Unravelling Protein-DNA Interaction Predictions: A State-of-the-Art review

Protein-DNA interactions are pivotal to numerous cellular processes, including DNA replication, and repair. Accurate prediction of these interactions is essential for understanding biological mechanisms and has significant implications in areas such as computer-aided drug design, synthetic biology and genetic engineering. Over the past decade, computational approaches have evolved considerably, incorporating sequence-based analysis, structural modeling, and advanced machine learning techniques. Sequence-based methods leverage DNA and protein sequence patterns, while structure-based approaches utilize 3D structural data to identify binding sites and affinities. Machine learning methods, particularly deep learning, have introduced a new era of predictive accuracy by uncovering complex patterns in large-scale datasets. Despite these advancements, several challenges persist. These include limited availability of high-quality, labeled datasets, difficulties in feature engineering, and the interpretability of machine learning models.This review synthesizes the latest developments in protein-DNA interaction prediction, highlights current challenges, and outlines emerging trends and future scope.