McIlwain Sean J, Hoefges Anna, Erbe Amy K, Sondel Paul M, Ong Irene M
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53705, USA.
University of Wisconsin Carbone Comprehensive Cancer Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.
Bioinformatics. 2024 Nov 28;40(12). doi: 10.1093/bioinformatics/btae637.
Ultradense peptide binding arrays that can probe millions of linear peptides comprising the entire proteomes of human or mouse, or hundreds of thousands of microbes, are powerful tools for studying the antibody repertoire in serum samples to understand adaptive immune responses.
There are few tools for exploring high-dimensional, significant and reproducible antibody targets for ultradense peptide binding arrays at the linear peptide, epitope (grouping of adjacent peptides), and protein level across multiple samples/subjects (i.e. epitope spread or immunogenic regions of proteins) for understanding the heterogeneity of immune responses.
We developed Hierarchical antibody binding Epitopes and pROteins from liNear peptides (HERON), an R package, which can identify immunogenic epitopes, using meta-analyses and spatial clustering techniques to explore antibody targets at various resolution and confidence levels, that can be found consistently across a specified number of samples through the entire proteome to study antibody responses for diagnostics or treatment. Our approach estimates significance values at the linear peptide (probe), epitope, and protein level to identify top candidates for validation. We tested the performance of predictions on all three levels using correlation between technical replicates and comparison of epitope calls on two datasets, and results showed HERON's competitiveness in estimating false discovery rates and finding general and sample-level regions of interest for antibody binding.
The HERON R package is available at Bioconductor https://bioconductor.org/packages/release/bioc/html/HERON.html.
超密集肽结合阵列能够探测数百万种包含人类或小鼠全部蛋白质组的线性肽,或数十万种微生物的线性肽,是研究血清样本中抗体库以了解适应性免疫反应的强大工具。
目前几乎没有工具可用于在多个样本/受试者中,在线性肽、表位(相邻肽的组合)和蛋白质水平上探索超密集肽结合阵列的高维、显著且可重复的抗体靶点,以了解免疫反应的异质性(即蛋白质的表位扩散或免疫原性区域)。
我们开发了一种名为“线性肽的分层抗体结合表位和蛋白质”(HERON)的R包,它可以利用荟萃分析和空间聚类技术,在各种分辨率和置信水平下识别免疫原性表位,通过整个蛋白质组在指定数量的样本中一致地找到这些表位,从而研究用于诊断或治疗的抗体反应。我们的方法在线性肽(探针)、表位和蛋白质水平上估计显著性值,以识别用于验证的顶级候选物。我们使用技术重复之间的相关性以及两个数据集上表位调用的比较,测试了所有三个水平上预测的性能,结果表明HERON在估计错误发现率以及找到抗体结合的一般和样本水平的感兴趣区域方面具有竞争力。
HERON R包可在Bioconductor上获取,网址为https://bioconductor.org/packages/release/bioc/html/HERON.html。