NABP-LSTM-Att: Nanobody–Antigen binding prediction using bidirectional LSTM and soft attention mechanism

Abstract

In vertebrates, antibody-mediated immunity is a vital component of the immune system, and antibodies have become a rapidly expanding class of therapeutic agents. Nanobodies, a distinct type of antibody, have recently emerged as a stable and cost-effective alternative to traditional antibodies. Their small size, high target specificity, notable solubility, and stability make nanobodies promising candidates for developing high-quality drugs. However, the lack of available nanobodies for most antigens remains a key challenge. Advancing the development of nanobodies requires a better understanding of their interactions with antigens to enhance binding affinity and specificity. Experimental methods for identifying these interactions are essential but often costly and time-consuming, posing challenges for developing nanobody therapies. Although several computational approaches have been designed to screen potential nanobodies, their dependency on 3D structures limits their broad application. This research introduces NABP-LSTM-Att, a deep learning model designed to predict nanobody–antigen binding solely from sequence information. NABP-LSTM-Att leverages bidirectional long short-term memory (biLSTM) to capture both long- and short-term dependencies within nanobody and antigen sequences, combined with a soft attention mechanism to focus on key features. When evaluated on nanobody–antigen sequence pairs from the SAbDab-nano database, NABP-LSTM-Att achieved an AUROC of 0.926 and an AUPR of 0.952. Considering the significance of nanobody-based treatments and their prospective uses in immunotherapy and diagnostics, we believe that the proposed model will serve as an effective tool for predicting nanobody–antigen binding.