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A polarizable coarse-grained protein model for dissipative particle dynamics
Emanuel K Peter1, Kirill Lykov, Igor V Pivkin
1Institute of Computational Science, Faculty of Informatics, University of Lugano, Lugano, Switzerland. igor.pivkin@usi.ch.
Physical Chemistry Chemical Physics : PCCP
|September 5, 2015
Summary
We developed a new coarse-grained polarizable protein model for Dissipative Particle Dynamics (DPD) simulations. This model accelerates protein sampling and accurately reproduces experimental structures at the atomistic level.
Area of Science:
- Computational Biology
- Biophysics
- Molecular Dynamics
Background:
- Particle-based simulations offer efficient methods for studying large systems.
- Coarse-grained models simplify complex molecular systems for faster simulations.
- Polarizable models are crucial for accurately capturing electrostatic interactions.
Purpose of the Study:
- To introduce a novel coarse-grained polarizable protein model for Dissipative Particle Dynamics (DPD).
- To enhance the speed and accuracy of molecular dynamics simulations for proteins.
- To enable large timesteps and rapid sampling in particle-based systems.
Main Methods:
- Developed a coarse-grained polarizable protein model incorporating electrostatic polarization of the backbone and detailed sidechain representation.
- Utilized a polarizable water model in conjunction with the protein model.
- Defined model parameters using experimental structures of TrpZip2 and TrpCage proteins.
- Verified the model through backmapping and replica-exchange molecular dynamics simulations.
Main Results:
- The model demonstrated convergence to experimental structures at the atomistic level.
- Validation on five diverse proteins (GB1, WW-domain, Protein A B-domain, peripheral binding subunit, villin headpiece) showed model efficacy.
- The Dissipative Particle Dynamics (DPD) method with the new model allows for significantly larger timesteps, accelerating sampling.
Conclusions:
- The new coarse-grained polarizable protein model provides an efficient and accurate approach for molecular dynamics simulations.
- This model facilitates rapid sampling and accurate structural reproduction for various proteins.
- The method holds promise for advancing computational studies in biophysics and structural biology.
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