Research Article

Mutational studies of novel screened molecules against wild and mutated HIV-1 using molecular docking studies Pawan Gupta1,2*, Prabha Garg2

ABSTRACT

Background and Aim: The screened molecules which proposed novel HIV-1 integrase inhibitors were collected from the literature. Mutational studies were performed to check whether these molecules are having good binding affinity against mutated HIV-1 IN or not using molecular docking technique. Materials and Methods: First, homology models of the mutated HIV-1 IN were prepared and subsequently all the models were refined and optimized in MODELLER program. Next, molecular docking studies were performed into the active site of mutated HIV-1 IN models using the proposed inhibitors in AutoDock 4.1 program. The results of these studies were compared with the wild type docking studies. Results: The docking studies were found that some of the screened molecules (ZINC1245110, 131614, 92749, ZINC05181828, and ZINC13147504) followed the same binding patterns (in term of locations, interactions, and binding score) as found with wild type HIV-1 IN. Conclusions: Computationally, the same binding patterns were exhibited by these molecules (ZINC1245110, 131614, 92749, ZINC05181828, and ZINC13147504) against mutated models as wild type. This elucidated that these molecules having susceptibility against the drug-resistant HIV-1 IN. Hence, these molecules may be used as a starting point to design novel inhibitors against mutated HIV-1 IN, which need to be confirmed experimentally.

KEY WORDS: Docking, Drug resistance, Homology modeling, HIV-1 integrase, Mutation

INTRODUCTION Drug resistance is the inevitable consequence of incomplete suppression of HIV-1 replication. The Human immuno-virus (HIV) causes AIDS. Three rapid replication rate of HIV and its inherent genetic essential required for HIV replication variation have led to the identification of many encoded by pol gene: Reverse transcriptase (nucleoside HIV-1 variants that exhibit altered drug susceptibility. and non-nucleoside), protease, and integrase (IN). Numbers of mutational studies have been published Last few decades, all of them are considered to be on HIV-1 IN which found to be important aspect to promising targets for the development of anti-HIV understand the drug resistance studies.[4,16-18] More [1-6] drugs. Among them, IN has been identified as a than 60 mutations have been specifically associated unique and validated HIV target for drug design and with resistance to HIV-1 IN inhibitors in vitro and [7,8] discovery as IN having no ortholog in human. In in vivo. The primary mutations are associated in the past decade, only few drugs have been approved active site and greatly affected the HIV-1 IN activity. [9] [10] and in clinical trial against HIV-1 IN: Other mutations are not found in active site, but exert [11] (FDA-2007), (FDA-2012), their effect along with other mutations (Table 1).[16,17] [12] (FDA-2013), BI 224436, These mutations sometime alleviate the effect of other [12-15] (GS-9883), , and MK-2048. mutations or enhanced it. The conformational changes which occurs due to mutations at the active site of Access this article online target protein causes alteration in the drug binding ability of the protein. At instances when the mutations Website: jprsolutions.info ISSN: 0974-6943 are severe, the binding ability gets diminished

1Department of Research and Development and School of Pharmaceutical Sciences, Lovely Professional University, Phagwara - 144 411, Punjab, India, Tel. :+91-1824-444022, Fax. +91-1824-506111, 2Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Sector-67, Mohali - 160 062, Punjab, India, Tel.: +91- 172-2214 682; fax: +91-172-2214 692

*Corresponding author: Pawan Gupta, Department of Research and Development (Research Coordinator) and School of Pharmaceutical Sciences (Assistant Professor), Block-38, Room No. 203, Lovely Professional University, Phagwara, Punjab, India - 144 411, E-mail: [email protected]

