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Journal articles on the topic 'MM-PBSA'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

Chen, Yuan-Qiang, Yan-Jing Sheng, Hong-Ming Ding, and Yu-Qiang Ma. "Evaluation on performance of MM/PBSA in nucleic acid-protein systems." Chinese Physics B 31, no. 4 (March 1, 2022): 048701. http://dx.doi.org/10.1088/1674-1056/ac3a5c.

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The molecular mechanics/Poisson–Boltzmann surface area (MM/PBSA) method has been widely used in predicting the binding affinity among ligands, proteins, and nucleic acids. However, the accuracy of the predicted binding energy by the standard MM/PBSA is not always good, especially in highly charged systems. In this work, we take the protein–nucleic acid complexes as an example, and showed that the use of screening electrostatic energy (instead of Coulomb electrostatic energy) in molecular mechanics can greatly improve the performance of MM/PBSA. In particular, the Pearson correlation coefficient of dataset II in the modified MM/PBSA (i.e., screening MM/PBSA) is about 0.52, much better than that (< 0.33) in the standard MM/PBSA. Further, we also evaluate the effect of solute dielectric constant and salt concentration on the performance of the screening MM/PBSA. The present study highlights the potential power of the screening MM/PBSA for predicting the binding energy in highly charged bio-systems.

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2

Sun, Huiyong, Youyong Li, Mingyun Shen, Sheng Tian, Lei Xu, Peichen Pan, Yan Guan, and Tingjun Hou. "Assessing the performance of MM/PBSA and MM/GBSA methods. 5. Improved docking performance using high solute dielectric constant MM/GBSA and MM/PBSA rescoring." Phys. Chem. Chem. Phys. 16, no. 40 (2014): 22035–45. http://dx.doi.org/10.1039/c4cp03179b.

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3

Sahakyan, Harutyun. "Improving virtual screening results with MM/GBSA and MM/PBSA rescoring." Journal of Computer-Aided Molecular Design 35, no. 6 (May 13, 2021): 731–36. http://dx.doi.org/10.1007/s10822-021-00389-3.

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4

Sun, Huiyong, Youyong Li, Sheng Tian, Lei Xu, and Tingjun Hou. "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." Phys. Chem. Chem. Phys. 16, no. 31 (2014): 16719–29. http://dx.doi.org/10.1039/c4cp01388c.

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Using different evaluation strategies, we systemically evaluated the performance of MM/GBSA and MM/PBSA methodologies based on more than 1800 protein–ligand crystal structures in the PDBbind database.

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5

Poli, Giulio, Carlotta Granchi, Flavio Rizzolio, and Tiziano Tuccinardi. "Application of MM-PBSA Methods in Virtual Screening." Molecules 25, no. 8 (April 23, 2020): 1971. http://dx.doi.org/10.3390/molecules25081971.

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Computer-aided drug design techniques are today largely applied in medicinal chemistry. In particular, receptor-based virtual screening (VS) studies, in which molecular docking represents the gold standard in silico approach, constitute a powerful strategy for identifying novel hit compounds active against the desired target receptor. Nevertheless, the need for improving the ability of docking in discriminating true active ligands from inactive compounds, thus boosting VS hit rates, is still pressing. In this context, the use of binding free energy evaluation approaches can represent a profitable tool for rescoring ligand-protein complexes predicted by docking based on more reliable estimations of ligand-protein binding affinities than those obtained with simple scoring functions. In the present review, we focused our attention on the Molecular Mechanics-Poisson Boltzman Surface Area (MM-PBSA) method for the calculation of binding free energies and its application in VS studies. We provided examples of successful applications of this method in VS campaigns and evaluation studies in which the reliability of this approach has been assessed, thus providing useful guidelines for employing this approach in VS.

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6

Thompson, David C., Christine Humblet, and Diane Joseph-McCarthy. "Investigation of MM-PBSA Rescoring of Docking Poses." Journal of Chemical Information and Modeling 48, no. 5 (May 2008): 1081–91. http://dx.doi.org/10.1021/ci700470c.

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7

Martins, Sílvia A., Marta A. S. Perez, Irina S. Moreira, Sérgio F. Sousa, M. J. Ramos, and P. A. Fernandes. "Computational Alanine Scanning Mutagenesis: MM-PBSA vs TI." Journal of Chemical Theory and Computation 9, no. 3 (February 25, 2013): 1311–19. http://dx.doi.org/10.1021/ct4000372.

