Protein Molecular Dynamics Simulations with Approximate QM: What Can We Learn?
Stephan Irle1,2, Van Q Vuong3,4, Mouhmad H Elayyan5
1Computational Sciences and Engineering Division & Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA. irles@ornl.gov.
Methods in Molecular Biology (Clifton, N.J.)
|February 5, 2020
Summary
Modern protein force fields need improvement for protein-protein interactions. This study compares molecular mechanics force fields with quantum mechanical methods (DFTB and FMO-DFTB) to assess peptide folding dynamics and guide future drug discovery.
Area of Science:
- Computational chemistry
- Biophysics
- Drug discovery
Background:
- Classical force fields are crucial for protein simulations, but often neglect protein-protein interactions.
- Understanding intermolecular interactions is key for advancing drug development and computational screening.
- Current force fields require critical assessment against quantum mechanical methods and experimental data.
Purpose of the Study:
- To compare peptide folding dynamics predicted by molecular mechanics force fields and quantum mechanical (QM) methods.
- To evaluate the performance of density-functional tight-binding (DFTB) and fragment molecular orbital DFTB (FMO-DFTB) against classical force fields.
- To identify implications for force field development and high-throughput screening in drug discovery.
Main Methods:
- Molecular dynamics simulations using a classical force field.
- Quantum mechanical simulations using density-functional tight-binding (DFTB).
- Near-linear scaling DFTB simulations using the fragment molecular orbital (FMO-DFTB) method for parallel computation.
Main Results:
- Differences in peptide folding dynamics were observed between the molecular mechanics force field and QM methods.
- Charge polarization and dynamic fluctuations varied across the simulated methods.
- The study highlights discrepancies and potential correlations in predicting protein dynamics.
Conclusions:
- Classical force fields may not fully capture the nuances of protein-protein interactions and dynamics.
- QM methods like DFTB and FMO-DFTB offer valuable insights for refining force fields.
- Findings can inform future computational screening for drug development, including personalized cancer therapies.
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