A Theoretical Study of the Docking of Medicines with some Proteins

: A set of ten drug compounds containing an amino group in the structure were determined theoretically. The parameters were entered into a model to forecast the optimal values of practical (log P) medicinal molecules. The drugs were evaluated theoretically using different types of calculations which are AM1, PM3, and Hartree Fock at the basis set (HF/STO-3G). The Physico-chemical data like (entropy, total energy, Gibbs Free Energy,…etc were computed and played an important role in the predictions of the practical lipophilicity values. Besides, Eigenvalues named HOMO and LUMO were determined. Linearity was shown when correlated between the experimental data with the evaluated physical properties. The statistical analysis was used to analyze the descriptors like multiple linear regression analysis performed to derive quantitative structure-activity relationship models which were further evaluated for the values of the prediction. The correlation coefficient gives an excellent relationship of more than (0.980, 0.980, and 0.978) for AM1, PM3, and HF/STO-3G respectively. A docking study was applied for the interaction of medicines with protein. All the drugs were connected with the protein to give the best energy stability for the docking mixtures. Nepafenac (compound No. 8) had the most stable energy with the protein compared with the 4-Aminosalicylic acid (compound No. 2) which had less energy stability.


Introduction:
Lipophilicity term is obtained by the partition and distribution between an aqueous solvent (water) and an immiscible organic solvent like octanolwater membrane which is used widely as a measurement in the drug compound activity 1 . The discovery of pharmaceutical actions and biological activity depends on lipophilicity 2 . The theoretical quantum chemistry 3 especially quantitative structure-activity relationships analysis (QSAR) and (QSPR) 4 were applied to predict these values in the drugs 5,6 . Besides, many derivatives of chalcone and flavones have been predicted experimentally and theoretically to calculate the logP 7 . The chromatographic technique 8 was applied to calculate the logP and for different kinds of profen medicines 9 . The statistical methods of a simple linear regression did not give an excellent correlation compared to the multiple linear regression which found an excellent relationship between the experimental and the calculated descriptors 10 .
Quantum method calculations were carried out using Hartree-Fock (HF) at 6-31G as a basis set for aryl-thiazole derivatives. Dipole moment, HOMO, LUMO heat of formation, and logP have been determined theoretically. There is a relationship between the predicted lipophilicity with their activity 11 . Furthermore, quantitative structural relationships for modeling the lipophilicity of platinum complexes and predicting their Log P values have been developed 12 . There are many methods to calculate and measure the lipophilicity of the HPLC 13 , the extraction of liquid-liquid 14 , and chromatographic technique 15 . A theoretical study of docking for (6YHU) was determined with the coumarin group as a treatment for the COVID-19 16 .
In a previous study, many drug compounds containing amino groups were calculated at semiempirical and ab initio levels 17 . Finally, theoretical calculations were applied widely in chemistry like corrosion protection 18,19 . The docking study [20][21][22] of (twenty-three) molecules with QRF3a SARS-CoV2 was studied using molecular docking. Using in silico modeling, researchers were able to discover leading compounds with potential inhibitory and virucidal properties 23 . Utilizing DockThor and Molegro docking scores, bioinformatics analysis was conducted using docking techniques and molecular dynamics to predict the binding and disruption caused by the ivermectin in proteins linked with SARS-CoV-2 24 . It was investigated how human serum albumin (HSA) interacts with two physiologically active derivatives (FNP and FNP4Br). Hydrogen bonding and electrostatic interactions (dipole-dipole) are the predominant binding factors for the associations between HSA: FNP and HSA:FNP4Br, according to thermodynamic characteristics and molecular docking data 25 . The most promising two ligandpocket complexes from docking experiments (alacepril and lisinopril) also had molecular dynamics (MD) simulations followed by binding free energy calculations to clarify some information on their thermodynamic and dynamic features and corroborate the docking results. These findings most likely presented a good lead candidate for the development of COVID-19 medicinal medicines 26 .
Chem Bio Office (version 11.0.1) software was applied for the descriptor's computational parameters. The MM2 force field method was used first to convert the structures to more stability and less steric energy. Later, (Gaussian 03W) 27 package is used to evaluate the Physico-chemical properties. Many data of thermodynamic values were calculated depending on the output file .
The goal of molecular docking is to use computational methods to anticipate the structure of the ligand-receptor complex. Docking is accomplished in two steps: first, sampling ligand conformations in the active site of the protein, and then rating these conformations using a scoring function.
The package of (MOE) 28 software (ver. 2015) was used for studying the docking between the medicines with (6YHU) protein. They were imported into MOE, and the MOE structure preparation wizard was used to fix all the problems with the protein structures. All solvent molecules were removed from the structures before the hydrogen atoms were added, and the structures were subsequently subjected to energy minimization. In the working directory, the final optimized structures were stored.
The aim of this study is the medications were theoretically assessed using several forms of computations. Thermodynamic parameters were calculated and had a significant impact in predicting realistic lipophilicity values. For the interaction of medications with proteins, docking research was used. All of the pharmaceuticals were attached to the protein to provide the best energy stability for the docking mixes.