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8

Genheden, Samuel, and Ulf Ryde. "The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities." Expert Opinion on Drug Discovery 10, no. 5 (April 2, 2015): 449–61. http://dx.doi.org/10.1517/17460441.2015.1032936.

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9

Fogolari, Federico, Alessandro Brigo, and Henriette Molinari. "Protocol for MM/PBSA Molecular Dynamics Simulations of Proteins." Biophysical Journal 85, no. 1 (July 2003): 159–66. http://dx.doi.org/10.1016/s0006-3495(03)74462-2.

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10

Kawatkar, Sameer, Demetri Moustakas, Matthew Miller, and Diane Joseph-McCarthy. "Virtual fragment screening: exploration of MM-PBSA re-scoring." Journal of Computer-Aided Molecular Design 26, no. 8 (August 2012): 921–34. http://dx.doi.org/10.1007/s10822-012-9590-x.

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11

Virtanen, Salla I., Sanna P. Niinivehmas, and Olli T. Pentikäinen. "Case-specific performance of MM-PBSA, MM-GBSA, and SIE in virtual screening." Journal of Molecular Graphics and Modelling 62 (November 2015): 303–18. http://dx.doi.org/10.1016/j.jmgm.2015.10.012.

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12

Beà, Ivan, Martin G. Gotsev, Petko M. Ivanov, Carlos Jaime, and Peter A. Kollman. "Chelate Effect in Cyclodextrin Dimers: A Computational (MD, MM/PBSA, and MM/GBSA) Study." Journal of Organic Chemistry 71, no. 5 (March 2006): 2056–63. http://dx.doi.org/10.1021/jo052469o.

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13

Wei, Haixin, Zekai Zhao, and Ray Luo. "Advancing MM/PBSA calculations with machine learning and cuda GPUs." Biophysical Journal 121, no. 3 (February 2022): 527a—528a. http://dx.doi.org/10.1016/j.bpj.2021.11.2779.

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14

Kumari, Rashmi, Rajendra Kumar, and Andrew Lynn. "g_mmpbsa—A GROMACS Tool for High-Throughput MM-PBSA Calculations." Journal of Chemical Information and Modeling 54, no. 7 (June 19, 2014): 1951–62. http://dx.doi.org/10.1021/ci500020m.

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15

Byun, Jinyoung, and Juyong Lee. "Identifying the Hot Spot Residues of the SARS-CoV-2 Main Protease Using MM-PBSA and Multiple Force Fields." Life 12, no. 1 (December 31, 2021): 54. http://dx.doi.org/10.3390/life12010054.

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In this study, we investigated the binding affinities between the main protease of SARS-CoV-2 virus (Mpro) and its various ligands to identify the hot spot residues of the protease. To benchmark the influence of various force fields on hot spot residue identification and binding free energy calculation, we performed MD simulations followed by MM-PBSA analysis with three different force fields: CHARMM36, AMBER99SB, and GROMOS54a7. We performed MD simulations with 100 ns for 11 protein–ligand complexes. From the series of MD simulations and MM-PBSA calculations, it is identified that the MM-PBSA estimations using different force fields are weakly correlated to each other. From a comparison between the force fields, AMBER99SB and GROMOS54a7 results are fairly correlated while CHARMM36 results show weak or almost no correlations with the others. Our results suggest that MM-PBSA analysis results strongly depend on force fields and should be interpreted carefully. Additionally, we identified the hot spot residues of Mpro, which play critical roles in ligand binding through energy decomposition analysis. It is identified that the residues of the S4 subsite of the binding site, N142, M165, and R188, contribute strongly to ligand binding. In addition, the terminal residues, D295, R298, and Q299 are identified to have attractive interactions with ligands via electrostatic and solvation energy. We believe that our findings will help facilitate developing the novel inhibitors of SARS-CoV-2.

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16

Karlov, Dmitry S., Mstislav I. Lavrov, Vladimir A. Palyulin, and Nikolay S. Zefirov. "MM-GBSA and MM-PBSA performance in activity evaluation of AMPA receptor positive allosteric modulators." Journal of Biomolecular Structure and Dynamics 36, no. 10 (August 10, 2017): 2508–16. http://dx.doi.org/10.1080/07391102.2017.1360208.

