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Journal articles on the topic 'Poisson-Boltzmann calculations'

Author: Grafiati

Published: 3 June 2025

Last updated: 20 July 2025

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1

Friedrichs, Mark, Ruhong Zhou, Shlomit R. Edinger, and Richard A. Friesner. "Poisson−Boltzmann Analytical Gradients for Molecular Modeling Calculations." Journal of Physical Chemistry B 103, no. 16 (1999): 3057–61. http://dx.doi.org/10.1021/jp982513m.

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2

Tjong, Harianto, and Huan-Xiang Zhou. "On the Dielectric Boundary in Poisson−Boltzmann Calculations." Journal of Chemical Theory and Computation 4, no. 3 (2008): 507–14. http://dx.doi.org/10.1021/ct700319x.

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3

Lo, Wai Yin, and Kwong‐Yu Chan. "Poisson–Boltzmann calculations of ions in charged capillaries." Journal of Chemical Physics 101, no. 2 (1994): 1431–34. http://dx.doi.org/10.1063/1.467767.

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4

Ivanović, Miloš T., Linda K. Bruetzel, Roman Shevchuk, Jan Lipfert, and Jochen S. Hub. "Quantifying the influence of the ion cloud on SAXS profiles of charged proteins." Physical Chemistry Chemical Physics 20, no. 41 (2018): 26351–61. http://dx.doi.org/10.1039/c8cp03080d.

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5

Pang, Xiaodong, and Huan-Xiang Zhou. "Poisson-Boltzmann Calculations: van der Waals or Molecular Surface?" Communications in Computational Physics 13, no. 1 (2013): 1–12. http://dx.doi.org/10.4208/cicp.270711.140911s.

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AbstractThe Poisson-Boltzmann equation is widely used for modeling the electro-statics of biomolecules, but the calculation results are sensitive to the choice of the boundary between the low solute dielectric and the high solvent dielectric. The default choice for the dielectric boundary has been the molecular surface, but the use of the van der Waals surface has also been advocated. Here we review recent studies in which the two choices are tested against experimental results and explicit-solvent calculations. The assignment of the solvent high dielectric constant to interstitial voids in the solute is often used as a criticism against the van der Waals surface. However, this assignment may not be as unrealistic as previously thought, since hydrogen exchange and other NMR experiments have firmly established that all interior parts of proteins are transiently accessible to the solvent.

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6

Wang, Jun, Qin Cai, Ye Xiang, and Ray Luo. "Reducing Grid Dependence in Finite-Difference Poisson–Boltzmann Calculations." Journal of Chemical Theory and Computation 8, no. 8 (2012): 2741–51. http://dx.doi.org/10.1021/ct300341d.

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7

Luo, Ray, Laurent David, and Michael K. Gilson. "Accelerated Poisson-Boltzmann calculations for static and dynamic systems." Journal of Computational Chemistry 23, no. 13 (2002): 1244–53. http://dx.doi.org/10.1002/jcc.10120.

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8

Morais, Pablo A., Francisco Franciné Maia, Christian Solis-Calero, Ewerton Wagner Santos Caetano, Valder Nogueira Freire, and Hernandes F. Carvalho. "The urokinase plasminogen activator binding to its receptor: a quantum biochemistry description within an in/homogeneous dielectric function framework with application to uPA–uPAR peptide inhibitors." Physical Chemistry Chemical Physics 22, no. 6 (2020): 3570–83. http://dx.doi.org/10.1039/c9cp06530j.

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DFT calculations using the MFCC fragment-based model considering a spatial-dependent dielectric function based on the Poisson–Boltzmann approximation were performed to describe the uPA–uPAR interactions.

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9

Li, Chuan, Lin Li, Marharyta Petukh, and Emil Alexov. "Progress in developing Poisson-Boltzmann equation solvers." Computational and Mathematical Biophysics 1 (March 21, 2013): 42–62. http://dx.doi.org/10.2478/mlbmb-2013-0002.

