Predicting antigenic peptides using a multi-level pooling-based transformer model with enhanced Kolaskar & Tongaonkar's algorithm for feature selection.

Antigenic peptide (AP) prediction is one of the most important roles in improve vaccine design and interpreting immune responses. This paper develops a Multi-Level Pooling-based Transformer (MLPT) model, which improves the accuracy and efficiency of predicting T-cell epitopes (TCEs). The model has utilized peptide sequences from the Immune Epitope Database (IEDB) and utilized a refined Kolaskar & Tongaonkar algorithm for feature extraction as well as a Self-Improved Black-winged Kite optimization algorithm to optimize the scoring matrix. The MLPT architecture takes the input features from the Adaptive Depthwise Multi-Kernel Atrous Module (ADMAM) as inputs to the Swin Transformer, and the output of Swin block 1 is concatenated with the features extracted from the Kolaskar-Tongaonkar algorithm with the SA-BWK model. This hierarchical integration enhances feature representation and predictive capability. Advanced feature extraction, coupled with optimized feature selection for the MLPT model improves its performance over the conventional approach in the identification of reduced-complexity antigenic determinants.