Received on: 17-05-2017; Revised on: 28-06-2017; Accepted on: 15-07-2017

Journal of Pharmacy Research | Vol 11 • Issue 9 • 2017 1067 Pawan Gupta and Prabha Garg resulting in survival of the organism in the presence Table 1: HIV‑1 integrase residues involved in drug of drug, leading to drug resistance. Therefore, it is resistance[16] necessary that drug resistance studies need to be done Raltegravir Elvitagravir for novel inhibitors to confirm their activity against N155H, L74M, E92Q, T66I, E92Q, S147G, mutated HIV-1 IN as well. T97A, V151I, Y143H, Q146P, H51Y, E157Q, G163R D232N, Q148K/ Q95K, E138K, R263K, To evaluate the drug resistance profile of inhibitors, R/H, L74M, E138K, F121Y, S153Y, S147G molecular docking is very suitable technique to analyze G140S/A, Y143R/C, L74A, the binding modes into the active site of mutated E92Q, I203M, S230R protein and compared with wild type proteins.[19] This Primary resistance mutations are indicated in bold. HIV‑1: Human can enable to depict what is actually happened after immuno‑virus ‑ 1 mutation into active site of protein and how binding MATERIALS AND METHODS affinity is changed. The relationship between the binding affinity of drug and drug resistance is inverse; Data Set Collection and Preparation [19] higher the affinity – lower will be the resistance. The data sets: Benzodithiazine, curcumine, and screened Although, binding affinity is measured by docking molecules[20-22,24] and HIV-1 IN (PDB ID: 1QS4 and 1BL3) score (high score leads to high affinity). However, were used in these studies. AutoDock tool (ADT)[25] was this is not sole factor for binding mode analysis. used to prepare these data sets (adding polar hydrogen’s Altogether, docking score and binding interactions are and loading Kollman United Atoms charges). For getting [20] best criteria for binding mode selection. PDBQT file format for AutoDock docking protocol, all the data sets were processed in MGL tool. In previous work, two series (benzodithiazine and curcumine series) of molecules were collected. To Mutational Model Development identify the novel lead molecules, docking, QSAR, and The mutational models (mutated homology models) for shape-based screening (SBS) were performed against HIV-1 IN were performed using Modeller9v7 program[26] HIV-1 IN (protein data bank [PDB] ID: 1QS4 and for wild type HIV-1 IN (PDB IDs: 1QS4, 1BL3). As 1BL3) using these series (benzodithiazine for 1QS4 evident from the literature,[4,17,18] important primary and curcumine series for 1BL3).[20-23] In these studies, mutations (highlighted in Table 1) were identified and first important structural features were identified selected for building of the mutational models of HIV-1 which were responsible for IN inhibitory activity. IN. These models are designated as follow: Second, results of the SBS of ZINC and SPECS • Model-1:Gln148Arg and Asn155His databases[24] and absorption, distribution, metabolism, • Model-2: Thr66Ile and Glu92Gln. and excretion studies were gave novel molecules (for benzodithiazine series Mol ID: ZINC07558742, For both IN proteins, only A chain of 1QS4 and C [22] ZINC07795482, ZINC11153210, ZINC12485110, chain of 1BL3 were taken as this is having full defined 131614, and 131621; for curcumine series Mol ID: active site of IN protein.[20,21] Complete sequences of ZINC05181828, ZINC13147504, ZINC14672476, PDB IDs: 1QS4 and 1BL3 were collected from PDB [24] 92749, 92770, 92827, and 146610) which satisfied and inserted the primary mutations in them. After that, and followed the same trend as best active molecules sequence alignments of mutated and wild types were (benzodithiazine and curcumine derivatives) using carried out using sequence alignment tool (ClustalX 2.1 in silico predictions (through QSAR equations program). Figure 2 is showing the sequence alignments [20-24] and docking analysis). It was revealed that between wild type (1QS4A and 1BL3C) and mutated these molecules may have potential against HIV- (Mut) protein of 1QS4 and 1BL3, respectively. 1 IN. In the presented work, mutational models for HIV-1 IN (PDB ID: 1QS4 and 1BL3)) were build Next, Model 1 and Model 2 were built using the using Modeller9v7 tool for the reported mutations Modeller9v7 program. It was given five best (Gln148Arg, Asn155His, Thr66Ile, and Glu92Gln, models on the basis of Discrete Optimized Protein highlighted in Table 1). The binding mode analysis Energy (DOPE) score and molecular probability of the best active molecules of the benzodithiazine, density function (molpdf). The lowest score of these curcumine, and screened molecules (Figure 1) were parameters are considered to be significant for good performed against mutated HIV-1 IN using docking model. The least energy model cannot be a good idea methodologies for drug resistance studies and for considering a best model. compared the results with wild type results. These studies will give the idea about which screened Model Validation and Refinement molecules are also computationally active against The developed models have to follow the model mutated HIV-1 IN, but biological assay need to be validation criteria significantly using structural done to confirm their in vitro activity against both analysis and verification server (SAVES) web-based mutated and wild type strain of HIV-1 IN. tools (PROCHECK [Ramachandran plot] and ERRAT

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Figure 1: Screened molecules obtained after molecular modeling plot).[27,28] All these five models were subjected for which appear to have unusual geometry and provide validation and refinement using SAVES tools (http:// an overall assessment of the structure as a whole. nihserver.mbi.ucla.edu/SAVES). The parameters generally checked by PROCHECK are covalent geometry, planarity, dihedral angles The PROCHECK analyses provide an idea of the (Ramachandran plot), chirality, non-bonded stereo chemical quality of all protein chains in a given interactions, main-chain hydrogen bonds, disulfide PDB structure. They highlight regions of the proteins bonds, and stereochemical parameters.

