Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms

Area of Science:

• Computational Biology
• Biochemistry
• Bioinformatics

Background:

• Protein structure prediction is a complex, interdisciplinary challenge involving biochemistry, physics, mathematics, and computer science.
• Diverse research paradigms, including principle-based, statistical, and optimization approaches, have been employed.

Purpose of the Study:

• To review and compare various research paradigms in protein structure prediction.
• To highlight the impact of deep learning on the field and identify future research needs.

Main Methods:

• Survey of existing literature on protein structure prediction methodologies.
• Comparison of data modeling and algorithmic modeling approaches, including deep learning.

Main Results:

• Algorithmic modeling, especially deep neural networks, has substantially enhanced prediction accuracy.
• A significant shift towards deep learning-based methods is observed in recent research.

Conclusions:

• Deep learning has revolutionized protein structure prediction accuracy.
• Further theoretical understanding of neural networks and protein folding principles is essential for continued progress.