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AbstractThis review outlines the recent progress made in developing more accurate and efficient solutions to model electrostatics in systems comprised of bio-macromolecules and nanoobjects, the last one referring to objects that do not have biological function themselves but nowadays are frequently used in biophysical and medical approaches in conjunction with bio-macromolecules. The problem of modeling macromolecular electrostatics is reviewed from two different angles: as a mathematical task provided the specific definition of the system to be modeled and as a physical problem aiming to better capture the phenomena occurring in the real experiments. In addition, specific attention is paid to methods to extend the capabilities of the existing solvers to model large systems toward applications of calculations of the electrostatic potential and energies in molecular motors, mitochondria complex, photosynthetic machinery and systems involving large nanoobjects.

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10

Lamm, Gene, and George R. Pack. "Induced Coalescence of Cations through Low-Temperature Poisson-Boltzmann Calculations." Biophysical Journal 87, no. 2 (2004): 764–67. http://dx.doi.org/10.1529/biophysj.104.040220.

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11

Tovstun, Sergey A., and Vladimir F. Razumov. "Symmetry and stability of AOT reverse micelles: Poisson–Boltzmann calculations." Journal of Molecular Liquids 275 (February 2019): 578–85. http://dx.doi.org/10.1016/j.molliq.2018.11.117.

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12

Zakrzewska, Krystyna, Andrea Madami, and Richard Lavery. "Poisson-Boltzmann calculations for nucleic acids and nucleic acids complexes." Chemical Physics 204, no. 2-3 (1996): 263–69. http://dx.doi.org/10.1016/0301-0104(95)00345-2.

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13

Nguyen, Duc D., Bao Wang, and Guo-Wei Wei. "Accurate, robust, and reliable calculations of Poisson-Boltzmann binding energies." Journal of Computational Chemistry 38, no. 13 (2017): 941–48. http://dx.doi.org/10.1002/jcc.24757.

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14

Kar, Parimal, Yanjie Wei, Ulrich H. E. Hansmann, and Siegfried Höfinger. "Systematic study of the boundary composition in Poisson Boltzmann calculations." Journal of Computational Chemistry 28, no. 16 (2007): 2538–44. http://dx.doi.org/10.1002/jcc.20698.

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15

Xiao, Li, Changhao Wang, and Ray Luo. "Recent progress in adapting Poisson–Boltzmann methods to molecular simulations." Journal of Theoretical and Computational Chemistry 13, no. 03 (2014): 1430001. http://dx.doi.org/10.1142/s0219633614300018.

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Electrostatic solvation modeling based upon the Poisson–Boltzmann equation is widely used in studies of biomolecular structures and functions. This manuscript provides a thorough review of published efforts to adapt the numerical Poisson–Boltzmann methods to molecular simulations so that these methods can be extended to biomolecular studies involving conformational fluctuation and/or dynamics. We first review the fundamental works on how to define the electrostatic free energy and the Maxwell stress tensor. These topics are followed by three different strategies in developing algorithms to compute electrostatic forces and how to improve their numerical performance. Finally procedures are also presented in detail on how to discretize these algorithms for numerical calculations. Given the pioneer works reviewed here, further developmental efforts will be on how to balance efficiency and accuracy in these theoretical sound approaches — two important issues in applying any numerical algorithms for routine biomolecular applications. Even if not reviewed here, more advanced numerical solvers are certainly necessary to achieve higher accuracy than the widely used classical methods to improve the overall performance of the numerical Poisson–Boltzmann methods.

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16

Liu, Jinn-Liang, Dexuan Xie, and Bob Eisenberg. "Poisson-Fermi Formulation of Nonlocal Electrostatics in Electrolyte Solutions." Computational and Mathematical Biophysics 5, no. 1 (2017): 116–24. http://dx.doi.org/10.1515/mlbmb-2017-0007.

