2025

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Browsing 2025 by Author "AHMED BILAL"

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    Prediction of dipeptidyl peptidase-iv inhibitory peptides using deep learning
    (UMT Lahore, 2025) AHMED BILAL
    Dipeptidyl Peptidase IV (DPP-IV), a critical enzyme in glucose metabolism regulation, has emerged as a promising therapeutic target for Type 2 diabetes mellitus (T2DM). Inhibition of DPP-IV enhances incretin hormone activity, thereby improving insulin secretion. While conventional methods for identifying DPP-IV-inhibiting peptides are time-consuming and costly, computational approaches leveraging machine learning (ML) and deep learning (DL) offer a transformative alternative for accurate prediction of inhibitory peptides. This study employed peptide sequences filtered using the "Amyloid" keyword from UniProt to construct a robust dataset. A comprehensive computational framework was developed, integrating traditional ML algorithms—Support Vector Machines (SVM), Random Forests (RF), and k-Nearest Neighbors (k-NN)—with advanced DL architectures, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, to classify DPP-IV inhibitors and non-inhibitors. Model stability was validated via k-fold cross-validation, while performance was assessed using accuracy, precision, recall, F1-score, and AUC-ROC metrics. DL models, particularly LSTM networks, achieved superior classification accuracy (96%), outperforming traditional ML methods. Visualization techniques such as t-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) confirmed distinct clustering of inhibitory peptides in feature space. Ensemble learning further enhanced classification reliability through generalized predictions across diverse datasets. The proposed framework demonstrates significant methodological advantages over conventional peptide discovery approaches, including automated feature extraction, high precision, and substantial reductions in experimental screening time and cost. Its scalability positions it as a versatile computational tool for identifying therapeutic peptides across multiple targets. This study underscores the efficacy of DL in deciphering critical peptide features for inhibition and highlights the value of multi-network integration and independent validation for reproducible outcomes. By enabling rapid discovery of novel T2DM therapeutics, this scalable system bridges computational and experimental drug discovery. Future efforts will focus on incorporating additional biological data and transfer learning to refine predictive accuracy and broaden applicability.