DeepANIS: Predicting antibody paratope from concatenated CDR sequences by integrating bidirectional long-short-term memory and transformer neural networks

MotivationAntibodies are a type of important biomolecules in the humoral immunity system, which can bind tightly to potential antigens with high affinity and specificity. An accurate identification of the paratope, the binding sites with antigens, is crucial for antibody mechanistic research and design. Although many methods have been developed for paratope prediction, further improvement of their accuracy is necessary. ResultsIn this study, we concatenated the sequences of Complementarity Determining Regions (CDRs) within a single antibody to better capture nonlocal interactions between different CDRs and loop type-specific features for improving paratope prediction. We further integrated BiLSTM and transformer networks to gain the dependencies among the residues within the concatenated CDR sequences and to increase the interpretability of the model. The new method called DeepANIS (Antibody Interacting Site prediction) outperforms other antibody paratope prediction methods compared. AvailabilityThe DeepANIS method is freely available as a webserver at https://biomed.nscc-gz.cn:9094/apps/DeepANIS and for download at https://github.com/HideInDust/DeepANIS Contactyangyd25@mail.sysu.edu.cn or zhouyq@szbl.ac.cn Supplementary informationSupplementary data are available at Bioinformatics online.