Screening of 1,3,4-Thiadiazole Derivatives by in silico Molecular Docking to Target Estrogen Receptor for Breast Cancer

: Estrogen receptor plays a key role in physiological functions and is activated by estrogens. Breast cancer is the leading cancer among women all over the world. The molecular docking research began with the use of Schrödinger software to find the lead molecule from the 1,3,4-thiadiazole derivatives. Also, ADME drug-likeness properties were predicted to find a suitable lead candidate. Out of fifty 1,3,4-thiadiazole derivatives, TOH62 and TF62 showed the highest binding energies, and gscores are -9.36 and -8.85 kcal/mol, respectively. These results proved that the phenolic OH has a certain role in the binding with the targeted protein and is responsible for the activity. In the near future, we are willing to do the in-vitro and in-vivo anti-breast cancer evaluation of these compounds. derivatives; TF = (Z)-2-(((5-(4-fluorophenyl)-1,3,4-thiadiazol-2-yl)imino)methyl)phenol derivatives; TC = (Z)-2-(((5-(4-fluorophenyl)-1,3,4-thiadiazol-2-yl)imino)methyl)phenol derivatives; TOC = (Z)-2-(((5-(4-methoxyphenyl)-1,3,4-thiadiazol-2-yl)imino)methyl)phenol derivatives; TV=(Z)-4-(5-((2-hydroxybenzylidene)amino)-1,3,4-thiadiazol-2-yl)-2-methoxyphenol derivatives; TPN = (Z)-2-(((5-(4-nitrophenyl)-1,3,4-thiadiazol-2-yl)imino)methyl)phenol derivatives; TMN = (Z)-2-(((5-(3-nitrophenyl)-1,3,4-thiadiazol-2-yl)imino)methyl)phenol derivatives; TOH = (Z)-2-(((5-(4-chlorophenyl)-1,3,4-thiadiazol-2-yl)imino)methyl)phenol derivatives.


Docking.
The ligands, 1,3,4-thiadiazoles were designed by changing the functional group and the substitutions, then LigPrep (v3.1), Schrödinger software suite was used to prepare the highresolution 3D structure of the respective ligands, which include the 2D to 3D conversion, optimization, minimization of energy states, some of the other corrections [13]. The protein structure (PDB: 3ERT) was downloaded from the PDB databank, which has the estrogen receptor cocrystallized with 4-hydroxytamoxifen. The protein preparation wizard was used to refine the protein structure [14] then the Receptor grid was generated using Receptor grid generation in Schrödinger software suite, LLC, NY, USA, 2015 [15,16]. The docking was carried out using the extra precision method (XP). The Glide score (Gscore) was calculated using the following formula in kcal/mol. G Score = a*vdW + b*Coul + Lipo+ Hbond +Metal + BuryP + RotB + Site (1) where Van der Waals energy is represented by vdW, with coefficients a=0.065 and b=0.130. Coul is the symbol for Coulomb energy. Lipo is a symbol of lipophilic interaction. Metalbinding is represented by Metal, and hydrogen bonding is represented by H-bond. BuryP represents the penalty for buried polar groups, while RotB represents the penalty for freezing rotatable bonds. The site represents polar interactions at the active site. The computational experiments were run using a Linux operating system [17].

ADME properties.
By use of the QikProp, which provides physically significant descriptors and uses them to do ADME predictions, was used to determine a set of ADME-related parameters of the ligands. The methods were carried out using the logarithm of aqueous solubility, log Swat (range for 95 percent of drugs: -6.0 to 0.5); the integer of assumed binding constant to human serum albumin, log KHSA (range for 95 percent of drugs: -1.5 to 1.2); and the integer of assumed blood/brain barrier partition coefficient, log B/B (range for 95 percent of drugs: -1.5 to 1.2). (range for 95 percent of drugs: -3.0 to 1.0) the evident Madin-Darby canine kidney (MDCK) cell permeability in nm s-1 (25>500 great); the index of cohesion interplay in solids, Indcoh, determined from the predicted Polarizability, QP polrz; the IC50 value for blocking HERG K+ channels, log HERG; the apparent Caco-2 cell permeability, QPP Caco in nm/s, metabolic reactions, #metab, Polarizability, QP polrz, etc. The ADME/T features of the finest ligands molecules were predicted using the Schrödinger 2013 QikProp tool. The number of Hydrogen Bond Acceptors (HBA) and Donors (HBD), QP log Po/w, skin permeability; log Kp, the percentage of human oral absorption, and other attributes were expected [18,19].

