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Refinement of Generalized Born Implicit Solvation Parameters for Nucleic Acids and Their Complexes with Proteins
Hai Nguyen1, Alberto Pérez1, Sherry Bermeo1
1Department of Chemistry, ‡Laufer Center for Physical and Quantitative Biology, and §Department of Biochemistry, Stony Brook University , Stony Brook, New York 11794, USA.
Journal of Chemical Theory and Computation
|November 18, 2015
Summary
A new Generalized Born (GB) model, GB-neck2, enhances accuracy for nucleic acid simulations. This improved model ensures greater structural stability in DNA and RNA simulations, including complex protein-nucleic acid interactions.
Area of Science:
- Computational chemistry
- Biophysics
- Molecular modeling
Background:
- Generalized Born (GB) models offer efficient implicit solvent simulations but face challenges with nucleic acid stability and structural bias.
- Previous GB models, including GB-neck2 for proteins, show potential for improvement in biomolecular simulations.
- Accurate modeling of nucleic acids and their interactions with proteins is crucial in molecular dynamics.
Purpose of the Study:
- To develop an accurate and stable Generalized Born (GB) implicit solvent model specifically for nucleic acid simulations.
- To improve the simulation of protein-nucleic acid complexes by addressing limitations of existing GB models.
- To provide a robust computational tool for studying DNA and RNA structures and dynamics.
Main Methods:
- Development of a new GB parameter set for nucleic acids, inspired by the GB-neck2 approach for proteins.
- Validation against Poisson-Boltzmann calculations to assess solvation energy accuracy for nucleic acids and protein-nucleic acid complexes.
- Molecular dynamics simulations to evaluate structural stability of DNA/RNA duplexes, quadruplexes, and protein-nucleic acid complexes.
- Comparison of simulation results with experimental data for folding of small DNA and RNA hairpins.
Main Results:
- The new GB parameter set significantly reduces energy errors compared to the predecessor GB-neck model for nucleic acids and complexes.
- Enhanced structural stability observed in simulations of DNA and RNA duplexes, quadruplexes, and protein-nucleic acid complexes.
- Successful prediction of near-native structures for small DNA and RNA hairpins, validated by experimental data.
Conclusions:
- The developed GB model offers a significant improvement for nucleic acid simulations, providing greater accuracy and stability.
- This advancement facilitates more reliable computational studies of nucleic acids and their interactions with proteins.
- The new model and parameters are available within the AMBER software package for broader application.
