AbAgIntPre is a deep learning-assisted web server for fast identification of antibody-antigen interactions that only relies on amino acid sequences. By using a Siamese-like convolutional neural network with the amino acid composition encoding scheme, AbAgIntPre achieved satisfactory performance with the Area Under Curve (AUC) of 0.82 on a high-quality generic independent test dataset. Besides, this approach also showed competitive performance on the more specific SARS-CoV dataset. We expect that AbAgIntPre can serve as an important complement to traditional experimental methods for antibody screening and effectively reduce the workload of antibody design. The web server of AbAgIntPre is freely available at here.
We used a Siamese-like CNN as the deep learning classifier to infer whether the query antigen and antibody can interact. Specifically, our model mainly included three parts: input module, convolution module, and prediction module. In the input module, antigens or antibodies were coded by CKSAAP encoding scheme, and four channels correspond to four k-spaced values in CKSAAP. The convolution module further processed the encoded feature vectors. AbAgIntPre included two convolution modules, each of which consists of a batch normalization layer, convolution layer, rectified linear unit, and pooling. Two fully connected layers were used to map the learned distributed features to the sample label space and yield the probability of interaction between the given antigen and antibody.
The source code of siamese CNN architechture and CKSAAP encoding strategy can be found in the model folder.
The binding samples we used in our study were from two public databases: SAbDab and CoV-AbDab.
Folder SAbDab stores all the PDB ID used in our generic model training. Folder CoV-AbDab stores the cross validation dataset and independent dataset used in our CoV-specific model traning. In CoV-AbDab, samples are divided into three columns: the first column is the name of antigens; the second column is the sequence of antibody heavy chain; the third column is the sequence of antibody light chain.
We would like to thank the SAbDab and CoV-AbDab for the antibody-antigen binding data support.