Shuai Lu

MsPBRsP predicts protein binding residues using a language model without MSA tools, achieving higher accuracy and faster performance than existing methods.

The paper proposes a deep learning model that combines local and global features to predict B-cell epitopes, showing improved performance over existing methods.

This study introduces AttCNNPPISP, a model combining attention mechanisms and CNNs to predict protein-protein interaction sites, showing improved performance over existing methods.

The paper introduces a method combining CNNs and GCNs to predict paratope residues in antibodies by integrating sequence and spatial information.

This paper proposes a sequence-based method for predicting antibody paratopes by integrating local-global sequence information and partner antigen features, achieving better performance than existing methods.