Results and Discussion
In this study, 1,3,4-thiadiazole derivatives were used in in-silico molecular docking investigations [17]. The selected ligands (50 different derivatives of 1,3,4-thiadiazole) were docked with the estrogen receptor protein (PDB: 3ERT). The thiadiazole derivatives were screened with ER to have a deeper insight into various chemical binding modes. The interaction between the best compounds of thiadiazole derivatives and ER receptor are shown in Figure 1. Molecular docking study suggests that electron-withdrawing group like CN group has strong H-bonding with the amino acid residue ARG 394; likewise, electron-donating group like OH group has strong H-bonding with ASP 351, GLU 353, GLY 521 and HID 524. Maruthanila et al. also reported that the amino acid residues GLU 353, and ARG 394 are the important Hbonding sides and are also responsible for the activity and binding energies. The 2D and 3D interactions of foremost thiadiazole derivatives are shown in Figure 1. Take a look at the thiadiazole derivatives shown above, TOH62 and TF62 have shown the H-bonding between the phenolic OH and ARG 394, GLY 521, HID 524, and GLU 353 residues and also have the highest glide score (-9.36 and -8.85 kcal/mol, respectively) among the series. Also, these highlighted the hydrophobic interactions which are partially contributing to the binding affinity. Table 1 shows the lowest binding energies for the ligand-ER interaction identified. Sakkiah et al. reported that OHT binds ER more strongly in the antagonist conformer, whereas E2 prefers the agonist conformer, according to the binding interaction energy analysis [20]. The most stable interactions in the ERalpha OHT complex include the hydrogen bond between OHT and GLU353 and the hydrophobic interaction between OHT and ALA350 and LEU525, according to the binding interaction analysis. Because the active region of ER is mainly hydrophobic, selective binding of potential anti-breast cancer targeted therapies should rely mostly on these hydrophobic and H-bond interactions. The van der Waals interactions with the surrounding hydrophobic residues LEU 346, ILE 424, ALA 350, LEU 387, LEU 384, LEU 388, and LEU 391 were identified by docking studies. The OH and CN groups are also required for the compound's active interaction with the receptor.  Figure 2 represents the 2D and 3D interactions of hERLBD in complex with 1,3,4thiadiazole compounds with CN group interactions. To analyze drug-likeness and pharmacokinetics parameters, we approximated the thiadiazole derivatives' absorption, distribution, metabolism, and elimination (ADME) features. ADME characteristics of the compounds are crucial descriptors for human medicinal application. For the evaluation of crucial ADME characteristics and their allowed ranges, the QikProp (Schrödinger, Inc., New York, NY, 2013) was utilized [19]. The typical range of Lipinski's rule of five for oral availability was achieved for all derivatives (Table 2). Further pharmacophore models can be utilized for comparing the screened compounds to the recorded compounds [21].  The computed log p (octanol-water partition coefficient), which has been implicated in logBB (blood-brain barrier) penetration and permeability investigations, was used to screen active and ADME compliant thiadiazole derivatives. The generated descriptors were used to evaluate the distribution of each derivative in a human ( Table 3). The ADME parameters are quite comparable to those of the standard, and they fall within the usual range of values for 95% of all known medicines [21,22].
The glide score is an indirect measure of the ligand-receptor interaction's binding free energy: the stronger the interaction, the more negative the glide score [23]. The majority of breast cancers nowadays are ER-positive. Chemotherapy is less successful in patients with ERpositive breast cancer than in those with ER-negative illness. The binding energy and explanation are important discoveries for future experimental and theoretical investigation [24]. Molecular dynamics investigations support the docking findings, demonstrating that, in addition to hydrophobic and π-π stacking interactions, H-bond contact with ARG394 drives binding within LBD [25,26]. These docking results significantly impact the experimental cytotoxicity of the compounds [27][28][29][30]. This study could be used as a lead molecule to further reasonably optimize the structure of anti-estrogen drugs with stronger binding and higher activity in DBD [31][32][33].

Conclusions
In this study, 3ERT was targeted using a logical method based on the in-silico predictive potential to find the lead molecule for breast cancer. The study took advantage of the power of binding affinity and physiologically significant interactions. Docking analyses were carried out after the screened compounds were compared to standard compounds. The docking results revealed significant affinity with the estrogen receptor as well as identical binding interactions with native cocrystallized ligands of the specific receptor.

Funding
Dr. R.Elancheran thanked DST-PURSE phase II for the financial support. Prof. S. Kabilan and U. Vanitha thank University Grants Commission, New Delhi, for the UGC BSR Faculty Fellowship.