Introduction:

Proteins are vital for life, performing various functions crucial for organisms. Understanding their 3D structures is essential, yet experimental determination is challenging and resource-intensive. Computational methods offer a solution, with Template-based Modeling (TBM) and Free Modeling (FM) being the main approaches.

Template-Based Modeling (TBM):

TBM relies on existing structures as templates, assuming sequence similarity implies structural similarity. While fast and accurate, TBM’s success hinges on template availability and quality.

Free Modeling (FM):

FM predicts structures without templates, using physical and chemical principles. Despite flexibility, FM is computationally intensive and less accurate, struggling with long-range interactions.

Deep Learning in Protein Structure Prediction:

Deep learning has revolutionized protein structure prediction. It’s employed in both TBM and FM tasks, enhancing alignment accuracy and predicting structural features like secondary structures and contact maps.

Applications of Deep Learning:

AlphaFold, a notable deep learning method, predicts inter-residue distances and optimizes 3D coordinates, outperforming experimental methods. RaptorX combines TBM and FM, refining templates using deep convolutional neural networks.

Challenges and Limitations:

While promising, deep learning methods face challenges such as interpretability, data quality dependence, and computational resource requirements. They complement experimental methods but require validation.

Conclusion:

Deep learning is a potent tool in protein structure prediction, yet it requires refinement and collaboration with other methods. Together, computational and experimental approaches can deepen our understanding of proteins and life itself.