Department of Chemical Engineering, University of California Santa Barbara, California, United States of America.
PLoS One. 2019 Sep 6;14(9):e0217668. doi: 10.1371/journal.pone.0217668. eCollection 2019.
Antibodies are essential to functional immunity, yet the epitopes targeted by antibody repertoires remain largely uncharacterized. To aid in characterization, we developed a generalizable strategy to predict antibody-binding epitopes within individual proteins and entire proteomes. Specifically, we selected antibody-binding peptides for 273 distinct sera out of a random library and identified the peptides using next-generation sequencing. To predict antibody-binding epitopes and the antigens from which these epitopes were derived, we tiled the sequences of candidate antigens into short overlapping subsequences of length k (k-mers). We used the enrichment over background of these k-mers in the antibody-binding peptide dataset to predict antibody-binding epitopes. As a positive control, we used this approach, termed K-mer Tiling of Protein Epitopes (K-TOPE), to predict epitopes targeted by monoclonal and polyclonal antibodies of well-characterized specificity, accurately recovering their known epitopes. K-TOPE characterized a commonly targeted antigen from Rhinovirus A, predicting four epitopes recognized by antibodies present in 87% of sera (n = 250). An analysis of 2,908 proteins from 400 viral taxa that infect humans predicted seven enterovirus epitopes and five Epstein-Barr virus epitopes recognized by >30% of specimens. Analysis of Staphylococcus and Streptococcus proteomes similarly predicted 22 epitopes recognized by >30% of specimens. Twelve of these common viral and bacterial epitopes agreed with previously mapped epitopes with p-values < 0.05. Additionally, we predicted 30 HSV2-specific epitopes that were 100% specific against HSV1 in novel and previously reported antigens. Experimentally validating these candidate epitopes could help identify diagnostic biomarkers, vaccine components, and therapeutic targets. The K-TOPE approach thus provides a powerful new tool to elucidate the organisms, antigens, and epitopes targeted by human antibody repertoires.
抗体对于功能性免疫至关重要,但抗体库所针对的表位在很大程度上仍未得到充分描述。为了辅助进行表位特征分析,我们开发了一种可普遍应用的策略,用于预测单个蛋白质和整个蛋白质组中的抗体结合表位。具体来说,我们从随机文库中选择了 273 种不同血清中的抗体结合肽,并使用下一代测序技术对这些肽进行了鉴定。为了预测抗体结合表位以及这些表位的来源抗原,我们将候选抗原的序列平铺成长度为 k(k-mers)的短重叠子序列。我们使用这些 k-mer 在抗体结合肽数据集中的背景富集程度来预测抗体结合表位。作为阳性对照,我们使用了这种称为蛋白质表位的 k-mer 平铺(K-mer Tiling of Protein Epitopes,K-TOPE)的方法,来预测具有明确特异性的单克隆和多克隆抗体所靶向的表位,准确地回收了它们已知的表位。K-TOPE 对来自 A 型鼻病毒的常见靶抗原进行了特征分析,预测了 4 个被 250 份血清(n = 250)中存在的抗体识别的表位。对来自 400 种感染人类的病毒分类群的 2908 种蛋白质的分析预测了 7 个肠病毒表位和 5 个爱泼斯坦-巴尔病毒表位,这些表位被超过 30%的样本识别。对葡萄球菌和链球菌蛋白质组的分析同样预测了 22 个被超过 30%的样本识别的表位。这 12 个常见的病毒和细菌表位与之前已映射的表位的 p 值 < 0.05 相吻合。此外,我们预测了 30 个 HSV2 特异性表位,这些表位在新型和先前报道的抗原中对 HSV1 具有 100%的特异性。对这些候选表位进行实验验证可能有助于确定诊断生物标志物、疫苗成分和治疗靶标。因此,K-TOPE 方法提供了一种强大的新工具,用于阐明人类抗体库所针对的生物体、抗原和表位。