Improved Generalized Born Solvent Model Parameters for Protein Simulations
- PMID: 25788871
- PMCID: PMC4361090
- DOI: 10.1021/ct3010485
Improved Generalized Born Solvent Model Parameters for Protein Simulations
Abstract
The generalized Born (GB) model is one of the fastest implicit solvent models and it has become widely adopted for Molecular Dynamics (MD) simulations. This speed comes with tradeoffs, and many reports in the literature have pointed out weaknesses with GB models. Because the quality of a GB model is heavily affected by empirical parameters used in calculating solvation energy, in this work we have refit these parameters for GB-Neck, a recently developed GB model, in order to improve the accuracy of both the solvation energy and effective radii calculations. The data sets used for fitting are significantly larger than those used in the past. Comparing to other pairwise GB models like GB-OBC and the original GB-Neck, the new GB model (GB-Neck2) has better agreement to Poisson-Boltzmann (PB) in terms of reproducing solvation energies for a variety of systems ranging from peptides to proteins. Secondary structure preferences are also in much better agreement with those obtained from explicit solvent MD simulations. We also obtain near-quantitative reproduction of experimental structure and thermal stability profiles for several model peptides with varying secondary structure motifs. Extension to non-protein systems will be explored in the future.
Keywords: GBSA; Generalized Born; Implicit solvent; perfect radii; protein folding; solvation energy.
Figures
Similar articles
-
Secondary structure bias in generalized Born solvent models: comparison of conformational ensembles and free energy of solvent polarization from explicit and implicit solvation.J Phys Chem B. 2007 Feb 22;111(7):1846-57. doi: 10.1021/jp066831u. Epub 2007 Jan 27. J Phys Chem B. 2007. PMID: 17256983 Free PMC article.
-
Accuracy Comparison of Generalized Born Models in the Calculation of Electrostatic Binding Free Energies.J Chem Theory Comput. 2018 Mar 13;14(3):1656-1670. doi: 10.1021/acs.jctc.7b00886. Epub 2018 Feb 15. J Chem Theory Comput. 2018. PMID: 29378399
-
Free energy landscape of protein folding in water: explicit vs. implicit solvent.Proteins. 2003 Nov 1;53(2):148-61. doi: 10.1002/prot.10483. Proteins. 2003. PMID: 14517967
-
Development and test of highly accurate endpoint free energy methods. 1: Evaluation of ABCG2 charge model on solvation free energy prediction and optimization of atom radii suitable for more accurate solvation free energy prediction by the PBSA method.J Comput Chem. 2023 May 30;44(14):1334-1346. doi: 10.1002/jcc.27089. Epub 2023 Feb 21. J Comput Chem. 2023. PMID: 36807356 Review.
-
Generalized Born Implicit Solvent Models for Biomolecules.Annu Rev Biophys. 2019 May 6;48:275-296. doi: 10.1146/annurev-biophys-052118-115325. Epub 2019 Mar 11. Annu Rev Biophys. 2019. PMID: 30857399 Free PMC article. Review.
Cited by
-
Molecular dynamics simulations as a guide for modulating small molecule aggregation.J Comput Aided Mol Des. 2024 Mar 12;38(1):11. doi: 10.1007/s10822-024-00557-1. J Comput Aided Mol Des. 2024. PMID: 38470532 Free PMC article.
-
Targeting the hSSB1-INTS3 Interface: A Computational Screening Driven Approach to Identify Potential Modulators.ACS Omega. 2024 Feb 8;9(7):8362-8373. doi: 10.1021/acsomega.3c09267. eCollection 2024 Feb 20. ACS Omega. 2024. PMID: 38405517 Free PMC article.
-
Accurate estimates of dynamical statistics using memory.J Chem Phys. 2024 Feb 28;160(8):084108. doi: 10.1063/5.0187145. J Chem Phys. 2024. PMID: 38391020
-
Oligo-PROTAC strategy for cell-selective and targeted degradation of activated STAT3.Mol Ther Nucleic Acids. 2024 Feb 5;35(1):102137. doi: 10.1016/j.omtn.2024.102137. eCollection 2024 Mar 12. Mol Ther Nucleic Acids. 2024. PMID: 38384444 Free PMC article.
-
Peptide-based inhibitors targeting the PD-1/PD-L1 axis: potential immunotherapeutics for cancer.Transl Oncol. 2024 Apr;42:101892. doi: 10.1016/j.tranon.2024.101892. Epub 2024 Feb 14. Transl Oncol. 2024. PMID: 38359715 Free PMC article.
References
-
- Feig M, Brooks CL. Recent advances in the development and application of implicit solvent models in biomolecule simulations. Curr. Opin. Struc. Biol. 2004;14(2):217–224. - PubMed
-
- Wang W, Donini O, Reyes CM, Kollman PA. BIOMOLECULAR SIMULATIONS: Recent Developments in Force Fields, Simulations of Enzyme Catalysis, Protein-Ligand, Protein-Protein, and Protein-Nucleic Acid Noncovalent Interactions. Annu. Rev. Bioph. Biom. 2001;30(1):211–243. - PubMed
-
- Zagrovic B, Pande V. Solvent viscosity dependence of the folding rate of a small protein: Distributed computing study. J. Comput. Chem. 2003;24(12):1432–1436. - PubMed
-
- Chen J;, III, C. L. B. Implicit modeling of nonpolar solvation for simulating protein folding and conformational transitions. Phys. Chem. Chem. Phys. 2008;10(4):471–481. - PubMed
- Levy RM, Zhang LY, Gallicchio E, Felts AK. On the Nonpolar Hydration Free Energy of Proteins: Surface Area and Continuum Solvent Models for the Solute−Solvent Interaction Energy. J. Am. Chem. Soc. 2003;125(31):9523–9530. - PubMed
- Wagoner JA, Baker NA. Assessing implicit models for nonpolar mean solvation forces: The importance of dispersion and volume terms. Proc. Natl. Acad. Sci. USA. 2006;103(22):8331–8336. - PMC - PubMed
- Chen J, Brooks CL. Critical Importance of Length-Scale Dependence in Implicit Modeling of Hydrophobic Interactions. J. Am. Chem. Soc. 2007;129(9):2444–2445. - PMC - PubMed
-
- Case DA, Darden TA, Cheatham TE, Simmerling CL, Wang J, Duke RE, Luo R, Crowley M, Walker RC, Zhang W, Merz KM, Wang B, Hayik S, Roitberg A, Seabra G, Kolossvary I, Wong KF, Paesani F, Vanicek J, Wu X, Brozell SR, Steinbrecher T, Gohlke H, Yang L, Tan C, Mongan J, Hornak V, Cui G, Mathews DH, Seetin MG, Sagui C, Babin V, Kollman PA. AMBER 10. 2008
Grants and funding
LinkOut - more resources
Full Text Sources
Other Literature Sources
Miscellaneous