AMPpred-DLFF:prediction of AMPs based on deep learning and multi-view features fusion

Antimicrobial peptides (AMPs) hold significant promise in antibacterial and anticancer research, offering a crucial solution to the escalating issue of antibiotic resistance and paving the way for developing novel strategy for cancer treatments. Previously, experimental methods were used to identify AMPs. However, there is now a shift towards developing computational approaches to predict AMPs accurately, greatly reducing the time and effort required for experimental identification. Past computation methods for predicting AMPs have either focused on feature encoding and extraction or emphasized neural network design. Unfortunately, relying on a single method or feature can lead to the oversight of important information. In this study, we present AMPpred-DLFF, a novel computational method for identifying AMPs. This method initially employs ESM-2 to generate residue embedding representations, captures the spatial relationships between residues, and constructs a graph to integrate peptide data. Subsequently, a graph attention network extracts features from the graph data. Concurrently, multiple encoding techniques are used to extract residue features from sequence data, which are then processed by a convolutional neural network. Finally, the feature representations from both modules are combined, and the peptide sequence is predicted as an antimicrobial peptide through a fully connected layer. Performance assessments on several benchmark datasets demonstrate that AMPpred-DLFF outperforms current leading methods, underscoring the effectiveness of multi-view fusion in enhancing AMP prediction accuracy.