Refinement of homology-based protein structures by molecular dynamics simulation techniques

Area of Science:

• Biophysics
• Computational Biology
• Structural Biology

Background:

• Protein structure prediction is crucial for understanding function.
• Ab initio and homology modeling generate initial structural hypotheses.
• Refinement methods are needed to improve the accuracy of these models.

Purpose of the Study:

• To investigate the utility of classical molecular dynamics (MD) simulations for refining protein structural models.
• To assess the effectiveness of MD simulations on explicit solvent for models generated via ab initio or homology methods.

Main Methods:

• Utilized classical molecular dynamics simulations in explicit water.
• Simulated 15 proteins, with four models each generated by the ROSETTA procedure.
• Simulations ranged from 5 to 400 nanoseconds (nsec).
• Included experimentally determined structures and regenerated side-chain structures as controls.

Main Results:

• Observed significant improvement in model structure accuracy compared to experimental structures in several cases.
• Found that regenerating side chains improved structural stability in proteins exhibiting internal strain during simulations.
• Demonstrated that MD simulations can reduce deviations between predicted and experimental protein structures.

Conclusions:

• Molecular dynamics simulations, particularly on the tens to hundreds of nanoseconds timescale, are effective for refining protein structural models.
• This approach is valuable for improving the accuracy of both ab initio and homology-based protein models.
• The findings support the use of MD simulations as a refinement tool in structural biology.