About molecular blind docking and data production:
Molecular docking is a computational technique used to predict the interaction between molecules - between a ligand and a target protein. Protein-Ligand Docking was used to determine the preferred orientation of PJ34 derivatives in relation to exotoxin A. This allows the identification of conformations that maximize binding affinity. Therefore, this step is essential to evaluate the potency of compounds as exotoxin A inhibitors.
In molecular docking , tests related to the potency of the compounds as potential inhibitors of exotoxin A were performed. Thus, data such as binding energy, inhibition constant and ligand efficiency were obtained. For this purpose, the AutoDock 4 tool (FORLI, S. et al., 2016) (MORRIS et al., 2009) was used.
Docking was done on the LAPTOP-57R8D5LP device, with 17.9 GB of usable computer memory and an AMD Ryzen 5 3500U (2.10 GHz) processor .
The first step to produce the docking data was the preparation of exotoxin A , whose three-dimensional structure, identified by 1IKQ in the database, was obtained from the Protein Data Bank (WEDEKIND et al., 2001). In this preparation, it was necessary to delete the water molecules and the atoms that are not part of the protein . Then, polar hydrogens were added . Then, the missing atoms were repaired and the Kollman charges were added. Finally, the last step for the preparation of the protein was to add AD4 atom types (from AutoDock 4) to the atoms.
The second step to perform molecular docking was the preparation of the ligand . The three-dimensional structure of each compound was obtained from PubChem (KIM et al., 2023) and, subsequently, its geometry was optimized in the Avogrado software (HANWELL et al., 2012) (AVOGRADO, 2015). Then, using the AutoDock 4 tool (FORLI, S. et al., 2016) (MORRIS et al., 2009), the Gasteiger loads were computed and the maximum number of binder twists was selected.
Source: Avogrado (HANWELL et al., 2012) (AVOGRADO, 2015)
The third step for this experiment was to establish the grid docking . Because it was a blind docking , the grid covered the entire exotoxin A. The exotoxin A grid was made with 88 points in the X dimension with center of 36.017, 66 points in the Y dimension with center 40.471 and 70 points in the Z dimension with center 18.473. The spacing of this grid was 1 angstrom . Then, after saving the grid dimensions, the AutoGrid 4 program of this tool (FORLI, S. et al., 2016) (MORRIS et al., 2009) was used to finish preparing the grid .
Finally, the last step was to assign the parameters for docking . For this procedure, the protein was configured as rigid and the default settings of AutoDock 4 (FORLI, S. et al., 2016) (MORRIS et al., 2009) were used in the ligand and docking parameters. In the search parameters, the Genetic Algorithm option was used with 40 conformations , with a maximum of 25,000,000 evaluations, with a population size of 150 and with a maximum number of generations of 27,000. In addition, as output , the Lamarckian Genetic Algorithm was used. After that, the AutoDock 4 program (FORLI, S. et al., 2016) (MORRIS et al., 2009) was used to perform the docking .
Source: AutoDock 4 (FORLI, S. et al., 2016) (MORRIS et al., 2009)
Data obtained:
At this stage, data on binding energy, inhibition constant and ligand efficiency were obtained. For analysis purposes , the best data from the largest docking cluster of each compound were used, since the purpose of blind docking is to identify promising binding sites.
Binding energy is the amount of energy released when a ligand binds to a receptor, forming a stable complex. Low binding energy values are desirable ; they indicate a stronger interaction between the molecules. Thus, the ligand fits into the active site of the receptor, increasing the likelihood of therapeutic efficacy.
Source: Authors
The graph above is a comparative representation of the binding energy for the 13 samples and PJ34. The results are presented in a vertical bar graph, where more negative values are better.
A variation in binding energy values is seen between the samples. The most notable performer is Sample 2 , with the lowest binding energy of -10.73 , closely followed by Sample 13 at -10.16 . These samples have a strong binding affinity.
At the opposite end of the spectrum, Sample 6 has the worst binding energy with a value of –6.53, a comparatively weaker interaction. PJ34 has a binding energy of –8.11, which is surpassed by eight samples.
Samples 11 and 9 also have low binding energies, all lower than -9.5, indicating that these, together with Samples 2 and 13, can be considered the most promising of the set in terms of interaction strength.
The inhibition constant is one of the most important parameters in pharmacology, because it concerns the affinity between an inhibitor and its target enzyme. Low values of this constant are related to a strong interaction, which leads to a favorable concentration-efficacy relationship. A low value may reveal that the inhibitor binds competitively to the enzyme's active site or that it interferes with other steps of the catalytic process.
Source: Authors
In the graph above, it is noticeable that Sample 2, Sample 13 and Sample 11 had the best performance in the inhibition constant (Ki), with, respectively, 13.57 nM, 35.67 nM and 95.66 nM. Thus, considering that these values are lower than 100 nM, it is possible to consider that these compounds are potent inhibitors of exotoxin A. In addition to these samples, Samples 9, 1, 3, 4 and 7 also had better results than PJ34, with the control group having an inhibition constant of 1130 nM. Sample 6 had the worst performance, with an inhibition constant of 16260 nM, being the only one greater than 4000 nM. Therefore, considering that only five samples had a worse performance than PJ34, it is possible to conclude that the extension of the R group of this chemical compound resulted in more potent inhibitors, considering the collected data set.
Ligand efficiency is a parameter in the interaction between a drug and its target enzyme. A low ligand efficiency value indicates that the amount of inhibitor needed to produce the desired effect is reduced, which indicates a more effective and specific interaction. This is important in drug development, since minimizing the dose can reduce potential side effects and improve patient safety.
Source: Authors
In the graph above, it is possible to identify that Sample 11 and Sample 2 had the best performance in the binder efficiency indicator, with data of 0.4. In addition to these compounds, Sample 1 also had a good value, with -0.38. PJ34, when compared to the other samples, had the fourth best performance. Samples 6, 8, 10 and 12 had the worst performance, with values greater than -0.3. Therefore, it is noticeable that, in this indicator, the extension of the R group of PJ34 was not a definitive factor for a good result, since most of the samples had a lower performance than the control group.
AVOGADRO. Avogadro: An open-source molecular editor and visualization tool. Available at: https://avogadro.cc/ . Version 1.2.0, 2015. Accessed on: August 10, 2024.
FORLI, Stefano et al. Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nature protocols , vol. 11, no. 5, p. 905-919, 2016. Available at: < https://www.nature.com/articles/nprot.2016.051 >. Accessed on: 5 Aug. 2024.
HANWELL, Marcus D. et al. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. Journal of cheminformatics , v. 4, p. 1-17, 2012. Available at: < https://link.springer.com/article/10.1186/1758-2946-4-17 >. Accessed on: August 15, 2024
MORRIS, GM et al. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Computational Chemistry , v. 30, n. 16, p. 2785-2791, 2009. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2760638/ . Accessed on: August 11, 2024.
WEDEKIND, Joseph E. et al. Refined crystallographic structure of Pseudomonas aeruginosa exotoxin A and its implications for the molecular mechanism of toxicity. Journal of molecular biology , vol. 314, no. 4, p. 823-837, 2001. Available at: < https://www.sciencedirect.com/science/article/pii/S0022283601951952?casa_token=gtiRpmc wvXIAAAAA:7Og_uofP8- INAPZVvyAvBPZd8C7g_49LWW8L6vvWJ_3QAJ9_7S_OZrja97- OOqmUGzWVfGJhEXU>. Accessed on: 5 Aug. 2024.
Comments