PDF(Pubmed)
Abstract：
Antibodies play a central role in the adaptive immune response of vertebrates through the specific recognition of exogenous or endogenous antigens. The rational design of antibodies has a wide range of biotechnological and medical applications, such as in disease diagnosis and treatment. However, there are currently no reliable methods for predicting the antibodies that recognize a specific antigen region (or epitope) and, conversely, epitopes that recognize the binding region of a given antibody (or paratope). To fill this gap, we developed ImaPEp, a machine learning-based tool for predicting the binding probability of paratope-epitope pairs, where the epitope and paratope patches were simplified into interacting two-dimensional patches, which were colored according to the values of selected features, and pixelated. The specific recognition of an epitope image by a paratope image was achieved by using a convolutional neural network-based model, which was trained on a set of two-dimensional paratope-epitope images derived from experimental structures of antibody-antigen complexes. Our method achieves good performances in terms of cross-validation with a balanced accuracy of 0.8. Finally, we showcase examples of application of ImaPep, including extensive screening of large libraries to identify paratope candidates that bind to a selected epitope, and rescoring and refining antibody-antigen docking poses.
摘要：
抗体通过特异性识别外源性或内源性抗原在脊椎动物的适应性免疫应答中起核心作用。抗体的合理设计具有广泛的生物技术和医学应用，如在疾病诊断和治疗中。然而,目前还没有可靠的方法来预测识别特定抗原区（或表位）的抗体，相反,识别给定抗体(或互补位)的结合区的表位。为了填补这个空白，我们开发了ImaPEp,一种基于机器学习的工具，用于预测互补位-表位对的结合概率，其中表位和互补位片被简化为相互作用的二维片，根据选定特征的值着色，和像素化。利用基于卷积神经网络的模型实现了对表位图像的特异性识别,在一组源自抗体-抗原复合物的实验结构的二维互补表位图像上进行了训练。我们的方法在交叉验证方面取得了良好的性能，平衡精度为0.8。最后,我们展示了ImaPep的应用实例，包括对大型文库的广泛筛选，以鉴定与选定表位结合的互补位候选物，以及重新评分和精炼抗体-抗原对接姿势。