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Figure 2: Sequence alignments of wild type (1QS4A and 1BL3C) and mutated (Mut) proteins of human immuno-virus–1 integrase. Rectangle boxes are corresponding to mutated residues with reference to wild type protein

The Ramachandran plot displays the psi and phi out into the active site of the mutated models (Model 1 backbone conformational angles for each residue in a and Model 2) of HIV-1 IN.[20,21] Similarly, docking studies protein. In a Ramachandran plot, the core or allowed were also performed for screened molecules given in regions are the areas in the plot show the preferred Figure 1. The results of these studies were compared with regions for psi/phi angle pairs for residues in a protein. previous docking studies with wild type HIV-1 IN.[20-22,24] If the determination of protein structure is reliable, most pairs will be in the favored regions of the plot and only For docking studies, AutoGrid and AutoDock program a few will be in disallowed regions. For analyzing the were used.[25] The AutoDock is grid-based approach in Ramachandran plot of the structures were submitted which ligand is docked into the active site of protein. to SAVES. The residues found in disallowed region Before docking, the AutoGrid program was used of this plot were subjected to energy minimization. generate the grids files. A grid box with spacing 0.375 and The energy minimized model was further subjected to dimensions 50 × 50 × 50 points was constructed around loop modeling in MODELLER using ERRAT plot as the binding site. The grid center is selected at the center of guidance for selecting a region for loop modeling. binding conformation of the molecule (benzodithiazine, curcumine, and screened molecules) into the active The ERRAT program examines a PDB file and generates site of wild type protein. The Lamarckian genetic a score based on the quality of the local structure algorithm was employed using different conformations surrounding each residue, based on the typical ranges of of molecules (benzodithiazine, curcumine, and screened dihedral angles, and side chain contacts observed in real molecules). This docking protocol was repeated many proteins. This program generates a plot which gives a times by altering the input parameters such as different measure of structure error at each residue in the protein. conformation of each molecule, different grid sizes, and It also calculates an overall score for structural quality. grid centers. This was done to check the reliability of The overall measure of quality is given at the top of the the docking program to generate consistent docking plot, and each bar in the histogram is shaded according pose. Each docking runs were set to 30 for ranking and to the significance of the local structural error. Higher scoring of docking poses.[29] the significance, with greater confidence a particular amino acid can be rejected. Those regions which have an For selection of most reliable conformation of the error values cutoff >95% in ERRAT plot were selected molecules into the active site of mutated models, pose for loop modeling one by one. After each round of assessment criteria were used as follow:[20,22,24] loop modeling in MODELLER, 10 different structures 1. Location of docked molecule (near to Mg2+) were generated which were again validated based on 2. Distance from Mg2+ PROCHECK and ERRAT plot evaluation and again 3. Relevant H-bond interactions (Asp64, Cys65, loop modeling carried out. Finally, a model showing Thr66, His67, Glu92, Asp116, Asn120, Gln148, good PROCHECK verification and ERRAT plot Glu152, Asn155, Lys156, and Lys159) were considered for final three-dimensional structural 4. Docking scores. comparison with wild type structures and root-mean- square deviation (RMSD) were also calculated. RESULTS AND DISCUSSION Molecular Docking Model Development and Refinement The docking studies of best active molecules of The developed models were refined using SAVEs server. benzodithiazine and curcumine derivatives were carried All the five models were evaluated and selected best model

1070 Journal of Pharmacy Research | Vol 11 • Issue 9 • 2017 Pawan Gupta and Prabha Garg on the basis of the DOPE score as well as results of the Ramachandran and ERRAT plots. After loop modeling of each generated models based on PROCHECK and ERRAT plot (described in material and methods), these final models are considered to be best (Figures 3-6). The Ramachandran plots of homology models of Models 1 and 2 for 1BL3 and 1QS4 are illustrated below. For final Model 1 of 1BL3, Ramachandran plot showed that there are 93.4% residues in the most favored region, 5.1% in allowed region and 1.5% in generously allowed Figure 3: Model structure (green for wild and cyan for region and 0.0% in disallowed region. For Model 2 of mutated protein) and Ramachandran plot for Model 1 of 1BL3 1BL3, Ramachandran plot showed that there are 92.7% residues in the most favored region, 7.3% in allowed region and 0.0% in generously allowed and disallowed region.

Similarly, Ramachandran plots were also analyzed for Model 1 and 2 of 1QS4 and RMSD was calculated. For Model 1 of 1QS4, Ramachandran plot showed that there were 93.8% residues in the most favored region, 6.2% in allowed region and 0.0% in generously allowed and disallowed region. For Model 2 of 1QS4, Ramachandran Figure 4: Model structure (green for wild and cyan for plot showed that there were 91.4% residues in the most mutated protein) and Ramachandran plot for Model 2 of favored region, 8.6% in allowed region and 0.0% in 1BL3 generously allowed and disallowed region.

Hence, these final models exhibited all the residues in allowed region and mutated, and wild type were completely superimposed to each other (Figures 3-6). This proves that the homology models are reasonably accurate in terms of dihedral distribution and steric clashes. The ERRAT plot for Models 1 and 2 are illustrated in Figure 7a-d. The overall quality factor was found to be 97.368 and 97.351 for Models 1 and 2 of 1BL3, respectively. On the hand, Figure 5: Model structure (green for wild and cyan for for Models 1 and 2 of 1QS4 were found 97.163 and mutated protein) and Ramachandran plot for Model 1 of 97.872, respectively. These plots further increase the 1QS4 confidence of the acceptability of these homology models.