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Abstract We present a nonlocal electrostatic formulation of nonuniform ions and water molecules with interstitial voids that uses a Fermi-like distribution to account for steric and correlation efects in electrolyte solutions. The formulation is based on the volume exclusion of hard spheres leading to a steric potential and Maxwell’s displacement field with Yukawa-type interactions resulting in a nonlocal electric potential. The classical Poisson-Boltzmann model fails to describe steric and correlation effects important in a variety of chemical and biological systems, especially in high field or large concentration conditions found in and near binding sites, ion channels, and electrodes. Steric effects and correlations are apparent when we compare nonlocal Poisson-Fermi results to Poisson-Boltzmann calculations in electric double layer and to experimental measurements on the selectivity of potassium channels for K+ over Na+.

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17

Reis, Pedro B. P. S., Diogo Vila-Viçosa, Walter Rocchia, and Miguel Machuqueiro. "PypKa: A Flexible Python Module for Poisson–Boltzmann-Based pKa Calculations." Journal of Chemical Information and Modeling 60, no. 10 (2020): 4442–48. http://dx.doi.org/10.1021/acs.jcim.0c00718.

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18

Swanson, Jessica M. J., Stewart A. Adcock, and J. Andrew McCammon. "Optimized Radii for Poisson−Boltzmann Calculations with the AMBER Force Field." Journal of Chemical Theory and Computation 1, no. 3 (2005): 484–93. http://dx.doi.org/10.1021/ct049834o.

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19

Lamm, Gene, and George R. Pack. "Local dielectric constants and Poisson-Boltzmann calculations of DNA counterion distributions." International Journal of Quantum Chemistry 65, no. 6 (1997): 1087–93. http://dx.doi.org/10.1002/(sici)1097-461x(1997)65:6<1087::aid-qua7>3.0.co;2-r.

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20

Marshall, Shannon A., Christina L. Vizcarra, and Stephen L. Mayo. "One- and two-body decomposable Poisson-Boltzmann methods for protein design calculations." Protein Science 14, no. 5 (2005): 1293–304. http://dx.doi.org/10.1110/ps.041259105.

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21

Dolinsky, T. J., J. E. Nielsen, J. A. McCammon, and N. A. Baker. "PDB2PQR: an automated pipeline for the setup of Poisson-Boltzmann electrostatics calculations." Nucleic Acids Research 32, Web Server (2004): W665—W667. http://dx.doi.org/10.1093/nar/gkh381.

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22

Mouesca, Jean-Marie, Jun L. Chen, Louis Noodleman, Donald Bashford, and David A. Case. "Density Functional/Poisson-Boltzmann Calculations of Redox Potentials for Iron-Sulfur Clusters." Journal of the American Chemical Society 116, no. 26 (1994): 11898–914. http://dx.doi.org/10.1021/ja00105a033.

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23

Lu, Benzhuo, and J. Andrew McCammon. "Improved Boundary Element Methods for Poisson−Boltzmann Electrostatic Potential and Force Calculations." Journal of Chemical Theory and Computation 3, no. 3 (2007): 1134–42. http://dx.doi.org/10.1021/ct700001x.

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24

Hsieh, Meng-Juei, and Ray Luo. "Exploring a coarse-grained distributive strategy for finite-difference Poisson–Boltzmann calculations." Journal of Molecular Modeling 17, no. 8 (2010): 1985–96. http://dx.doi.org/10.1007/s00894-010-0904-4.

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25

Homeyer, Nadine, and Holger Gohlke. "Free Energy Calculations by the Molecular Mechanics Poisson−Boltzmann Surface Area Method." Molecular Informatics 31, no. 2 (2012): 114–22. http://dx.doi.org/10.1002/minf.201100135.

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26

Nielsen, Jens E., and Gerrit Vriend. "Optimizing the hydrogen-bond network in Poisson-Boltzmann equation-based pKa calculations." Proteins: Structure, Function, and Genetics 43, no. 4 (2001): 403–12. http://dx.doi.org/10.1002/prot.1053.

