RNA Bioinformatics /High Throughput Analysis, Faculty of Mathematics and Computer Science.
Bioinformatics Core Facility Jena, Friedrich Schiller University Jena, Jena 07743, Germany.
Bioinformatics. 2021 May 1;37(4):448-455. doi: 10.1093/bioinformatics/btaa773.
By binding to specific structures on antigenic proteins, the so-called epitopes, B-cell antibodies can neutralize pathogens. The identification of B-cell epitopes is of great value for the development of specific serodiagnostic assays and the optimization of medical therapy. However, identifying diagnostically or therapeutically relevant epitopes is a challenging task that usually involves extensive laboratory work. In this study, we show that the time, cost and labor-intensive process of epitope detection in the lab can be significantly reduced using in silico prediction.
Here, we present EpiDope, a python tool which uses a deep neural network to detect linear B-cell epitope regions on individual protein sequences. With an area under the curve between 0.67 ± 0.07 in the receiver operating characteristic curve, EpiDope exceeds all other currently used linear B-cell epitope prediction tools. Our software is shown to reliably predict linear B-cell epitopes of a given protein sequence, thus contributing to a significant reduction of laboratory experiments and costs required for the conventional approach.
EpiDope is available on GitHub (http://github.com/mcollatz/EpiDope).
Supplementary data are available at Bioinformatics online.
通过与抗原蛋白上的特定结构（所谓的表位）结合，B 细胞抗体可以中和病原体。鉴定 B 细胞表位对于开发特异性血清诊断检测和优化医学治疗具有重要价值。然而，鉴定具有诊断或治疗意义的表位是一项具有挑战性的任务，通常涉及大量的实验室工作。在这项研究中，我们表明，使用计算机预测可以显著减少实验室中表位检测的时间、成本和劳动强度。
在这里，我们提出了 EpiDope，这是一个使用深度神经网络来检测单个蛋白质序列上线性 B 细胞表位区域的 Python 工具。在接收器操作特征曲线中，曲线下面积为 0.67±0.07，EpiDope 超过了目前所有其他使用的线性 B 细胞表位预测工具。我们的软件被证明可以可靠地预测给定蛋白质序列的线性 B 细胞表位，从而显著减少了实验室实验和传统方法所需的成本。
EpiDope 可在 GitHub 上获得（http://github.com/mcollatz/EpiDope）。
补充数据可在生物信息学在线获得。