Molecular Docking All the docking results were analyzed using ADT tool. First criteria of docking pose assessment were used for selection of best docking pose. If molecules bound near to metal ion, then moved to next criteria and calculated the distant from highly electronegative atoms of molecule to metal ion (Mg). Next, key interactions were checked and finally docking score. If molecules Figure 6: Model structure (green for wild and cyan for have similar binding interactions, then best binding mutated protein) and Ramachandran plot for Model 2 of pose was selected on the basis of docking score. 1QS4

The results of docking analysis are given in Table 2. well. This means that these molecules may also have In this table, docking results were compared with wild susceptibility to interact with mutated models, thereby type docking studies. If molecules exhibited similar active against drug resistance condition. or high binding interactions (evaluated form docking pose assessment criteria) as compared to wild type During docking analysis, benzodithiazine derivative docking results, then these molecules were considered was found to be shown good binding pose in to be active in case of mutations in the active site as Model 1, but not in Model 2 (Table 2 and Figure 8 is

Journal of Pharmacy Research | Vol 11 • Issue 9 • 2017 1071 Pawan Gupta and Prabha Garg showing docking results with Model 1). This means binding affinity and interactions which may directly benzodithiazine may be susceptible against Model 1; reflect the activity against mutations. however, it may have resistant against Model 2. The docking studies of the screened molecules (Mol Similarly, docking analysis results were shown ID: For benzodithiazines series ZINC07558742, that curcumine derivative found to be susceptible ZINC07795482, ZINC11153210, ZINC12485110,[22] against both Model 1 and 2 (Figure 9). When these 131614 and 131621; for curcumine series Mol ID: results (benzodithiazine and curcumine series) ZINC05181828, ZINC13147504, ZINC14672476, were compared with docking studies with wild type 92749, 92770, 92827, and 146610)[24] were performed protein, it was revealed that binding conformations into the mutational models and compared the results of these docking poses were found different. These with wild type (Table 2). mutations caused slightly change in the active site pocket which still accommodated these molecules. The screened molecules were not completely It is elucidated that these molecules may also have superimposed (during docking studies with Model 1 and sufficient structural features which enabled them to 2) with wild type docking pose such as ZINC07558742, bind in mutated models (in active site) with good ZINC07795482, ZINC11153210, and 131621 (Table

a b

c d Figure 7: ERRAT plots for (a) Model 1 and (b) Model 2 of 1BL3 and (c) Model 1 and (d) Model 2 of 1QS4

a b Figure 8: Docking pose of benzodithiazine derivative into the active site of (a) wild type[21] and (b) mutational Model 1. Blue sticks are docked conformation (for mutational Model 1) and white sticks are docked conformation obtained from docking with wild type model in (b)

