A Knowledge-Based Bidirectional Encoder Representation from Transformers to Predict a Paratope Position from a B Cell Receptor’s Amino Acid Sequence Alone

Antibody function is an important topic for the understanding of disease, but it would be quite challenging to make an accurate prediction of a paratope position from very limited information such as a B cell receptor’s (BCR’s) amino acid sequence alone. In this context, this study presents a knowledge-based Bidirectional Encoder Representation from Transformers (K-BERT) to deliver a precise prediction of a paratope position from a B cell receptor’s amino acid sequence alone. Here, the knowledge context of an amino acid consisted of its predecessor amino acids. These knowledge contexts were either common among all amino acids within each BCR chain or different for different amino acids within each BCR chain. Also, oversampling was employed given that the original data of 20,679 cases (900 BCR chains) were characterized by class imbalance, i.e., 18,724:1955 for labels 0:1. The performance measures in terms of sensitivity and F1 registered great improvements as different knowledge contexts and oversampling were introduced. The accuracy, sensitivity, specificity and F1 of a baseline model (with common knowledge and no oversampling) were 90.3, 0.0, 100.0 and 50.0, respectively. On the other hand, the corresponding accuracy, sensitivity, specificity and F1 of the final model (with different knowledge and strong oversampling) were 83.2, 90.1, 78.1 and 84.1. The final model demonstrated better sensitivity and F1 outcomes compared to the baseline model, i.e., 90.1 vs. 0.0 for sensitivity, 84.1 vs. 50.0 for F1. In conclusion, the K-BERT is an effective decision support system to predict a paratope position from a B cell receptor’s amino acid sequence alone. It has great potential for antibody therapeutics.