Abstract
Motivation Antibodies 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.
Results In 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.
Availability The 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
Contact yangyd25{at}mail.sysu.edu.cn or zhouyq{at}szbl.ac.cn
Supplementary information Supplementary data are available at Bioinformatics online.
Competing Interest Statement
The authors have declared no competing interest.