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Table 2: Docking interactions of mutational model of HIV‑1 IN Molecules Wild type Model 1 Model 2 Comparison (Gln148Arg, (Thr66Ile, Glu92Gln) with wild type Asn155His) results Benzodiathiazine32 Score: −1.41 Score: −6.37 Not docked Model 1: H‑bonding: Cys65, His67, H‑bonding: Cys65 susceptible Asn155 Metal interactions (Å): Model 2: Metal interactions (Å): N‑Mg2+ Mg2+‑O‑S 2.831, Resistant 2.77, NH‑Mg2+ 3.19, O‑Mg2+ Mg2+‑SH 2.317 1.75, HS‑Mg2+ 3.21 Cation‑π: Mg2+ Cation‑π: Lys156, Lys159, Mg2+ ZINC07558742 Score: −2.36 Score: −5.94 Score: −4.97 Not fulfilled H‑bonding: Asn155 H‑bonding: His67, Glu92 H‑bonding: Lys156 the pose Mg‑interactions: Mg2+‑C=O 1.69, Cation‑π: Lys159 Metal interactions (Å): assessment Mg2+‑N 3.42, Mg2+‑NH 3.22 Metal interactions (Å): Mg2+‑O 1.726 criteria Cation‑π: Lys156 Mg2+‑O 1.726 ZINC07795482 Score: −2.06 Score: −5.77 Score: −6.81 Not fulfilled H‑bonding: Asn155 H‑bonding: Asp64, Cys65 H‑bonding: Asp116 the pose Mg‑interactions: Mg2+‑O 1.61, Metal interactions (Å): Metal interactions (Å): assessment Mg2+‑N 3.36, Mg2+‑S 4.21 Mg2+‑O 1.700, Mg2+‑S Mg2+‑O 1.765, Mg2+‑S criteria 3.100, Mg2+‑N 3.00 2.578, Mg2+‑N 2.260 ZINC11153210 Score: −2.07 Score: −4.94 Score: −5.59 Not fulfilled H‑bonding: Asn155 Metal interactions (Å): Metal interactions (Å): the pose Mg‑interactions: Mg2+‑C=O 1.64, Mg2+‑O 1.799, Mg2+‑S Mg2+‑O 1.634, Mg2+‑S assessment Mg2+‑N 2.86, Mg2+‑NH 3.76, 3.904, Mg2+‑N 3.363 3.047, Mg2+‑N 2.368 criteria Mg2+‑S 2.88 Cation‑π: Mg2+ Cation‑π: Mg2+ ZINC12485110 Score: −2.49 Score: −5.22 Score: −5.87 Susceptible for H‑bonding: Cys65, His67, H‑bonding: Arg148* Metal interactions (Å): both Model 1 Asn155, Lys159 Metal interactions (Å): Mg2+‑O 1.638, Mg2+‑S and 2 Mg‑interactions: Mg2+‑C=O 2.47, Mg2+‑O 1.664, Mg2+‑S 2.501, Mg2+‑N 2.079 Mg2+‑N 2.68, Mg2+‑NH 2.00, 2.833, Mg2+‑N 2.671 Mg2+‑S 3.96 Cation‑π: Mg2+ 131614 Score: −3.43 Score: −5.93 Score: −3.92 Susceptible for H‑bonding: Asn155 H‑bonding: Asp116 H‑bonding: Asp64 both Model 1 Mg‑interactions: Mg2+‑O 1.75, Metal interactions (Å): Metal interactions (Å): and 2 Mg2+‑NH 3.46, Cation‑π: Mg2+ Mg2+‑O 2.030, Mg2+‑O Mg2+‑O 1.800 3.905 131621 Score: −2.10 Score: −6.44 Score: −4.81 Not fulfilled H‑bonding: Asn155 H‑bonding: Thr66 H‑bonding: Glu152 the pose Mg‑interactions: Mg2+‑O 1.65, Metal interactions (Å): Metal interactions (Å): assessment Mg2+‑O 2.44 Mg2+‑OH 1.862, Mg2+‑O Mg2+‑O 1.810, Mg2+‑O criteria Cation‑π: Mg2+ 2.080 1.760 Curcumine32 Score: −1.36 Score: −4.31 Score: −6.37 Susceptible for H‑bonding: Asp64, Asp116, H‑bonding: Glu152, H‑bonding: Asn120, both Model 1 Asn120, Lys159 His155* Glu152, Asn155 and 2 2+ Mg‑interactions: HO‑Mg1 1.93, Metal interactions (Å): Metal interactions (Å): 2+ 2+ 2+ 2+ 2+ C=O‑Mg1 2.73, C=O‑Mg1 Mg2 ‑O 1.783, Mg1 ‑O Mg2 ‑O 1.689, 2+ 2+ 2+ 3.96, C=O‑Mg2 2.08, 2.605, Cation‑π: Mg2 Mg2 ‑O 3.591, 2+ 2+ 2+ C=O‑Mg2 4.33, O‑Mg2 3.27 Mg1 ‑O 2.396 2+ 2+ 2+ Cation‑π: Mg1 , Mg2 , Lys159 Cation‑π: Mg1 , 2+ Mg2 , Lys159 ZINC05181828 Score: −2.19 Score: −6.68 Score: −6.15 Susceptible for H‑bonding: His67 Metal interactions (Å): H‑bonding: Asp116 both Model 1 2+ 2+ 2+ Mg‑interactions: Mg1 ‑O 1.91, Mg1 ‑O 1.695, Mg1 ‑O Metal interactions (Å): and 2 2+ 2+ 2+ 2+ Mg1 ‑O 1.85, Mg2 ‑O 2.36, 2.102, Mg2 ‑O 1.828, Mg2 ‑O 1.688, 2+ 2+ 2+ Mg2 ‑O 1.77 Mg2 ‑O 1.836 Mg2 ‑O 1.796, 2+ 2+ 2+ Cation‑ii: Mg1 Cation‑ii: Mg1 Mg2 ‑O 2.520, 2+ Mg1 ‑O 1.717, 2+ Mg1 ‑O 2.843 2+ Cation‑ii: Mg1 (Contd...)