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17

Bea, I., E. cervello, P. Kollman, and C. jaime. "Molecular Recognition by beta-Cyclodextrin Derivatives: FEP vs MM / PBSA Study." Combinatorial Chemistry & High Throughput Screening 4, no. 8 (December 1, 2001): 605–11. http://dx.doi.org/10.2174/1386207013330689.

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18

Zhu, Yu-Xin, Yan-Jing Sheng, Yu-Qiang Ma, and Hong-Ming Ding. "Assessing the Performance of Screening MM/PBSA in Protein–Ligand Interactions." Journal of Physical Chemistry B 126, no. 8 (February 21, 2022): 1700–1708. http://dx.doi.org/10.1021/acs.jpcb.1c09424.

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19

Yam, Wai Keat, and Habibah A. Wahab. "Molecular Insights into 14-Membered Macrolides Using the MM-PBSA Method." Journal of Chemical Information and Modeling 49, no. 6 (May 26, 2009): 1558–67. http://dx.doi.org/10.1021/ci8003495.

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20

Kuhn, Bernd, Paul Gerber, Tanja Schulz-Gasch, and Martin Stahl. "Validation and Use of the MM-PBSA Approach for Drug Discovery." Journal of Medicinal Chemistry 48, no. 12 (June 2005): 4040–48. http://dx.doi.org/10.1021/jm049081q.

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21

Page, Christopher S., and Paul A. Bates. "Can MM-PBSA calculations predict the specificities of protein kinase inhibitors?" Journal of Computational Chemistry 27, no. 16 (2006): 1990–2007. http://dx.doi.org/10.1002/jcc.20534.

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22

Uciechowska, Urszula, Jörg Schemies, Michael Scharfe, Michael Lawson, Kanin Wichapong, Manfred Jung, and Wolfgang Sippl. "Binding free energy calculations and biological testing of novel thiobarbiturates as inhibitors of the human NAD+ dependent histone deacetylase Sirt2." MedChemComm 3, no. 2 (2012): 167–73. http://dx.doi.org/10.1039/c1md00214g.

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23

Mikurova, A. V., and V. S. Skvortsov. "Prediction of Progestin Affinity for the Human Progesterone Receptor Based on Corrected RBA Data." Biomedical Chemistry: Research and Methods 1, no. 4 (2018): e00080. http://dx.doi.org/10.18097/bmcrm00080.

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The modeling of complexes of 3 sets of steroid and nonsteroidal progestins with the ligand-binding domain of the nuclear progesterone receptor was performed. Molecular docking procedure, long-term simulation of molecular dynamics and subsequent analysis by MM-PBSA (MM-GBSA) were used to model the complexes. Using the characteristics obtained by the MM-PBSA method two data sets of steroid compounds obtained in different scientific groups a prediction equation for the value of relative binding activity (RBA) was constructed. The RBA value was adjusted so that in all samples the actual activity was compared with the progesterone activity. The third data set of nonsteroidal compounds was used as a test. The resulted equation showed that the prediction results could be applied to both steroid molecules and nonsteroidal progestins.

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24

El Hassab, Mahmoud A., Tamer M. Ibrahim, Aly A. Shoun, Sara T. Al-Rashood, Hamad M. Alkahtani, Amal Alharbi, Razan O. Eskandrani, and Wagdy M. Eldehna. "In silico identification of potential SARS COV-2 2′-O-methyltransferase inhibitor: fragment-based screening approach and MM-PBSA calculations." RSC Advances 11, no. 26 (2021): 16026–33. http://dx.doi.org/10.1039/d1ra01809d.

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25

Fang, Liang, Xiaojian Wang, Meiyang Xi, Tianqi Liu, and Dali Yin. "Assessing the ligand selectivity of sphingosine kinases using molecular dynamics and MM-PBSA binding free energy calculations." Molecular BioSystems 12, no. 4 (2016): 1174–82. http://dx.doi.org/10.1039/c6mb00067c.

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26

Weng, Gaoqi, Ercheng Wang, Fu Chen, Huiyong Sun, Zhe Wang, and Tingjun Hou. "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 21, no. 19 (2019): 10135–45. http://dx.doi.org/10.1039/c9cp01674k.

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Determination of protein–peptide interactions is critical to gain an in-depth understanding of the protein–protein interaction network. Computational approaches, especially MM/PBSA and MM/GBSA, are powerful tools to predict the binding affinities and identify the correct binding poses for protein–peptide systems.