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27

Muhtar, Eldar, Mengyang Wang, and Haimei Zhu. "In silico discovery of SARS-CoV-2 main protease inhibitors from the carboline and quinoline database." Future Virology 16, no. 8 (2021): 507–18. http://dx.doi.org/10.2217/fvl-2021-0099.

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Aim: SARS-CoV-2 caused more than 3.8 million deaths according to the WHO. In this urgent circumstance, we aimed at screening out potential inhibitors targeting the main protease of SARS-CoV-2. Materials & methods: An in-house carboline and quinoline database including carboline, quinoline and their derivatives was established. A virtual screening in carboline and quinoline database, 50 ns molecular dynamics simulations and molecular mechanics Poisson−Boltzmann surface area calculations were carried out. Results: The top 12 molecules were screened out preliminarily. The molecular mechanics Poisson−Boltzmann surface area ranking showed that p59_7m, p12_7e, p59_7k stood out with the lowest binding energies of -24.20, -17.98 and -17.67 kcal/mol, respectively. Conclusion: The study provides powerful in silico results that indicate the selected molecules are valuable for further evaluation as SARS-CoV-2 main protease inhibitors.

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28

Womack, James C., Lucian Anton, Jacek Dziedzic, Phil J. Hasnip, Matt I. J. Probert, and Chris-Kriton Skylaris. "DL_MG: A Parallel Multigrid Poisson and Poisson–Boltzmann Solver for Electronic Structure Calculations in Vacuum and Solution." Journal of Chemical Theory and Computation 14, no. 3 (2018): 1412–32. http://dx.doi.org/10.1021/acs.jctc.7b01274.

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29

Löffler, Gerald. "Poisson-Boltzmann calculations versus molecular dynamics simulations for calculating the electrostatic potential of a solvated peptide." Theoretical Chemistry Accounts: Theory, Computation, and Modeling (Theoretica Chimica Acta) 101, no. 1-3 (1999): 163–69. http://dx.doi.org/10.1007/s002140050424.

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30

Bertonati, Claudia, Barry Honig, and Emil Alexov. "Poisson-Boltzmann Calculations of Nonspecific Salt Effects on Protein-Protein Binding Free Energies." Biophysical Journal 92, no. 6 (2007): 1891–99. http://dx.doi.org/10.1529/biophysj.106.092122.

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31

Tjong, Harianto, and Huang-Xiang Zhou. "The dependence of electrostatic solvation energy on dielectric constants in Poisson-Boltzmann calculations." Journal of Chemical Physics 125, no. 20 (2006): 206101. http://dx.doi.org/10.1063/1.2393243.

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32

Aleksandrov, Alexey, Benoît Roux, and Alexander D. MacKerell. "pKa Calculations with the Polarizable Drude Force Field and Poisson–Boltzmann Solvation Model." Journal of Chemical Theory and Computation 16, no. 7 (2020): 4655–68. http://dx.doi.org/10.1021/acs.jctc.0c00111.

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33

Fogolari, F., G. Manzini, and F. Quadrifoglio. "Polyelectrolytes in mixed salts: Scatchard plots obtained by means of Poisson-Boltzmann calculations." Biophysical Chemistry 43, no. 2 (1992): 213–19. http://dx.doi.org/10.1016/0301-4622(92)80035-4.

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34

Klingen, Astrid R., Hildur Palsdottir, Carola Hunte, and G. Matthias Ullmann. "Redox-linked protonation state changes in cytochrome bc1 identified by Poisson–Boltzmann electrostatics calculations." Biochimica et Biophysica Acta (BBA) - Bioenergetics 1767, no. 3 (2007): 204–21. http://dx.doi.org/10.1016/j.bbabio.2007.01.016.

