Protein structure prediction via deep learning: an in-depth review

Area of Science:

• Computational Biology
• Biochemistry
• Drug Discovery

Background:

• Accurate protein structures are vital for understanding biological functions and developing therapeutics.
• Experimental methods for protein structure determination are often slow, expensive, and labor-intensive.
• A significant gap exists between the number of known protein sequences and experimentally determined structures.

Purpose of the Study:

• To provide a comprehensive review of deep learning applications in protein structure prediction.
• To guide researchers in utilizing deep learning methodologies and tools.
• To discuss the role of large language models in advancing this field.

Main Methods:

• Review of existing literature on deep learning for protein structure prediction.
• Exploration of relevant databases for protein structure data.
• Analysis of state-of-the-art deep learning models, including large language models.

Main Results:

• Deep learning offers a powerful computational approach to bridge the gap in protein structure determination.
• Recent advancements include sophisticated deep learning models and large language models tailored for this task.
• Databases and tools are crucial for applying these methods effectively.

Conclusions:

• Deep learning is revolutionizing protein structure prediction, accelerating drug discovery.
• The review highlights current methodologies and future directions in the field.
• Challenges and opportunities in predicting protein structure using AI are discussed.