Evaluating the Reliability of MM-PB/GB-SA Method for the Protein-Ligand Binding Free Energies Using Penicillopepsin-Inhibitor ligands

Authors

  • Twana Salih Department of Pharmacognosy & Pharmaceutical Chemistry, College of Pharmacy, University of Sulaimani, Iraq

DOI:

https://doi.org/10.32947/ajps.v22i3.889

Keywords:

Predicted binding free energy, Molecular Mechanics-Generalized Born Surface Area method, Molecular dynamic simulations, Correlation coefficient, root-mean-square deviation.

Abstract

An accurate prediction of the ligand-receptor binding free energies (ΔG) is a critical step in the early stages of rational drug design. The Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) method is a popular

 

approach to estimate ΔG. However, correlations between the predicted and the experimental ΔG are variable. The goal of this study is to investigate various approaches to optimize accuracy of the MM-GBSA method. A molecular dynamic (MD) simulations protocol was applied using penicillopepsin receptor against its inhibitor ligands, repeated 50 times for each complex system. After that, ΔG of the five inhibitors were predicted using MM-GBSA method. Moreover, a diverse ΔG values were calculated from the replicate MD simulations of each system. The results were showed correlations not only between the predicted and the experimental binding affinities of the systems but also between the predicted values and root-mean-square deviation. In addition, statistical analysis was evaluated the sample size.

References

- Huang K, Luo S, Cong Y, Zhong S, Zhang JZH, Duan L. An accurate free energy estimator: based on MM/PBSA combined with interaction entropy for protein-ligand binding affinity. Nanoscale. 2020;12(19) :10737-10750. DOI: https://doi.org/10.1039/C9NR10638C

- Weng G, Wang E, Chen F, Sun H, Wang Z, Hou T. Assessing the performance of MM/PBSA and MM/GBSA methods. 9. Prediction reliability of binding affinities and binding poses for protein-peptide complexes. Physical chemistry chemical physics: PCCP. 2019;21(19):10135-10145. DOI: https://doi.org/10.1039/C9CP01674K

- He X, Man VH, Ji B, Xie XQ, Wang J. Calculate protein-ligand binding affinities with the extended linear interaction energy method: application on the Cathepsin S set in the D3R Grand Challenge 3. J Comput Aided Mol Des. 2019;33(1):105-117. DOI: https://doi.org/10.1007/s10822-018-0162-6

- Ngo ST. Estimating the ligand‐binding affinity via λ‐dependent umbrella sampling simulations. J Comput Chem. 2021;42(2):117-123. DOI: https://doi.org/10.1002/jcc.26439

- Zou J, Tian C, Simmerling C. Blinded prediction of protein-ligand binding affinity using Amber thermodynamic integration for the 2018 D3R grand challenge 4. J Comput Aided Mol Des. 2019;33(12):1021-1029. DOI: https://doi.org/10.1007/s10822-019-00223-x

- Wang L, Chambers J, Abel R. Protein–ligand binding free energy calculations with FEP+. Biomolecular Simulations: Springer; 2019. p. 201-232. DOI: https://doi.org/10.1007/978-1-4939-9608-7_9

- Genheden S, Ryde U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert opinion on drug discovery. 2015;10(5):449-461. DOI: https://doi.org/10.1517/17460441.2015.1032936

- Wang E, Sun H, Wang J, Wang Z, Liu H, Zhang JZH, et al. End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. Chemical reviews. 2019;119(16):9478-9508. DOI: https://doi.org/10.1021/acs.chemrev.9b00055

- Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L, et al. Calculating Structures and Free Energies of Complex Molecules: Combining Molecular Mechanics and Continuum Models. Accounts of Chemical Research. 2000; 33:889-897. DOI: https://doi.org/10.1021/ar000033j

- Sun H, Li Y, Tian S, Xu L, Hou T. Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Physical chemistry chemical physics: PCCP. 2014;16(31):16719-16729. DOI: https://doi.org/10.1039/C4CP01388C

- Wang C, Greene D, Xiao L, Qi R, Luo R. Recent Developments and Applications of the MMPBSA Method. Front Mol Biosci. 2017; 4:87. DOI: https://doi.org/10.3389/fmolb.2017.00087

- Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG. A smooth particle mesh Ewald method. The Journal of Chemical Physics. 1995;103(19):8577. DOI: https://doi.org/10.1063/1.470117

- Wu X, Xu LY, Li EM, Dong G. Application of molecular dynamics simulation in biomedicine. Chemical biology & drug design. 2022. DOI: https://doi.org/10.1111/cbdd.14038

