Influence of the Method of Hydrogen Atoms Incorporation into the Target Protein on the Protein-Ligand Binding Energy

Preparation of the target-protein, particularly the protein protonation method can affect considerably the spatial arrangement of the attached hydrogen atoms and the charge state of individual molecular groups in amino acid residues. This means that the calculated protein-ligand binding energies can vary significantly depending on the method of the protein preparation, and it also can lead to the different docked positions of the ligand in the case of docking (positioning of the ligand in the protein active site). This work investigates the effect of the hydrogen atoms arrangement method in the target-protein on the protein-ligand binding energy. All hydrogen atoms of target-protein are fixed or movable. The comparison of the protein-ligand binding energies obtained for the test set of target-proteins prepared using six different programs is performed and it is shown that the protein-ligand binding energy depends significantly on the method of hydrogen atoms incorporation, and differences can reach $100$ kcal/mol. It is also shown that taking into account solvent in the frame of one of the two continuum implicit models smooths out these differences, but they are still about 10-20 kcal/mol. Moreover, we carried out the docking of the crystallized (native) ligands from the protein-ligand complexes using the SOL program and showed that the different methods of the hydrogen atoms addition to the protein can give significantly different results both for the positioning of the native ligand and for its protein-ligand binding energy.

Keywords

target-protein; crystal structure; protonation; protein-ligand binding energy; docking; force field.