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27

Huang, Kaifang, Song Luo, Yalong Cong, Susu Zhong, John Z. H. Zhang, and Lili Duan. "An accurate free energy estimator: based on MM/PBSA combined with interaction entropy for protein–ligand binding affinity." Nanoscale 12, no. 19 (2020): 10737–50. http://dx.doi.org/10.1039/c9nr10638c.

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28

Zhou, Zhi-Guang, Qi-Zheng Yao, Dong Lei, Qing-Qing Zhang, and Ji Zhang. "Investigations on the mechanisms of interactions between matrix metalloproteinase 9 and its flavonoid inhibitors using a combination of molecular docking, hybrid quantum mechanical/molecular mechanical calculations, and molecular dynamics simulations." Canadian Journal of Chemistry 92, no. 9 (September 2014): 821–30. http://dx.doi.org/10.1139/cjc-2014-0180.

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Many experimental studies have found that flavonoids can inhibit the activities of matrix metalloproteinases (MMPs), but the relevant mechanisms are still unclear. In this paper, the interaction mechanisms of MMP-9 with its five flavonoid inhibitors are investigated using a combination of molecular docking, hybrid quantum mechanical and molecular mechanical (QM/MM) calculations, and molecular dynamics simulations. The molecular dynamics simulation results show a good linear correlation between the calculated binding free energies of QM/MM−Poisson–Boltzmann surface area (PBSA) and the experimental −log(EC50) regarding the studied five flavonoids on MMP-9 inhibition in explicit solvent. It is found that compared with the MM−PBSA method, the QM/MM−PBSA method can obviously improve the accuracy for the calculated binding free energies. The predicted binding modes of the five flavonoid−MMP-9 complexes reveal that the different hydrogen bond networks can form besides producing the Zn−O coordination bonds, which can reasonably explain previous experimental results. The agreement between our calculated results and the previous experimental facts indicates that the force field parameters used here are effective and reliable for investigating the systems of flavonoid−MMP-9 interactions, and thus, these simulations and analyses could be reproduced for the other related systems involving protein−ligand interactions. This paper may be helpful for designing the new MMP-9 inhibitors having higher biological activities by carrying out the structural modifications of flavonoid molecules.

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29

Wang, Rui-Ge, Hong-Xing Zhang, and Qing-Chuan Zheng. "Revealing the binding and drug resistance mechanism of amprenavir, indinavir, ritonavir, and nelfinavir complexed with HIV-1 protease due to double mutations G48T/L89M by molecular dynamics simulations and free energy analyses." Physical Chemistry Chemical Physics 22, no. 8 (2020): 4464–80. http://dx.doi.org/10.1039/c9cp06657h.

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30

Kaukonen, Markus, Pär Söderhjelm, Jimmy Heimdal, and Ulf Ryde. "QM/MM−PBSA Method To Estimate Free Energies for Reactions in Proteins." Journal of Physical Chemistry B 112, no. 39 (October 2, 2008): 12537–48. http://dx.doi.org/10.1021/jp802648k.

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31

Adekoya, Olayiwola A., Nils-Peder Willassen, and Ingebrigt Sylte. "Molecular insight into pseudolysin inhibition using the MM-PBSA and LIE methods." Journal of Structural Biology 153, no. 2 (February 2006): 129–44. http://dx.doi.org/10.1016/j.jsb.2005.11.003.

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32

Horoiwa, Shinri, Taiyo Yokoi, Satoru Masumoto, Saki Minami, Chiharu Ishizuka, Hidetoshi Kishikawa, Shunsuke Ozaki, Shigeki Kitsuda, Yoshiaki Nakagawa, and Hisashi Miyagawa. "Structure-based virtual screening for insect ecdysone receptor ligands using MM/PBSA." Bioorganic & Medicinal Chemistry 27, no. 6 (March 2019): 1065–75. http://dx.doi.org/10.1016/j.bmc.2019.02.011.

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33

Zhu, Jingxuan, Xin Li, Siqi Zhang, Hen Ye, Hui Zhao, Hanyong Jin, and Weiwei Han. "Exploring stereochemical specificity of phosphotriesterase by MM-PBSA and MM-GBSA calculation and steered molecular dynamics simulation." Journal of Biomolecular Structure and Dynamics 35, no. 14 (October 28, 2016): 3140–51. http://dx.doi.org/10.1080/07391102.2016.1244494.