- Geng C, Xue LC, Roel‐Touris J, Bonvin AM. Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it? Wiley Interdisciplinary Reviews: Computational Molecular Science. 2019;9(5): e1410. DOI: https://doi.org/10.1002/wcms.1410

- Mishra SK, Koča J. Assessing the performance of MM/PBSA, MM/GBSA, and QM–MM/GBSA approaches on protein/carbohydrate complexes: Effect of implicit solvent models, QM methods, and entropic contributions. The Journal of Physical Chemistry B. 2018;122(34):8113-8121. DOI: https://doi.org/10.1021/acs.jpcb.8b03655

- Poli G, Granchi C, Rizzolio F, Tuccinardi T. Application of MM-PBSA Methods in Virtual Screening. Molecules. 2020;25(8). DOI: https://doi.org/10.3390/molecules25081971

- Salo-Ahen OMH, Alanko I, Bhadane R, Bonvin AMJJ, Honorato RV, Hossain S, et al. Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development. Processes. 2020;9(1). DOI: https://doi.org/10.3390/pr9010071

- Tuccinardi T. What is the current value of MM/PBSA and MM/GBSA methods in drug discovery? Expert opinion on drug discovery. 2021;16(11):1233-1237. DOI: https://doi.org/10.1080/17460441.2021.1942836

- Wan S, Bhati AP, Zasada SJ, Coveney PV. Rapid, accurate, precise and reproducible ligand-protein binding free energy prediction. Interface Focus. 2020;10(6):20200007. DOI: https://doi.org/10.1098/rsfs.2020.0007

- Hofmann T. [35] Penicillopepsin. Methods in Enzymology. 45: Academic Press; 1976. p. 434-452. DOI: https://doi.org/10.1016/S0076-6879(76)45038-3. DOI: https://doi.org/10.1016/S0076-6879(76)45038-3

- Chen F, Sun H, Wang J, Zhu F, Liu H, Wang Z, et al. Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes. RNA. 2018;24(9):1183-1194. DOI: https://doi.org/10.1261/rna.065896.118

- Patel H, Kukol A. Integrating molecular modelling methods to advance influenza A virus drug discovery. Drug discovery today. 2021;26(2):503-510. DOI: https://doi.org/10.1016/j.drudis.2020.11.014

- Liu X, Shi D, Zhou S, Liu H, Liu H, Yao X. Molecular dynamics simulations and novel drug discovery. Expert opinion on drug discovery. 2018;13(1):23-37. DOI: https://doi.org/10.1080/17460441.2018.1403419

- James M, Sielecki A, Moult J, Hruby V, Rich D, editors. Crystallographic analysis of a pepstatin analogue binding to the aspartyl proteinase penicillopepsin at 1.8 Angstroms resolution. Proceedings of the of the Eighth American Peptide Symposium; 1983.

- James MN, Sielecki AR, Hayakawa K, Gelb MH. Crystallographic analysis of transition state mimics binding to penicillopepsin: difluorostatine-and difluorostatone-containing peptides. Biochemistry. 1992;31(15):3872-3886. DOI: https://doi.org/10.1021/bi00130a019

- Ding J, Fraser ME, Meyer JH, Bartlett PA, James MN. Macrocyclic inhibitors of penicillopepsin. 2. X-ray crystallographic analyses of penicillopepsin complexed with a P3− P1 macrocyclic peptidyl inhibitor and with its two acyclic analogues. Journal of the American Chemical Society. 1998;120(19):4610-4621. DOI: https://doi.org/10.1021/ja973714r

- Humphrey W, Dalke A, Schulten K. VMD: Visual molecular dynamics. Journal of Molecular Graphics & Modelling. 1996;14(1):33-38. DOI: https://doi.org/10.1016/0263-7855(96)00018-5

- Wickstrom L, Okur A, Simmerling C. Evaluating the Performance of the ff99SB Force Field Based on NMR Scalar Coupling Data. Biophys J. 2009;97(3):853-856. DOI: https://doi.org/10.1016/j.bpj.2009.04.063

- Aqvist J. Ion-Water Interaction Potentials Derived from Free Energy Perturbation Simulations. J Phys Chem. 1990; 94:8021-8024. DOI: https://doi.org/10.1021/j100384a009

- Ryckaert JP, Ciccotti G, Berendsen HJC. NUMERICAL-INTEGRATION OF CARTESIAN EQUATIONS OF MOTION OF A SYSTEM WITH CONSTRAINTS - MOLECULAR-DYNAMICS OF N-ALKANES. J Comput Phys. 1977;23(3):327-341. DOI: https://doi.org/10.1016/0021-9991(77)90098-5

