Role of Deep Learning in Protein Structure Prediction: Current Progress and Open Challenges

Abstract

Introduction: Protein structure prediction is a fundamental challenge in computational biology, with significant implications for drug discovery, disease modelling, and biotechnology. Traditional experimental methods such as X-ray crystallography, nuclear magnetic resonance spectroscopy, and cryo-electron microscopy provide highly accurate structural insights but are expensive, time-consuming, and often unsuitable for complex or membrane-bound proteins. To address these limitations, computational approaches have emerged, categorized into physics-based simulations, evolutionary-based modeling, and deep learning techniques. Recent advancements in machine learning and deep learning have revolutionized protein structure prediction. Models like AlphaFold2 and RoseTTAFold have achieved near-experimental accuracy by leveraging large-scale protein datasets and advanced neural network architectures. However, challenges remain in multi-protein interaction modeling, side-chain conformation prediction, dataset biases, and computational efficiency. Additionally, the black-box nature of deep learning models limits their interpretability, necessitating efforts to enhance transparency and explainability in AI-driven protein modeling.

Objectives: The main objective of this study is to comprehensive review the protein structure prediction methodologies, highlighting the evolution from traditional computational models to cutting-edge deep learning frameworks.

Methods: It discusses key challenges, including dataset limitations, model scalability, and integration with experimental techniques, and explores future research directions such as self-supervised learning, quantum computing for protein folding simulations, and energy-efficient deep learning architectures.

Conclusions: By addressing these challenges, computational protein modeling can further advance biomedical research, enabling more accurate disease modeling, rational drug design, and the development of synthetic biomolecules with tailored functions.