Progress and challenges in protein structure prediction.

Area of Science:

• Computational biology
• Structural bioinformatics

Background:

• Protein structure prediction is crucial for understanding biological function.
• Methods are broadly categorized into template-based modeling and free modeling.
• Template-based modeling relies on existing structures in the Protein Data Bank (PDB).

Purpose of the Study:

• To review the current state and challenges in protein structure prediction methodologies.
• To highlight advancements in template-based modeling, particularly structure refinement.
• To discuss the progress and limitations of free modeling approaches.

Main Methods:

• Template-based modeling utilizes threading to detect structural analogs.
• Structure refinement employs multiple structure templates and hybrid force fields (knowledge-based and physics-based).
• Free modeling focuses on ab initio folding, particularly for smaller proteins.

Main Results:

• Methodology development in threading has reached a plateau.
• Structure refinement shows significant progress, improving accuracy towards native structures.
• Free modeling has achieved atomic resolution for small proteins but faces challenges with larger ones (>150 residues).

Conclusions:

• Advancements in template-based modeling, especially refinement, are promising.
• Predicting structures for large proteins remains a significant challenge in free modeling.
• Future efforts in free modeling need to address force field limitations and conformational search space.