛋白质和病原体）的生物活性的抗肽抗体的关键示例，由于依赖于免疫复合物结构细节，一些表位的映射存在不一致性，即使在利用相同表位-表位组合的实验中（例如，表位是肽或蛋白质的一部分）也是如此。
结果表明，基于多克隆抗肽抗体中和生物活性的方法，参考免疫原性肽序列及其在未结合抗原结构中的构象无序性，为表位-抗体结合数据的整理提供了一种替代方法。
