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使用全蛋白质组预测抗体库靶向的蛋白质表位的一般方法。

A general approach for predicting protein epitopes targeted by antibody repertoires using whole proteomes.

机构信息

Department of Chemical Engineering, University of California Santa Barbara, California, United States of America.

出版信息

PLoS One. 2019 Sep 6;14(9):e0217668. doi: 10.1371/journal.pone.0217668. eCollection 2019.

Abstract

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.

摘要

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。

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