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34

Jamshidi, Shirin, Hashem Rafii-Tabar, and Seifollah Jalili. "Investigation into mechanism of orotidine 5′-monophosphate decarboxylase enzyme by MM-PBSA/MM-GBSA and molecular docking." Molecular Simulation 40, no. 6 (July 23, 2013): 469–76. http://dx.doi.org/10.1080/08927022.2013.819579.

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35

Wang, Lei, Qi-Chao Bao, Xiao-Li Xu, Fen Jiang, Kai Gu, Zheng-Yu Jiang, Xiao-Jin Zhang, Xiao-Ke Guo, Qi-Dong You, and Hao-Peng Sun. "Discovery and identification of Cdc37-derived peptides targeting the Hsp90–Cdc37 protein–protein interaction." RSC Advances 5, no. 116 (2015): 96138–45. http://dx.doi.org/10.1039/c5ra20408a.

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In order to explore the key residues of the Hsp90–Cdc37 binding interface for further design of peptide inhibitors, a combined strategy of molecular dynamics simulation and MM-PBSA analysis was performed.

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36

Garcia, Danielle R., Felipe R. Souza, Ana P. Guimarães, Martin Valis, Zbyšek Pavelek, Kamil Kuca, Teodorico C. Ramalho, and Tanos C. C. França. "In Silico Studies of Potential Selective Inhibitors of Thymidylate Kinase from Variola virus." Pharmaceuticals 14, no. 10 (October 9, 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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37

Cebrián-Prats, Anna, Tiffani Rovira, Patricia Saura, Àngels González-Lafont, and José M. Lluch. "Inhibition of Mammalian 15-Lipoxygenase by Three Ebselen-like Drugs. A QM/MM and MM/PBSA Comparative Study." Journal of Physical Chemistry A 121, no. 51 (December 14, 2017): 9752–63. http://dx.doi.org/10.1021/acs.jpca.7b10416.

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38

Wang, Ercheng, Huiyong Sun, Junmei Wang, Zhe Wang, Hui Liu, John Z. H. Zhang, and Tingjun Hou. "End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design." Chemical Reviews 119, no. 16 (June 24, 2019): 9478–508. http://dx.doi.org/10.1021/acs.chemrev.9b00055.

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39

Karnati, Konda Reddy, Yixuan Wang, and Yongli Du. "Exploring the binding mode and thermodynamics of inverse agonists against estrogen-related receptor alpha." RSC Advances 10, no. 28 (2020): 16659–68. http://dx.doi.org/10.1039/c9ra10697a.

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All-atom MD simulations were for the first time carried out for the complexes of inverse agonists and ERRα, and their binding free energies were also calculated with MM-PBSA to quantitatively discuss the binding of the inverse agonists with ERRα.

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40

Zhu, Tian, Hyun Lee, Hao Lei, Christopher Jones, Kavankumar Patel, Michael E. Johnson, and Kirk E. Hevener. "Fragment-Based Drug Discovery Using a Multidomain, Parallel MD-MM/PBSA Screening Protocol." Journal of Chemical Information and Modeling 53, no. 3 (March 14, 2013): 560–72. http://dx.doi.org/10.1021/ci300502h.

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41

Paissoni, C., D. Spiliotopoulos, G. Musco, and A. Spitaleri. "GMXPBSA 2.0: A GROMACS tool to perform MM/PBSA and computational alanine scanning." Computer Physics Communications 185, no. 11 (November 2014): 2920–29. http://dx.doi.org/10.1016/j.cpc.2014.06.019.

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42

Paissoni, C., D. Spiliotopoulos, G. Musco, and A. Spitaleri. "GMXPBSA 2.1: A GROMACS tool to perform MM/PBSA and computational alanine scanning." Computer Physics Communications 186 (January 2015): 105–7. http://dx.doi.org/10.1016/j.cpc.2014.09.010.

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43

Kongsted, Jacob, and Ulf Ryde. "An improved method to predict the entropy term with the MM/PBSA approach." Journal of Computer-Aided Molecular Design 23, no. 2 (September 10, 2008): 63–71. http://dx.doi.org/10.1007/s10822-008-9238-z.

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44

Wang, Xiaowen, and Wenjin Li. "Comparative Study of Interactions between Human cGAS and Inhibitors: Insights from Molecular Dynamics and MM/PBSA Studies." International Journal of Molecular Sciences 22, no. 3 (January 25, 2021): 1164. http://dx.doi.org/10.3390/ijms22031164.