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35

Fushiki, M., B. Svensson, Bo Jönsson, and C. E. Woodward. "Electrostatic interactions in protein solution-a comparison between poisson-boltzmann and Monte Carlo calculations." Biopolymers 31, no. 10 (1991): 1149–58. http://dx.doi.org/10.1002/bip.360311003.

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36

Lamm, Gene, Linda Wong, and George R. Pack. "Monte Carlo and Poisson-Boltzmann calculations of the fraction of counterions bound to DNA." Biopolymers 34, no. 2 (1994): 227–37. http://dx.doi.org/10.1002/bip.360340209.

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37

Pack, George R., Linda Wong, and Gene Lamm. "Divalent cations and the electrostatic potential around DNA: Monte Carlo and Poisson-Boltzmann calculations." Biopolymers 49, no. 7 (1999): 575–90. http://dx.doi.org/10.1002/(sici)1097-0282(199906)49:7<575::aid-bip4>3.0.co;2-j.

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38

Pack, George R., Linda Wong, and Gene Lamm. "PKa of cytosine on the third strand of triplex DNA: Preliminary Poisson-Boltzmann calculations." International Journal of Quantum Chemistry 70, no. 6 (1998): 1177–84. http://dx.doi.org/10.1002/(sici)1097-461x(1998)70:6<1177::aid-qua7>3.0.co;2-x.

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39

Votapka, Lane W., Luke Czapla, Maxim Zhenirovskyy, and Rommie E. Amaro. "DelEnsembleElec: Computing Ensemble-Averaged Electrostatics Using DelPhi." Communications in Computational Physics 13, no. 1 (2013): 256–68. http://dx.doi.org/10.4208/cicp.170711.111111s.

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AbstractA new VMD plugin that interfaces with DelPhi to provide ensemble-averaged electrostatic calculations using the Poisson-Boltzmann equation is presented. The general theory and context of this approach are discussed, and examples of the plugin interface and calculations are presented. This new tool is applied to systems of current biological interest, obtaining the ensemble-averaged electrostatic properties of the two major influenza virus glycoproteins, hemagglutinin and neuraminidase, from explicitly solvated all-atom molecular dynamics trajectories. The differences between the ensemble-averaged electrostatics and those obtained from a single structure are examined in detail for these examples, revealing how the plugin can be a powerful tool in facilitating the modeling of electrostatic interactions in biological systems.

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40

Watanabe, Hirofumi, Yoshio Okiyama, Tatsuya Nakano, and Shigenori Tanaka. "Incorporation of solvation effects into the fragment molecular orbital calculations with the Poisson–Boltzmann equation." Chemical Physics Letters 500, no. 1-3 (2010): 116–19. http://dx.doi.org/10.1016/j.cplett.2010.10.017.

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41

Martinez, Matias, Horacio Vargas-Guzman, and Christopher D. Cooper. "Implicit Solvent Calculations at Large-Scale Virus-Level Poisson-Boltzmann and Multiscale Simulations for Electrostatics." Biophysical Journal 116, no. 3 (2019): 291a. http://dx.doi.org/10.1016/j.bpj.2018.11.1574.

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42

Kocakaya, S. Ozhan. "BINDING AFFINITIES OF SANGGENON DERIVATIVES AS PTP1B INHIBITORS; USING MOLECULAR DYNAMICS AND FREE ENERGY CALCULATIONS." Azerbaijan Chemical Journal, no. 4 (November 14, 2023): 71–83. http://dx.doi.org/10.32737/0005-2531-2023-4-71-83.

