CSI: Contrastive data Stratification for Interaction prediction and its application to compound-protein interaction prediction

FusionESP: Improved Enzyme-Substrate Pair Prediction by Fusing Protein and Chemical Knowledge

To reduce the cost of the experimental characterization of the potential substrates for enzymes, machine learning prediction models offer an alternative solution. Pretrained language models, as powerful approaches for protein and molecule representation, have been employed in the development of enzyme-substrate prediction models, achieving promising performance. In addition to continuing improvements in language models, effectively fusing encoders to handle multimodal prediction tasks is critical for further enhancing model performance by using available representation methods. Here, we present FusionESP, a multimodal architecture that integrates protein and chemistry language models with two independent projection heads and a contrastive learning strategy for predicting enzyme-substrate pairs. Our best model achieved state-of-the-art performance with an accuracy of 94.77% on independent test data and exhibited better generalization capacity while requiring fewer computational resources and training data, compared to previous studies of a fine-tuned encoder or employing more encoders. It also confirmed our hypothesis that embeddings of positive pairs are closer to each other in a high-dimension space, while negative pairs exhibit the opposite trend. Our ablation studies showed that the projection heads played a crucial role in performance enhancement, while the contrastive learning strategy further improved the projection heads' capacity in classification tasks. The proposed architecture is expected to be further applied to enhance performance in additional multimodality prediction tasks in biology. A user-friendly web server of FusionESP is established and freely accessible at https://rqkjkgpsyu.us-east-1.awsapprunner.com/.

Protein-DNA binding sites prediction based on pre-trained protein language model and contrastive learning

Protein-DNA interaction is critical for life activities such as replication, transcription and splicing. Identifying protein-DNA binding residues is essential for modeling their interaction and downstream studies. However, developing accurate and efficient computational methods for this task remains challenging. Improvements in this area have the potential to drive novel applications in biotechnology and drug design. In this study, we propose a novel approach called Contrastive Learning And Pre-trained Encoder (CLAPE), which combines a pre-trained protein language model and the contrastive learning method to predict DNA binding residues. We trained the CLAPE-DB model on the protein-DNA binding sites dataset and evaluated the model performance and generalization ability through various experiments. The results showed that the area under ROC curve values of the CLAPE-DB model on the two benchmark datasets reached 0.871 and 0.881, respectively, indicating superior performance compared to other existing models. CLAPE-DB showed better generalization ability and was specific to DNA-binding sites. In addition, we trained CLAPE on different protein-ligand binding sites datasets, demonstrating that CLAPE is a general framework for binding sites prediction. To facilitate the scientific community, the benchmark datasets and codes are freely available at https://github.com/YAndrewL/clape.