Predicting Protein-Protein Binding Affinity with Deep Learning: A Comparative Analysis of CNN and Transformer Models

Binding affinity (BA) prediction is important for drug discovery and protein engineering. It seeks to understand the interaction strength between proteins and their ligands (or proteins). This information assists in the design of proteins with enhanced or novel functions, as well as understanding the molecular mechanisms of drug action. This paper presents the development and comparative analysis of two deep learning models, a convolutional neural network (CNN) and a transformer model. Many variants of models in this research were developed using TensorFlow. One model that utilizes ProteinBERT was developed using PyTorch. The CNN model captures local sequence features effectively, while the Transformer model leverages self-attention mechanisms to learn long-range dependencies within the sequences. Protein sequences are the inputs for the models. The sequences are processed using various encoders, like One-hot encoding, Sequence-Statistics-Content, and Position Specific Scoring Matrix. The predicted outputs are Gibbs free energy changes, a key indicator of binding affinity. From this study, both the CNN and transformer models can achieve the same level of accuracy under different conditions. For the CNN model, it can handle full data without sacrificing performance, but it takes much more time to preprocess the features from the protein sequences. The transformer model can achieve the same level of accuracy as the CNN model with no big predictive errors for each protein, but it requires the model to run on less data, which removes some rarely long protein sequences. This study emphasizes the potential of advanced deep learning architectures to enhance the predictive strengths of binding affinity models.