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Strategy for supplementing structure calculations using limited data with hydrophobic distance restraints
1Department of Molecular and Cell Biology, University of Connecticut, Storrs, Connecticut 06269-3125, USA. andrei@uconn.edu
Proteins
|May 27, 2004
Summary
Protein folding is not fully determined by dihedral angles alone. Incorporating hydrogen bonds and hydrophobic restraints significantly improves protein structure prediction accuracy.
Area of Science:
- Biochemistry
- Structural Biology
- Computational Biology
Background:
- Protein structure is crucial for function.
- Dihedral angles (phi and psi) are traditionally considered sufficient to define protein fold.
- This study investigates the sufficiency of dihedral angle restraints for accurate protein structure prediction.
Purpose of the Study:
- To test the hypothesis that dihedral angles alone define protein fold.
- To evaluate the effectiveness of Torsion Angle Dynamics/Simulated Annealing (TAD/SA) with dihedral restraints.
- To develop and test improved TAD/SA methods incorporating long-range restraints.
Main Methods:
- Torsion Angle Dynamics and Simulated Annealing (TAD/SA) calculations.
- Utilized dihedral angle restraints based on X-ray crystallography data.
- Incorporated hydrogen-bond restraints and hydrophobic distance restraints.
- Tested with simulated restraints from various protein structures and NMR data.
Main Results:
- Dihedral angle restraints alone resulted in significant deviation (4 Å RMSD) from the native protein structure.
- Deviations were attributed to non-planar peptide bonds, energy conflicts, and lack of long-range information.
- Including even a few long-range contacts (hydrogen bonds) dramatically improved fold accuracy.
- The developed TAD/SA procedure with combined restraints showed improved performance.
Conclusions:
- Dihedral angles alone are insufficient to accurately predict protein structure.
- Long-range interactions, particularly hydrogen bonds, are critical for defining protein fold.
- A combined restraint strategy improves the accuracy of computational protein structure prediction.