Bublil Erez M, Freund Natalia Tarnovitski, Mayrose Itay, Penn Osnat, Roitburd-Berman Anna, Rubinstein Nimrod D, Pupko Tal, Gershoni Jonathan M
Department of Cell Research and Immunology, Tel Aviv University, Tel-Aviv, Israel.
Proteins. 2007 Jul 1;68(1):294-304. doi: 10.1002/prot.21387.
Mapping the epitope of an antibody is of great interest, since it contributes much to our understanding of the mechanisms of molecular recognition and provides the basis for rational vaccine design. Here we present Mapitope, a computer algorithm for epitope mapping. The algorithm input is a set of affinity isolated peptides obtained by screening phage display peptide-libraries with the antibody of interest. The output is usually 1-3 epitope candidates on the surface of the atomic structure of the antigen. We have systematically tested the performance of Mapitope by assessing the effect of the algorithm parameters on the final prediction. Thus, we have examined the effect of the statistical threshold (ST) parameter, relating to the frequency distribution and enrichment of amino acid pairs from the isolated peptides and the D (distance) and E (exposure) parameters which relate to the physical parameters of the antigen. Two model systems were analyzed in which the antibody of interest had previously been co-crystallized with the antigen and thus the epitope is a given. The Mapitope algorithm successfully predicted the epitopes in both models. Accordingly, we formulated a stepwise paradigm for the prediction of discontinuous conformational epitopes using peptides obtained from screening phage display libraries. We applied this paradigm to successfully predict the epitope of the Trastuzumab antibody on the surface of the Her-2/neu receptor in a third model system.
绘制抗体的表位很有意义，因为这有助于我们深入了解分子识别机制，并为合理的疫苗设计提供基础。在此，我们介绍Mapitope，一种用于表位绘制的计算机算法。该算法的输入是通过用目标抗体筛选噬菌体展示肽库获得的一组亲和分离肽。输出通常是抗原原子结构表面上的1至3个表位候选物。我们通过评估算法参数对最终预测的影响，系统地测试了Mapitope的性能。因此，我们研究了统计阈值（ST）参数的影响，该参数与分离肽中氨基酸对的频率分布和富集有关，以及与抗原物理参数相关的D（距离）和E（暴露）参数。分析了两个模型系统，其中目标抗体先前已与抗原共结晶，因此表位是已知的。Mapitope算法在两个模型中均成功预测了表位。因此，我们制定了一个逐步的范例，用于使用从筛选噬菌体展示文库获得的肽来预测不连续的构象表位。我们应用这个范例在第三个模型系统中成功预测了曲妥珠单抗抗体在Her-2/neu受体表面的表位。