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Recent studies have identified cyclic GMP-AMP synthase (cGAS) as an important target for treating autoimmune diseases, and several inhibitors of human cGAS (hcGAS) and their structures in complexation with hcGAS have been reported. However, the mechanisms via which these inhibitors interact with hcGAS are not completely understood. Here, we aimed to assess the performance of molecular mechanics/Poisson–Boltzmann solvent-accessible surface area (MM/PBSA) in evaluating the binding affinity of various hcGAS inhibitors and to elucidate their detailed interactions with hcGAS from an energetic viewpoint. Using molecular dynamics (MD) simulation and MM/PBSA approaches, the estimated free energies were in good agreement with the experimental ones, with a Pearson’s correlation coefficient and Spearman’s rank coefficient of 0.67 and 0.46, respectively. In per-residue energy decomposition analysis, four residues, K362, R376, Y436, and K439 in hcGAS were found to contribute significantly to the binding with inhibitors via hydrogen bonding, salt bridges, and various π interactions, such as π· · ·π stacking, cation· · ·π, hydroxyl· · ·π, and alkyl· · ·π interactions. In addition, we discussed other key interactions between specific residues and ligands, in particular, between H363 and JUJ, F379 and 9BY, and H437 and 8ZM. The sandwiched structures of the inhibitor bound to the guanidinium group of R376 and the phenyl ring of Y436 were also consistent with the experimental data. The results indicated that MM/PBSA in combination with other virtual screening methods, could be a reliable approach to discover new hcGAS inhibitors and thus is valuable for potential treatments of cGAS-dependent inflammatory diseases.

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45

Zhou, Boqian, Yongguang Zhang, Wanyun Jiang, and Haiyang Zhang. "Virtual Screening of FDA-Approved Drugs for Enhanced Binding with Mitochondrial Aldehyde Dehydrogenase." Molecules 27, no. 24 (December 10, 2022): 8773. http://dx.doi.org/10.3390/molecules27248773.

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Mitochondrial aldehyde dehydrogenase (ALDH2) is a potential target for the treatment of substance use disorders such as alcohol addiction. Here, we adopted computational methods of molecular dynamics (MD) simulation, docking, and molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) analysis to perform a virtual screening of FDA-approved drugs, hitting potent inhibitors against ALDH2. Using MD-derived conformations as receptors, butenafine (net charge q = +1 e) and olaparib (q = 0) were selected as promising compounds with a low toxicity and a binding strength equal to or stronger than previously reported potent inhibitors of daidzin and CVT-10216. A few negatively charged compounds were also hit from the docking with the Autodock Vina software, while the MM-PBSA analysis yielded positive binding energies (unfavorable binding) for these compounds, mainly owing to electrostatic repulsion in association with a negatively charged receptor (q = −6 e for ALDH2 plus the cofactor NAD+). This revealed a deficiency of the Vina scoring in dealing with strong charge–charge interactions between binding partners, due to its built-in protocol of not using atomic charges for electrostatic interactions. These observations indicated a requirement of further verification using MD and/or MM-PBSA after docking prediction. The identification of key residues for the binding implied that the receptor residues at the bottom and entrance of the substrate-binding hydrophobic tunnel were able to offer additional interactions with different inhibitors such as π-π, π-alkyl, van der Waals contacts, and polar interactions, and that the rational use of these interactions is beneficial to the design of potent inhibitors against ALDH2.

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46

Zazeri, Gabriel, Ana Paula Ribeiro Povinelli, Marcelo de Freitas Lima, and Marinônio Lopes Cornélio. "Detailed Characterization of the Cooperative Binding of Piperine with Heat Shock Protein 70 by Molecular Biophysical Approaches." Biomedicines 8, no. 12 (December 18, 2020): 629. http://dx.doi.org/10.3390/biomedicines8120629.

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In this work, for the first time, details of the complex formed by heat shock protein 70 (HSP70) independent nucleotide binding domain (NBD) and piperine were characterized through experimental and computational molecular biophysical methods. Fluorescence spectroscopy results revealed positive cooperativity between the two binding sites. Circular dichroism identified secondary conformational changes. Molecular dynamics along with molecular mechanics Poisson Boltzmann surface area (MM/PBSA) reinforced the positive cooperativity, showing that the affinity of piperine for NBD increased when piperine occupied both binding sites instead of one. The spontaneity of the complexation was demonstrated through the Gibbs free energy (∆G < 0 kJ/mol) for different temperatures obtained experimentally by van’t Hoff analysis and computationally by umbrella sampling with the potential of mean force profile. Furthermore, the mean forces which drove the complexation were disclosed by van’t Hoff and MM/PBSA as being the non-specific interactions. In conclusion, the work revealed characteristics of NBD and piperine interaction, which may support further drug discover studies.