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Table 2: (Continued) Molecules Wild type Model 1 Model 2 Comparison (Gln148Arg, (Thr66Ile, Glu92Gln) with wild type Asn155His) results ZINC13147504 Score: −3.89 Score: −6.34 Score: −7.05 Susceptible for H‑bonding: Asp64, Glu152, H‑bonding: Asp64, H‑bonding: both Model 1 Asn155 Glu152, Lys156 Asp64, Glu152 and 2 2+ Mg‑interactions: Mg1 ‑O 1.71, Metal interactions (Å): Metal interactions (Å): 2+ 2+ 2+ 2+ 2+ Mg1 ‑O 3.56, Mg2 ‑O 1.72, Mg2 ‑O 2.137, Mg2 ‑O Mg2 ‑O 3.099, 2+ 2+ 2+ Mg2 ‑O 1.75 1.731, Mg1 ‑O 2.271, Mg2 ‑O 1.667, 2+ 2+ Cation-ii: Lys156, Lys159 Mg1 ‑O 2.807 Mg2 ‑O 1.601, 2+ 2+ Cation‑ii: Mg , Arg148* Mg1 ‑O 2.250, 2+ Mg1 ‑2.108 2+ Cation‑ii: Mg1 , Lys159 ZINC14672476 Score: −3.51 Score: −5.93 Score: −6.82 Not fulfilled H‑bonding: Glu152, Asn155 Metal interactions (Å): H‑bonding: Asn155, the pose 2+ 2+ 2+ Mg‑interactions: Mg1 ‑O 1.76, Mg2 ‑O 1.787, Mg2 ‑O Glu152 assessment 2+ 2+ 2+ Mg1 ‑N 3.73, Mg1 ‑N 3.04, 2.740, Mg2 ‑O 2.198, Metal interactions (Å): criteria 2+ 2+ 2+ 2+ 2+ Mg2 ‑O 2.05, Mg2 ‑N 3.53 Mg1 ‑N 3.043, Mg1 ‑O Mg2 ‑O 1.798, 2+ 1.779 Mg2 ‑O 3.040, 2+ Mg1 ‑O 1.772, 2+ Mg1 ‑O 2.112 92749 Score: −3.83 Score: −6.76 Score: −6.36 Susceptible for 2+ Metal interactions (Å): Mg1 ‑O Metal interactions (Å): Metal interactions (Å): both Model 1 2+ 2+ 2+ 2+ 2+ 2.039, Mg1 ‑O 2.778, Mg2 ‑O Mg2 ‑O 1.745, Mg2 ‑O Mg2 ‑O 1.973, and 2 2+ 2+ 2+ 2+ 1.756, Mg2 ‑NH 2.989, Mg2 ‑O 2.634, Mg1 ‑O 3.063, Mg2 ‑O 2.787, 2+ 2+ 2.788 Mg1 ‑O 1.838 Mg1 ‑O 2.328, 2+ Mg1 ‑O 2.194 92770 Score: −4.21 Score: −6.57 Score: −8.11 Not fulfilled 2+ Metal interactions (Å): Mg1 ‑O Metal interactions (Å): Metal interactions (Å): the pose 2+ 2+ 2+ 2+ 2+ 1.891, Mg1 ‑O 2.957, Mg2 ‑O Mg1 ‑O 1.791, Mg1 ‑O Mg2 ‑O 1.812, assessment 2+ 2+ 2+ 2+ 1.806, Mg2 ‑O 2.653, Mg2 ‑NH 2.550, Mg2 ‑O 2.811, Mg2 ‑O 2.212, criteria 2+ 2+ 2.861 Mg2 ‑O 1.936 Mg1 ‑O 2.056 92827 Score: −4.04 Score: −7.07 Score: −6.05 Not fulfilled 2+ Metal interactions (Å): Mg1 ‑O Metal interactions (Å): Metal interactions (Å): the pose 2+ 2+ 2+ 2+ 2+ 1.903, Mg1 ‑O 3.340, Mg1 ‑NH Mg1 ‑O 1.861, Mg1 ‑O Mg1 ‑O 3.047, assessment 2+ 2+ 2+ 2+ 3.860, Mg2 ‑O 1.748, Mg2 ‑O 2.462, Mg2 ‑O 1.829, Mg2 ‑O 1.641, criteria 2+ 2+ 2+ 2.633, Mg2 ‑NH 2.849 Mg2 ‑O 2.902 Mg2 ‑O 1.701 146610 Score: −2.31 Score: −6.29 Score: −6.57 Not fulfilled H‑bonding: Asn155, Glu152 H‑bonding: Lys156, H‑bonding: Asn155 the pose 2+ Mg‑interactions: Mg1 ‑N 2.01, Lys159 Metal interactions (Å): assessment 2+ 2+ 2+ Mg1 ‑O 2.11, Mg1 ‑NH 2.79, Metal interactions (Å): Mg2 ‑N 2.827, criteria 2+ 2+ 2+ 2+ Mg2 ‑O 1.84 Mg2 ‑N 2.084, Mg2 ‑N Mg2 ‑N 1.888, 2+ 2+ 2+ Cation‑ii: Lys156, Lys159, Mg1 , 2.264, Mg1 ‑O 2.868 Mg2 ‑O 2.012, 2+ 2+ Mg2 Cation‑ii: Lys156 Mg1 ‑O 1.889, 2+ Mg1 ‑N 1.958 2+ 2+ Mg interactions in Å; Score in Kcal/mol. *Mutated residue, Mg1 for metal between Asp64 and Glu152, Mg2 for metal between Asp64 and Asp116, HIV‑1: Human immuno‑virus – 1, IN: Integrase