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Recently, protein tyrosine phosphatase 1B (PTP1B) inhibitors have become the frontier as possible targeting for anti-cancer and antidiabetic drugs. The contemporary observe represents a pc assisted version to investigate the importance of precise residues within the binding web site of PTP1B with numerous Sanggenon derivatives remoted from nature. Molecular dynamics (MD) simulations were performed to estimate the dynamics of the complexes, and absolute binding unfastened energies have been calculated with exclusive additives, and carried out through the usage of the Molecular Mechanics-Poisson-Boltzmann floor region (MM-PB/SA) and Generalized Born surface vicinity (MM-GB/SA) strategies. The effects show that the expected free energies of the complexes are normally constant with the available experimental statistics. MM/GBSA free energy decomposition analysis shows that the residues Asp29, Arg24, Met258, and , Arg254 in the second active site in PTP1B are crucial for the excessive selectivity of the inhibitors

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43

Williams, Christopher D., Karl P. Travis, John H. Harding, and Neil A. Burton. "Selective Ordering of Pertechnetate at the Interface between Amorphous Silica and Water: a Poisson Boltzmann Treatment." MRS Proceedings 1744 (2015): 53–58. http://dx.doi.org/10.1557/opl.2015.298.

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ABSTRACTCalculations based on Poisson-Boltzmann theory are used to investigate the equilibrium properties of an electrolyte containing TcO4− and SO42− ions near the surface of amorphous silica. The calculations show that the concentration of TcO4− is greater than SO42− at distances less than 1 nm from the surface due to the negative charge density caused by deprotonation of the amorphous silica silanol groups. At lower pH, the surface becomes protonated and the magnitude of this effect is reduced. These results have implications for the potential use of oxyanion-SAMMS for the environmental remediation of water contaminated with 99Tc.

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44

Jang, Yun Hee, William A. Goddard, Katherine T. Noyes, Lawrence C. Sowers, Sungu Hwang, and Doo Soo Chung. "pKaValues of Guanine in Water: Density Functional Theory Calculations Combined with Poisson−Boltzmann Continuum−Solvation Model." Journal of Physical Chemistry B 107, no. 1 (2003): 344–57. http://dx.doi.org/10.1021/jp020774x.

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45

Bruccoleri, Robert E., Jiri Novotny, Malcolm E. Davis, and Kim A. Sharp. "Finite difference Poisson-Boltzmann electrostatic calculations: Increased accuracy achieved by harmonic dielectric smoothing and charge antialiasing." Journal of Computational Chemistry 18, no. 2 (1997): 268–76. http://dx.doi.org/10.1002/(sici)1096-987x(19970130)18:2<268::aid-jcc11>3.0.co;2-e.

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46

Wan, Shunzhou, Peter V. Coveney, and Darren R. Flower. "Peptide recognition by the T cell receptor: comparison of binding free energies from thermodynamic integration, Poisson–Boltzmann and linear interaction energy approximations." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 363, no. 1833 (2005): 2037–53. http://dx.doi.org/10.1098/rsta.2005.1627.

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The binding to the T cell receptor of wild-type and variant HTLV-1 Tax peptide complexed to the major histocompatibility complex has been investigated by means of molecular dynamics simulations. The binding free energy difference is calculated using the molecular mechanics Poisson–Boltzmann surface area and linear interaction energy methods. These methods extract useful information on the binding energetics from simulations of the physical states of the ligands, which are more computationally expedient than the commonly used thermodynamic integration method. The successful reproduction of the relative binding free energies shows that these methods can be useful for free energy calculations and the rational design of drugs and vaccines.

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47

Kwon, In, Gwanghyun Jo, and Kwang-Seong Shin. "A Deep Neural Network Based on ResNet for Predicting Solutions of Poisson–Boltzmann Equation." Electronics 10, no. 21 (2021): 2627. http://dx.doi.org/10.3390/electronics10212627.

