IQSPred-PLM: An Interpretable Quorum Sensing Peptides Prediction Model Based on Protein Language Model

Quorum sensing regulates cooperative behaviors in bacteria through the accumulation and detection of signaling molecules. This process plays a crucial role in various biological functions, including biofilm formation, antibiotic production, regulation of virulence factors, and immune modulation. Quorum sensing peptides (QSPs), primarily produced by Gram-positive bacteria, are key components of the quorum sensing mechanism, and their identification is crucial for understanding bacterial regulation. Despite the availability of several QSP prediction tools based on handcrafted features and machine learning techniques, there is still potential for improving their performance and interpretability. In this study, we present IQSPred-PLM, a novel model for predicting QSPs that integrates protein language models (PLMs) with a convolutional neural network (CNN). First, we utilize the pre-trained PLM ESM-2 to encode peptide sequences. Then, feature extraction is performed using a multi-scale residual CNN (MSRes-CNN), with dynamic feature integration through an adaptive weight modulation (AWM) module. Finally, a fully connected network is designed to conduct the classification of QSPs. Evaluated on the benchmark dataset, IQSPred-PLM demonstrated the outstanding predictive performance with accuracy (ACC), Matthews correlation coefficient (MCC), and the area under the receiver operating characteristic (ROC) curve (AUC) of 97.50%, 0.951, and 0.990, respectively. Furthermore, case studies and interpretability analyses confirmed the effectiveness of IQSPred-PLM for the QSP prediction task.