References

1. Sadovnichii V.A., Sulimov V.B. Supercomputing Technologies in Medicine. In Supercomputing Technologies in Science, Education, and Industry, Moscow, Moscow University Publishing, 2009, pp. 16-23. (in Russian)
2. Protein Data Bank. Available at: http://www.rcsb.org/ (accessed October 27, 2016)
3. Berman H.M., Westbrook J., Feng Z., Gilliland G., Bhat T.N., Weissig H., Shindyalov I.N., Bourne P.E. The Protein Data Bank. Nucleic Acids Research, 2000, vol. 28, pp. 235-242. DOI: 10.1093/nar/28.1.235
4. Romanov A.N., Kondakova O.A., Grigoriev F.V., Sulimov A.V., Lushekina S.V., Martynov Y.B., Sulimov V.B. The SOL Docking Package for Computer-Aided Drug Design. Numerical Methods and Programming, 2008, vol. 9, no. 2, pp. 64-84. (in Russian)
5. Oferkin I.V., Sulimov A.V., Kondakova O.A., Sulimov V.B. Implementation of Parallel Computing for Docking Programs SOLGRID and SOL. Numerical Methods and Programming, 2011, vol. 12, no. 1, pp. 205-219. (in Russian)
6. Sulimov A.V., Kutov D.C., Oferkin I.V., Katkova E.V., Sulimov V.B. Application of the Docking Program SOL for CSAR Benchmark. Journal of Chemical Information and Modeling, 2013, 53, pp. 1946-1956.
7. Oferkin I.V., Sulimov A.V., Katkova E.V., Kutov D.C., Grigoriev F.V., Kondakova O.A., Sulimov V.B. Supercomputer Investigation of the Protein-Ligand System Low-Energy Minima. Biomeditsinskaya khimiya, 2015, vol. 61, no. 6, pp. 712-716. (in Russian)
8. Oferkin I.V., Katkova E.V., Sulimov A.V., Kutov D.C., Sobolev S.I., Voevodin V.V., Sulimov V.B. Evaluation of Docking Target Functions by the Comprehensive Investigation of Protein-Ligand Energy Minima. Advances in Bioinformatics, 2015, vol. 2015, Article ID 126858, 12 p. DOI: 10.1155/2015/126858
9. Sastry G.M., Adzhigirey M., Day T., Annabhimoju R., Sherman W. Protein and Ligand Preparation: Parameters, Protocols, and Influence on Virtual Screening Enrichments. Journal of Computer-Aided Molecular Design, 2013, vol. 27, no. 3, pp. 221-234.
10. Pettersen E.F., Goddard T.D., Huang C.C., Couch G.S., Greenblatt D.M., Meng E.C., Ferrin T.E. UCSF Chimera - a Visualization System for Exploratory Research and Analysis. Journal of Computational Chemistry, 2004, vol. 25, no. 13, pp. 1605-1612.
11. Word J.M., Lovell S.C., Richardson J.S., Richardson D.C. Asparagine and Glutamine: Using Hydrogen Atom Contacts in the Choice of Side-Chain Amide Orientation. Journal of Molecular Biology, 1999, vol. 285, no. 4, pp. 1735-1747.
12. Morris G.M., Huey R., Lindstrom W., Sanner M.F., Belew R.K., Goodsell D.S., Olson A.J. AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility. Journal of Computational Chemistry, 2009, vol. 16, pp. 2785-2791.
13. O'Boyle N.M., Banck M., James C.A., Morley C., Vandermeerschand T., Hutchison G.R. Open Babel: an Open Chemical Toolbox. Journal of Cheminformatics, 2011, vol. 3, article number 33, 14 p. DOI: 10.1186/1758-2946-3-33
14. Stewart J.J.P. Optimization of Parameters for Semiempirical Methods VI: More Modifications to the NDDO Approximations and Re-Optimization of Parameters. Journal of Molecular Biology, 2013, vol. 19, no. 1, pp. 1-32. DOI:10.1007/s00894-012-1667-x
15. Avogadro: an Open-Source Molecular Builder And Visualization Tool. Version 1.1.1. Available at: http://avogadro.openmolecules.net/ (accessed October 27, 2016).
16. Hanwell M.D., Curtis D.E., Lonie D.C., Vandermeersch T., Zurek E., Hutchison G.R. Avogadro: An Advanced Semantic Chemical Editor, Visualization, and Analysis Platform. Journal of Cheminformatics, 2012, vol. 4, 17 p.
17. Halgren T.A. Merck Molecular Force Field. I. Basis, Form, Scope, Parameterization, and Performance of MMFF94. Journal of Computational Chemistry, 1996, vol. 17, no. 5-6, pp. 490-641.
18. Romanov A.N., Jabin S.N., Martynov Y.B., Sulimov A.V., Grigoriev F.V., Sulimov V.B. Surface Generalized Bornmethod: a Simple, Fast and Precise Implicit Solvent Model Beyond the Coulomb Approximation. The Journal of Physical Chemistry A, 2004, vol. 108, pp. 9323-9327.
19. Kupervasser O.Yu., Zhabin S.N., Martynov Y.B., Fedulov C.M., Oferkin I.V., Sulimov A.V., Sulimov V.B. Continual Model of Solvent: the DISOLV Software-Algorithms, Implementation and Validation. Numerical Methods and Programming, 2011, vol. 12, pp. 246-261. (in Russian)
20. Mikhalev A.Y., Oferkin I.V., Oseledets I.V., Sulimov A.V., Tyrtyshnikov E.E., Sulimov V.B. Application of Themulticharge Approximation for Large Dense Matrices in the Framework of the Polarized Continuum Solvent Model. Numerical Methods and Programming, 2014, vol. 15, pp. 9-21. (in Russian)
21. Sulimov V.B., Mikhalev A.Yu., Oferkin I.V., Oseledets I.V., Sulimov A.V., Kutov D.C., Katkova E.V., Tyrtyshnikov E.E. Polarized Continuum Solvent Model: Considerable Acceleration with the Multicharge Matrix Approximation. International Journal of Applied Engineering Research, 2015, vol. 10, no. 24, pp. 44815-44830.
22. Oferkin I.V., Zheltkov D.A., Tyrtyshnikov E.E., Sulimov A.V., Kutov D.C., Sulimov V.B. Bulletin of the South Ural State University. Series: Mathematical Modelling, Programming and Computer Software, 2015, vol. 8, no. 4, pp. 83-99. DOI: 10.14529/mmp150407