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The Poisson–Boltzmann equation (PBE) arises in various disciplines including biophysics, electrochemistry, and colloid chemistry, leading to the need for efficient and accurate simulations of PBE. However, most of the finite difference/element methods developed so far are rather complicated to implement. In this study, we develop a ResNet-based artificial neural network (ANN) to predict solutions of PBE. Our networks are robust with respect to the locations of charges and shapes of solvent–solute interfaces. To generate train and test sets, we have solved PBE using immersed finite element method (IFEM) proposed in (Kwon, I.; Kwak, D. Y. Discontinuous bubble immersed finite element method for Poisson–Boltzmann equation. Communications in Computational Physics 2019, 25, pp. 928–946). Once the proposed ANNs are trained, one can predict solutions of PBE in almost real time by a simple substitution of information of charges/interfaces into the networks. Thus, our algorithms can be used effectively in various biomolecular simulations including ion-channeling simulations and calculations of diffusion-controlled enzyme reaction rate. The performance of the ANN is reported in the result section. The comparison between IFEM-generated solutions and network-generated solutions shows that root mean squared error are below 5·10−7. Additionally, blow-ups of electrostatic potentials near the singular charge region and abrupt decreases near the interfaces are represented in a reasonable way.

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48

Garcia, Danielle R., Felipe R. Souza, Ana P. Guimarães, et al. "In Silico Studies of Potential Selective Inhibitors of Thymidylate Kinase from Variola virus." Pharmaceuticals 14, no. 10 (2021): 1027. http://dx.doi.org/10.3390/ph14101027.

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Continuing the work developed by our research group, in the present manuscript, we performed a theoretical study of 10 new structures derived from the antivirals cidofovir and ribavirin, as inhibitor prototypes for the enzyme thymidylate kinase from Variola virus (VarTMPK). The proposed structures were subjected to docking calculations, molecular dynamics simulations, and free energy calculations, using the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method, inside the active sites of VarTMPK and human TMPK (HssTMPK). The docking and molecular dynamic studies pointed to structures 2, 3, 4, 6, and 9 as more selective towards VarTMPK. In addition, the free energy data calculated through the MM-PBSA method, corroborated these results. This suggests that these compounds are potential selective inhibitors of VarTMPK and, thus, can be considered as template molecules to be synthesized and experimentally evaluated against smallpox.

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49

Li, Anbang. "Performance comparison of Poisson–Boltzmann equation solvers DelPhi and PBSA in calculation of electrostatic solvation energies." Journal of Theoretical and Computational Chemistry 13, no. 05 (2014): 1450040. http://dx.doi.org/10.1142/s0219633614500400.

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Many Poisson–Boltzmann equation (PBE) solvers have been developed to calculate electrostatic energy of biomolecules, and each PBE solver has its advantages. In this study two PBE solvers — DelPhi and PBSA module in AMBER — are compared in terms of calculation results, convergence and program-running time by calculating the electrostatic solvation energy for a test set composed of 4 Kirkwood models and another test set of 25 protein structures. The protein structures were pretreated by AMBER and AMBER99SB force field parameters were used in all calculations. It is found that (i) At fine grids, both PBE solvers can produce accurate results on test set 1 and consistent results with each other on test set 2, with differences between each other varying from several to ~ 10 kcal/mol. (ii) Under convergence criterion "absolute value of relative error is less than or equal to 2% (|RE| ≤ 2%)", both PBE solvers need very fine grids to produce convergent results on small and complex Kirkwood models, while grid spacings of ≤ 0.5 Å–0.6 Å are well enough for them to achieve good convergent results on various molecular structures. We recommend users to adopt such grid spacing in using of these PBE solvers so as to get good enough convergent results. (iii) In terms of time consumption, DelPhi appears to be more time-saving than PBSA. In summary, according to our comparison, DelPhi and PBSA are paralleled good PBE solvers. The convergence of PBSA is a little better than DelPhi, while DelPhi exceeds PBSA in running speed.

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50

Ringe, Stefan, Harald Oberhofer, and Karsten Reuter. "Transferable ionic parameters for first-principles Poisson-Boltzmann solvation calculations: Neutral solutes in aqueous monovalent salt solutions." Journal of Chemical Physics 146, no. 13 (2017): 134103. http://dx.doi.org/10.1063/1.4978850.

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