- Wu X, Brooks BR. Self-guided Langevin dynamics simulation method. Chemical Physics Letters. 2003;381(3-4):512-518. DOI: https://doi.org/10.1016/j.cplett.2003.10.013

- Miller BR, McGee TD, Swails JM, Homeyer N, Gohlke H, Roitberg AE. MMPBSA.py: An Efficient Program for End-State Free Energy Calculations. Journal of Chemical Theory and Computation. 2012;8(9):3314-3321. DOI: https://doi.org/10.1021/ct300418h

- Darden T, York D, Pedersen L. Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems. The Journal of Chemical Physics. 1993;98(12):10089. DOI: https://doi.org/10.1063/1.464397

- Case DA, Babin V, Berryman JT, Betz RM, Cai Q, Cerutti DS, et al. AMBER 14. University of California, San Francisco. 2014.

- Tian S, Zeng J, Liu X, Chen J, Zhang JZH, Zhu T. Understanding the selectivity of inhibitors toward PI4KIIIalpha and PI4KIIIbeta based molecular modeling. Physical chemistry chemical physics: PCCP. 2019;21(39):22103-22112. DOI: https://doi.org/10.1039/C9CP03598B

- Xu Z, Peng C, Shi Y, Zhu Z, Mu K, Wang X, et al. Nelfinavir was predicted to be a potential inhibitor of 2019-nCov main protease by an integrative approach combining homology modelling, molecular docking and binding free energy calculation. BioRxiv. 2020. DOI: https://doi.org/10.1101/2020.01.27.921627

- Knapp B, Ospina L, Deane CM. Avoiding False Positive Conclusions in Molecular Simulation: The Importance of Replicas. J Chem Theory Comput. 2018;14(12):6127-6138. DOI: https://doi.org/10.1021/acs.jctc.8b00391

- Hou TJ, Wang JM, Li YY, Wang W. Assessing the Performance of the MM/PBSA and MM/GBSA Methods. 1. The Accuracy of Binding Free Energy Calculations Based on Molecular Dynamics Simulations. Journal of Chemical Information and Modeling. 2011;51(1):69-82. DOI: https://doi.org/10.1021/ci100275a

- Salih T. Inhibiting protein–protein interactions in telomeres as an approach to cancer chemotherapy: University of Nottingham; 2016.

- Peat JK, Barton B, ebrary I. Medical statistics: a guide to data analysis and critical appraisal. 1st ed. Malden, Mass.: Blackwell Pub.; 2005. xii, 324 p. p. DOI: https://doi.org/10.1002/9780470755945

- Moore DS, Notz W, Fligner MA. The Basic Practice of Statistics. 2013. W.H. Freeman and Company; [15-24]. Available from: https://books.google.iq/books?id=aw61ygAACAAJ.

- Ramírez D, Caballero J. Is It Reliable to Take the Molecular Docking Top Scoring Position as the Best Solution without Considering Available Structural Data? Molecules (Basel, Switzerland). 2018;23(5):1038. DOI: https://doi.org/10.3390/molecules23051038

- E Lohning A, M Levonis S, Williams-Noonan B, S Schweiker S. A practical guide to molecular docking and homology modelling for medicinal chemists. Current topics in medicinal chemistry. 2017;17(18):2023-2040. DOI: https://doi.org/10.2174/1568026617666170130110827

- Perricone U, Gulotta MR, Lombino J, Parrino B, Cascioferro S, Diana P, et al. An overview of recent molecular dynamics applications as medicinal chemistry tools for the undruggable site challenge. MedChemComm. 2018;9(6):920-936. DOI: https://doi.org/10.1039/C8MD00166A

- Hou TJ, Wang JM, Li YY, Wang W. Assessing the Performance of the Molecular Mechanics/Poisson Boltzmann Surface Area and Molecular Mechanics/Generalized Born Surface Area Methods. II. The Accuracy of Ranking Poses Generated from Docking. J Comput Chem. 2011;32(5):866-877. DOI: https://doi.org/10.1002/jcc.21666

Downloads

Published

2022-10-24

How to Cite

Twana Salih. (2022). Evaluating the Reliability of MM-PB/GB-SA Method for the Protein-Ligand Binding Free Energies Using Penicillopepsin-Inhibitor ligands. Al Mustansiriyah Journal of Pharmaceutical Sciences, 22(3), 51–64. https://doi.org/10.32947/ajps.v22i3.889