Allergen based classification of peptides

Abstract

Peptides, as essential biological molecules, exhibit diverse structural and functional properties, some of which can trigger allergenic responses and pose significant challenges in biotherapeutic applications and drug design. This project investigates the binary classification of peptides into liable (allergenic) and non-liable (non-allergenic) categories using advanced machine learning techniques, aiming to aid in early allergenicity screening during drug development and bioengineering applications. The research utilized a comprehensive dataset of 25,000 peptide sequences to develop an end-to-end machine learning pipeline for allergenic potential prediction. The methodology involved extensive feature extraction using multiple biochemical and structural descriptors including Amino Acid Composition (AAC), Dipeptide Composition (DPC), Tripeptide Composition (TPC), Composition of k-Spaced Amino Acid Pairs (CKSAAP) and various grouped compositions computed through the iFeature tool. Additionally, ESM2 transformer-based language model embeddings were incorporated to capture deep contextual patterns. Many feature selection strategy was implemented, combining filter methods (Variance Threshold and ANOVA F-score), embedded methods (XGBoost) in a multi-stage process to optimize the feature space while preserving critical information. The framework was evaluated using a comprehensive suite of machine learning and deep learning models, including Random Forest, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Gradient Boosting variants (CatBoost, AdaBoost, XGBoost, LightGBM), and deep learning architectures such as Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and ResNet. Model performance was rigorously assessed using multiple evaluation metrics including Accuracy, AUROC, AUPRC, F1-score, Cohen's Kappa, and Matthews Correlation Coefficient (MCC). This project demonstrates the potential of combining advanced bioinformatics techniques with machine learning to address critical biological classification challenges and establishes a foundation for developing practical tools for peptide allergenicity screening in therapeutic and food applications.