2). In case of 131614 and ZINC12485110 molecules, metal ion) and binding interactions as compared to docking poses were almost similar as wild type studies the results obtained from docking studies of screened as per the pose assessment criteria. These molecules molecules obtained from benzodithiazine template. were also had good binding affinity against these The molecules ZINC05181828, ZINC13147504, and mutations (Model 1 and 2) (Figure 10). This means 92749 were similarly bound as docking studies with that these molecules may be active against these wild type protein (Figure 11); however, molecules mutations. Overall results of these studies found that 146610, ZINC14672476, 92770, and 92827 were not different molecules are interacted in different manner fulfilled the pose assessment criteria. so that high binding affinity and interactions can be established while mutations are present in protein. The results of these docking studies elucidated that almost all the series and some of the screened It was found that docking results of these set of molecules molecules exhibited good binding affinity and are very much promising in term of position (near to interactions against mutational models. However,

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a b

c Figure 9: Docking poses of curcumine derivatives into the active site of (a) wild type,[20] (b) mutational model-1 and (c) mutational Model 2. Blue sticks are docked conformations in mutational models in (b) and (c), yellow sticks are docked conformations obtained from docking with wild type in (b) and (c). Green sticks are mutated residues in (b) and (c)

Figure 10: Docking poses of 131614 and ZINC12485110 into the active site of mutational Models 1 and 2. Green sticks represent mutated residues, orange sticks for docked conformations obtained from docking with wild type and magenta sticks for docked conformation with mutated models

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Figure 11: Docking poses of 92749, ZINC05181828 and ZINC13147504 into the active site of mutational Models 1 and 2. Green sticks represent mutated residues, orange sticks for docked conformations obtained from docking with wild type and magenta sticks for docked conformation with mutated models binding conformations were found somewhat different type results (experimentally given minimum criteria which directly correlated with different architecture of for binding should be followed). the active site of the mutated models as compared to wild type. The results of these studies are needed to be CONCLUSIONS further refined by docking studies. Therefore, repetitive The mutational studies were performed all the docking studies were also performed using same proposed screened molecules using docking studies. parameters and program for further validation. It was The docking studies were found that benzodithiazine, found that all the screened molecules (ZINC1245110, curcumine, and some of the screened molecules 131614, 92749, ZINC05181828, and ZINC13147504) (ZINC1245110, 131614, 92749, ZINC05181828, were followed the same binding pattern as found in and ZINC13147504) followed the same binding first step of docking results (Table 2). Therefore, it poses in term of locations, interactions, and score in was confirmed that docking poses are reliable and mutational models. All the molecules were bound consistent. These studies were also given an idea that slightly different conformation into the active site as changed in the architecture of the active site due to found in wild type docking studies. These analyses mutation were associated with the changed in the reflected that presence of mutations into active site accommodation of the binding molecules into the was rendered to change the geometry of active site. active site. Eventually, these changes caused different However, these molecules ZINC1245110, 131614, binding patterns (conformation of ligand, interactions 92749, ZINC05181828, and ZINC13147504 were and affinity) of the molecules as compared to wild found enable to bind into this mutated active site of

