TF-BAPred: A Universal Bioactive Peptide Predictor Integrating Multiple Feature Representations

Bioactive peptides play essential roles in various biological processes and hold significant therapeutic potential. However, predicting the functions of these peptides is challenging due to their diversity and complexity. Here, we develop TF-BAPred, a framework for universal peptide prediction incorporating multiple feature representations. TF-BAPred feeds original peptide sequences into three parallel modules: a novel feature proposed in this study called FVG extracts the global features of each peptide sequence; an automatic feature recognition module based on a temporal convolutional network extracts the temporal features; and a module integrates multiple widely used features such as AAC, DPC, BPF, RSM, and CKSAAGP. In particular, FVG constructs a fixed-size vector graph to represent the global pattern by capturing the topological structure between amino acids. We evaluated the performance of TF-BAPred and other peptide predictors on different types of peptides, including anticancer peptides, antimicrobial peptides, and cell-penetrating peptides. The benchmarking tests demonstrate that TF-BAPred displays strong generalization and robustness in predicting various types of peptide sequences, highlighting its potential for applications in biomedical engineering.