Target docking

The target docking process is similar to that of blind docking , with the same steps for preparing exotoxin A and ligands in AutoDock 4 (FORLI, S. et al., 2016) (MORRIS et al., 2009). However, the preparation of the grid was different , since the grid extended only in the region indicated by blind docking as the most promising binding site. Thus, the exotoxin A grid was made with 82 points in the X dimension with a center of 52.946, 92 points in the Y dimension with a center of 40.471 and 108 points in the Z dimension with a center of 8.702. The spacing of this grid was 0.375 angstrom.

For the docking parameters, the protein was still configured as rigid and the default AutoDock 4 settings (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 50 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. Furthermore, as output , the Lamarckian Genetic Algorithm was used.

Data obtained:

The target docking data was the average of the values of the largest cluster to avoid analyzing outliners .

Figura 1: Gráfico do desempenho dos compostos no indicador de energia de ligação target

Source: Authors

It is noticeable that Samples 2, 11 and 13 had the best performance in this criterion, with values lower than -12. Furthermore, Samples 3, 9, 8 and 1 also presented an excellent performance, with data lower than -11. PJ34 only had a better performance than Sample 10, demonstrating that the derivatives of the control group resulted in more potent inhibitors.

Figura 2: Gráfico do desempenho dos compostos no indicador de constante de inibição target com dados em nM

Source: Authors

It can be seen that Samples 2, 11 and 13 had an excellent performance in the inhibition constant, while Sample 10 clearly had the worst performance. PJ34 was outperformed by 12 of the 13 samples, although it had a similar performance to Samples 5, 6 and 7.

Figura 3: Gráfico do desempenho dos compostos no indicador de eficiência do ligante no target

Source: Authors

In this indicator, Sample 11 had the best performance, with the only value lower than -0.5. In addition, Sample 2 and PJ34 also stood out, while Samples 6 and 10 had the worst performance.

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.

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.