Protein structure prediction using residue- and fragment-environment potentials in CASP11

Area of Science:

• Computational Biology
• Structural Biology
• Bioinformatics

Background:

• Accurate protein structure prediction is crucial for understanding biological function.
• Existing scoring functions often struggle to reliably select near-native models from large decoy sets.

Purpose of the Study:

• To develop and evaluate novel scoring functions for improved protein structure prediction.
• To assess the performance of a new protocol in the Critical Assessment of Techniques for Protein Structure Prediction (CASP) 11.

Main Methods:

• Developed PRESCO (Protein Residue Environment SCOre), a coarse-grained scoring function evaluating local residue environments.
• Incorporated a helix interaction potential as an additional decoy selection tool.
• Employed structure refinement including side-chain modeling and molecular dynamics simulations.

Main Results:

• The PRESCO scoring function demonstrated high accuracy in identifying native-like protein models.
• The developed protocol achieved the top rank in the CASP11 free modeling category.
• A significant margin in accumulated Z-score was observed compared to other groups for top models.

Conclusions:

• The novel scoring functions, PRESCO and helix interaction potential, significantly enhance protein structure prediction.
• The developed protocol represents a state-of-the-art approach for selecting accurate protein structure models.
• This work advances the field of computational protein structure prediction.