A Sequence-based Antibody Paratope Prediction Model Through Combing Local-Global Information and Partner Features

Abstract

Antibodies are proteins which play a vital role in the immune system by recognizing and neutralizing antigens. The region on the antibody binds to the antigens, also known as paratope, mediates antibody-antigen interaction with high affinity and specificity. And the accurate prediction of those regions from antibody sequence contributes to the design of therapeutic antibodies and remains challenging. However, the experimental methods are time-consuming and expensive. In this article, we propose a sequence-based method for antibody paratope prediction by combing the local and global features of antibody sequence and global features of partner antigen sequence. For extracting local features, we use Convolution Neural Networks(CNNs) and a sliding window approach on antibody sequence. For extracting global features, we use Attention-based Bidirectional Long Short-Term Memory(Att-BLSTM) networks on antibody sequence. For extracting partner features, we employ Att-BLSTM on the partner antigen sequence as well. And then, all features are combined to predict antibody paratope by fully-connected networks. The experiments show that our proposed method achieves superior performance over the state-of-the-art sequenced-based antibody paratope prediction methods on benchmark datasets.