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Sgobba, Miriam, Fabiana Caporuscio, Andrew Anighoro, Corinne Portioli, and Giulio Rastelli. "Application of a post-docking procedure based on MM-PBSA and MM-GBSA on single and multiple protein conformations." European Journal of Medicinal Chemistry 58 (December 2012): 431–40. http://dx.doi.org/10.1016/j.ejmech.2012.10.024.

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Ismail, Saba, Sumra Wajid Abbasi, Maha Yousaf, Sajjad Ahmad, Khalid Muhammad, and Yasir Waheed. "Design of a Multi-Epitopes Vaccine against Hantaviruses: An Immunoinformatics and Molecular Modelling Approach." Vaccines 10, no. 3 (February 28, 2022): 378. http://dx.doi.org/10.3390/vaccines10030378.

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Hantaviruses are negative-sense, enveloped, single-stranded RNA viruses of the family Hantaviridae. In recent years, rodent-borne hantaviruses have emerged as novel zoonotic viruses posing a substantial health issue and socioeconomic burden. In the current research, a reverse vaccinology approach was applied to design a multi-epitope-based vaccine against hantavirus. A set of 340 experimentally reported epitopes were retrieved from Virus Pathogen Database and Analysis Resource (ViPR) and subjected to different analyses such as antigenicity, allergenicity, solubility, IFN gamma, toxicity, and virulent checks. Finally, 10 epitopes which cleared all the filters used were linked with each other through specific GPGPG linkers to construct a multi-antigenic epitope vaccine. The designed vaccine was then joined to three different adjuvants—TLR4-agonist adjuvant, β-defensin, and 50S ribosomal protein L7/L12—using an EAAAK linker to boost up immune-stimulating responses and check the potency of vaccine with each adjuvant. The designed vaccine structures were modelled and subjected to error refinement and disulphide engineering to enhance their stability. To understand the vaccine binding affinity with immune cell receptors, molecular docking was performed between the designed vaccines and TLR4; the docked complex with a low level of global energy was then subjected to molecular dynamics simulations to validate the docking results and dynamic behaviour. The docking binding energy of vaccines with TLR4 is −29.63 kcal/mol (TLR4-agonist), −3.41 kcal/mol (β-defensin), and −11.03 kcal/mol (50S ribosomal protein L7/L12). The systems dynamics revealed all three systems to be highly stable with a root-mean-square deviation (RMSD) value within 3 Å. To test docking predictions and determine dominant interaction energies, binding free energies of vaccine(s)–TLR4 complexes were calculated. The net binding energy of the systems was as follows: TLR4-agonist vaccine with TLR4 (MM–GBSA, −1628.47 kcal/mol and MM–PBSA, −37.75 kcal/mol); 50S ribosomal protein L7/L12 vaccine with TLR4 complex (MM–GBSA, −194.62 kcal/mol and MM–PBSA, −150.67 kcal/mol); β-defensin vaccine with TLR4 complex (MM–GBSA, −9.80 kcal/mol and MM–PBSA, −42.34 kcal/mol). Finally, these findings may aid experimental vaccinologists in developing a very potent hantavirus vaccine.

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Federico, Leonardo Bruno, Guilherme Martins Silva, Suzane Quintana Gomes, Isaque Antonio Galindo Francischini, Mariana Pegrucci Barcelos, Cleydson Breno Rodrigues dos Santos, Luciano T. Costa, Joaquín María Campos Rosa, and Carlos Henrique Tomich de Paula da Silva. "Potential colchicine binding site inhibitors unraveled by virtual screening, molecular dynamics and MM/PBSA." Computers in Biology and Medicine 137 (October 2021): 104817. http://dx.doi.org/10.1016/j.compbiomed.2021.104817.

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Zhu, Yong-Liang, Paul Beroza, and Dean R. Artis. "Including Explicit Water Molecules as Part of the Protein Structure in MM/PBSA Calculations." Journal of Chemical Information and Modeling 54, no. 2 (January 16, 2014): 462–69. http://dx.doi.org/10.1021/ci4001794.

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