1076 Journal of Pharmacy Research | Vol 11 • Issue 9 • 2017 Pawan Gupta and Prabha Garg protein. Eventually, these results were elucidated 2011;80:565-72. that these molecules were susceptible against these 13. Dayam R, Al-Mawsawi LQ, Neamati N. HIV-1 integrase inhibitors: An emerging clinical reality. Drugs R D. primary mutations of HIV IN protein. These molecular 2007;8:155-68. scaffolds will give roadmap for further design of the 14. McColl DJ, Chen X. Strand transfer inhibitors of HIV-1 new inhibitors which can active against mutation, but integrase: Bringing IN a new era of antiretroviral therapy. further validated by in vitro assay. Antiviral Res. 2010;85:101-18. 15. De Clercq E. Anti-HIV drugs: 25 compounds approved within 25 years after the discovery of HIV. Int J Antimicrob Agents. ACKNOWLEDGMENTS 2009;33(4):307-20. 16. Loizidou EZ, Zeinalipour-Yazdi CD, Christofides T, Prof. Prabha and Dr. Pawan are very much thankful to Kostrikis LG. Analysis of binding parameters of HIV-1 integrase the CSIR, New Delhi for their research associate grant inhibitors: Correlates of drug inhibition and resistance. Bioorg Med Chem. 2009;17:4806-18. (File No.: 09/727(0095)/2012-EMR-I) for this work. 17. Mouscadet JF, Delelis O, Marcelin AG, Tchertanov L. Resistance to HIV-1 integrase inhibitors: A structural REFERENCES perspective. Drug Resist Updat. 2010;13:139-50. 18. Blanco JL, Varghese V, Rhee SY, Gatell JM, Shafer RW. 1. d’Angelo J, Mouscadet JF, Desmaële D, Zouhiri F, Leh H. HIV-1 resistance and its clinical implications. HIV-1 integrase: The next target for AIDS therapy? Pathol Biol J Infect Dis. 2011;203:1204-14. (Paris). 2001;49(3):237-46. 19. Unissa AN, Selvakumar N, Hassan S. Insight to pyrazinamide 2. UK Collaborative Group on HIV Drug Resistance; UK resistance in Mycobacterium tuberculosis by molecular CHIC Study Group. Long-term probability of detecting drug- docking. Bioinformation. 2009;4:24-9. resistant HIV in treatment-naive patients initiating combination 20. Gupta P, Garg P, Roy N. Comparative docking and CoMFA antiretroviral therapy. Clin Infect Dis. 2010;50:1275-85. analysis of curcumine derivatives as HIV-1 integrase inhibitors. 3. Pommier Y, Johnson AA, Marchand C. Integrase inhibitors to Mol Divers. 2011;15(3):733-50. treat HIV/AIDS. Nat Rev Drug Discov. 2005;4:236-48. 21. Gupta P, Roy N, Garg P. Docking-based 3D-QSAR study of HIV-1 4. Harrigan PR. HIV drug resistance over the long haul. Clin integrase inhibitors. Eur J Med Chem. 2009;44(11):4276-87. Infect Dis. 2010;50(9):1286-7. 22. Gupta P, Garg P, Roy N. Identification of novel HIV-1 integrase 5. Johnson AA, Marchand C, Pommier Y. HIV-1 integrase inhibitors using shape-based screening, QSAR, and docking inhibitors: A decade of research and two drugs in clinical trial. approach. Chem Biol Drug Des. 2012;79(5):835-49. Curr Top Med Chem. 2004;4:1059-77. 23. Gupta P, Sharma A, Garg P, Roy N. QSAR study of curcumine 6. Ding J, Das K, Moereels H, Koymans L, Andries K, Janssen PA, derivatives as HIV-1 integrase inhibitors. Curr Comput Aided et al. Structure of HIV-1 RT/TIBO R 86183 complex reveals Drug Des. 2013;9(1):141-50. similarity in the binding of diverse nonnucleoside inhibitors. 24. Gupta P, Garg P, Roy N. In silico screening for identification Nat Struct Biol. 1995;2:407-15. of novel HIV-1 integrase inhibitors using QSAR and docking 7. Goldgur Y, Craigie R, Cohen GH, Fujiwara T, Yoshinaga T, methodologies. Med Chem Res. 2013. DOI: 10.1007/s00044- Fujishita T, et al. Structure of the HIV-1 integrase catalytic 013-0490-y. domain complexed with an inhibitor: A platform for antiviral 25. Goodsell DS, Morris GM, Olson AJ. Automated docking of drug design. Proc Natl Acad Sci U S A. 1999;96(3):13040-3. flexible ligands: Applications of auto dock. J Mol Recognit. 8. Richman DD. HIV chemotherapy. Nature. 2001;410(6831):995- 1996;9:1-5. 1001. 26. Eswar N, Webb B, Marti-Renom MA, Madhusudhan MS, 9. Mehellou Y, De Clercq E. Twenty-six years of anti-HIV drug Eramian D, Shen MY, et al. Comparative protein structure discovery: Where do we stand and where do we go? J Med modeling using modeller. In: Current Protocols in Chem. 2010;53(2):521-38. Bioinformatics. Hoboken, NJ, USA: John Wiley & Sons, Inc.; 10. Hicks C, Gulick RM. Raltegravir: The first HIV Type 1 2002. integrase inhibitor. Clin Infect Dis. 2009;48:931-9. 27. Laskowski RA, MacArthur MW, Moss DS, Thornton JM. 11. Shimura K, Kodama E, Sakagami Y, Matsuzaki Y, Watanabe W, PROCHECK: A program to check the stereochemical quality Yamataka K, et al. Broad antiretroviral activity and resistance of protein structures. J Appl Crystallogr. 1993;26:283-91. profile of the novel human immunodeficiency virus 28. Colovos C, Yeates TO. Verification of protein structures: integrase inhibitor elvitegravir (JTK-303/GS-9137). J Virol. Patterns of nonbonded atomic interactions. Protein Sci. 2008;82(2):764-74. 1993;2:1511-9. 12. Hare S, Smith SJ, Metifiot M, Jaxa-Chamiec A, Pommier Y, 29. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Hughes SH, et al. Structural and functional analyses Belew RK, et al. Automated docking using a Lamarckian of the second-generation integrase strand transfer genetic algorithm and an empirical binding free energy inhibitor dolutegravir (S/GSK1349572). Mol Pharmacol. function. J Comput Chem. 1998;19:1639-62.

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