Kernelized Fuzzy System for Predicting Therapeutic Peptides via Deep Stacked Encoder

Therapeutic peptides play a key role in regulating cellular functions and repairing damaged cells through targeted molecular interactions. Traditional wet-lab methods for identifying therapeutic peptides rely on time-consuming biochemical assays and low-throughput screening techniques, which struggle to capture complex sequence-stability relationships critical for drug development. To address these limitations, we innovatively integrate a pretrained protein language model with stacked bidirectional long short-term memory (BiLSTM) encoders. This hybrid architecture enables hierarchical extraction of both global contextual patterns (via the language model) and localized sequential dependencies (via BiLSTM), effectively modeling nonlinear correlations within peptide sequences. A key technical lies in the proposed kernelized Takagi-Sugeno-Kang fuzzy system (K-TSK-FS), which combines fuzzy logic with kernel methods to handle sequence ambiguity while maintaining interpretability. Unlike conventional classifiers, this system maps high-dimensional features into a reproducing kernel Hilbert space, enhancing discrimination between therapeutic and non-therapeutic peptides through nonlinear decision boundaries. To evaluate the model, six benchmark datasets are used to test our model. Experimental results show that our method achieves better prediction performance.