Results and Discussion:
Ten drug compounds were applied in this study as shown in their name and log P data in Table. 1. Semi-empirical (AM1 and PM3) and ab initio methods (HF/STO-3G) were chosen to calculate the physical properties of these drugs. These parameters were analyzed using statistical equations depending on the simple and multiple linear regression. The last regression was used to find the correlation and relationship between the evaluated physical properties with the practical lipophilicity. Many models of the correlation were analyzed to choose the best equation to depend on the coefficient (R) and values of Fisher (F) to predict the values and compare with the experimental parameters .

Set of Data :
Table. 1 shows the values of the physical properties of the ten drugs using three methods of calculation. Also, we can notice the practical parameters of (logP) 29 for all the drug compounds. The steric energy effect was shown clearly for the drugs, the steric effect had little for the small configuration compared to the bulk compound which has a big value. The thiazole derivatives had more steric compared to the others .   The SPSS software was used to analyse the physic-chemical values. Multiple linear regression was applied for the theoretical methods (PM3 and AM1) and Hartree-Fock method ( So, the relationship between the practical and the predicted shows an excellent correlation expected for the ten medicines found (R=0.9802) to all. Table. 5 and Fig. 1 show the predicted values of (logP) for the ten medicine mistreatment stepwise equation.  All the drugs were docking with the protein named (6YHU). There is a difference in the stability between the medicines and the protein. Drug number (8) is more stable compared to drug number (2) having less stability with the protein as shown in Fig. 2 and table. 6.

Figure 2. Comparison between the medicines with their stability.
The following Table explains the energy of the docking between all medicines with the protein (6YHU).  It was shown that the medicine in the benzene ring was attached with (Lys139) having this amino acid with different active groups like (-NH2), (-OH), and (-C=O). These groups were docking with medicine and had more stability. While a less stable state, the configuration was different compared with its configuration at more stable.
[More stable] [Less stable] Also, it was shown that the medicine for the (-OH) group in the benzene ring was attached with (Cys 142) having this amino acid with different active groups. These groups were docking with medicine and were very close to the medicine and the steric effect is compared to the more stable.

Conclusion:
Computational quantum and statistical approaches calculate docking in greater depth using various descriptors, resulting in a broad interpretation of medical effects. The prediction of the log P values is determined by three different methods of calculation. These methods give an excellent correlation between the practical values with the experimental. But (AM1) method gives the best relationship compared with (PM3) and (HF/STO-3G) depending on the Fisher values.
Some drugs have been explored using a theoretical technique and molecular docking calculations due to their high efficacy in the treatment of the COVID-19 pandemic. These medications and protein molecular structures have been optimized.
The docking analysis has found that the medicine number (8) has more stability with the protein (6YHU) compared with the medicine (2).
The reason may depend on the fact that the medicine contains (8) two amino groups (-NH2) and two carbonyls (C=O) compared to the medicine (2) which has two hydroxyl groups (-OH) and one amino group (-NH2) and one carbonyl (C=O). So, the ion pair is more effective in medicine (8) rather than in medicine (2). This means that the interaction between the amino group (-NH2) with the amino acid in the surrounding is more stable compared with the hydroxyl group (-OH) 30