Computational Screening of CCR5 Inhibitors as ...

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RESEARCH ARTICLE

Computational Screening of CCR5 Inhibitors as Potential Entry Inhibitor Microbicides Using 3D-QSAR Studies, Docking and Molecular Dynamics Simulation Radhika Ramachandran1, Muthusankar Aathi2, Ruban Durairaj D1 and Shanmughavel Piramanyagam1,* 1

Department of Bioinformatics, Bharathiar University, Coimbatore-641046, Tamilnadu, India; 2National PostDoctoral Fellow, Membrane Protein Biology Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi-110067, India Abstract: Background: The chemokine receptor CCR5 acts as a co-receptor for HIV binding and it is considered as an important target by CCR5 antagonists. Entry inhibitor based microbicides gain much importance nowadays as these drugs act at an early stage of HIV lifecycle and thus hinder the viral replication process in humans. The present study intends to identify a CCR5 antagonist which could be developed as a microbicide using computational approaches.

ARTICLE HISTORY Received: July 20, 2016 Revised: September 06, 2016 Accepted: December 24, 2016 DOI: 10.2174/1570162X1566617010612 4216

Methods: The pharmacophore modeling and 3D QSAR studies was used to screen CCR5 antagonists with enhanced antagonist activity. The docking studies ranked the compounds according to their binding affinity and molecular dynamics simulation validated the stability of the enzyme-ligand complex. Results: A five point pharmacophore hypothesis HHPRR (2 hydrophobic; 1 positively ionisable; 2 aromatic ring) was generated. A statistically significant 3D QSAR model with 3 PLS factors was generated for common pharmacophore hypothesis HHPRR.3 with good correlation coefficient value (R 2=0.7483). The docking studies revealed that molecular interaction of CCR5 antagonists having good binding affinity are better than the microbicides taken for this study. The QSAR maps revealed the regions as a combined effect of hydrogen bond donors, hydrogen bond acceptors and hydrophobic groups which denoted the substitution of groups indicating the favorable and unfavorable regions for antagonist activity of hydroxypiperidine derivatives. The docking analysis and molecular dynamics simulation screened and validated CCR5 antagonists.

Conclusion: The present study was successful in identifying a CCR5 antagonist which could be developed as a microbicide.

Keywords: CCR5 antagonists, Docking, 3D-QSAR, Microbicides, Molecular dynamics, Pharmacophore. 1. INTRODUCTION Entry inhibitors act very early in the HIV life cycle long before integration occurs. HIV infects a cell by binding to CD4 receptor of the target cell membrane. In addition to binding to CD4, HIV must also bind with its co-receptors i.e., chemokine receptors CCR5 and CXCR4 expressed on the cell membrane to enter a T cell [1]. The phenotypic switch from R5 to X4 viruses in vivo typically occurs only after several years of infection [2]. The CCR5 receptor belongs to the rhodopsin family of G-protein coupled receptors (GPCR’s) characterized by seven transmembrane motifs. CCR5 has become a very attractive target for the development of novel anti-HIV drugs *Address correspondence to this author at the Department of Bioinformatics, Bharathiar University, Coimbatore-641046, India; Tel: +0422 2422655; E-mail: [email protected]

1570-162X/17 $58.00+.00

as it plays a prominent role in HIV transmission and pathogenesis. The primary infection occurs by R5 viruses and they usually persist during the entire course of infection. The significance of CCR5 is very much important during HIV infection as the genetically CCR5-deficient (Δ32) individuals are highly resistant to HIV-1 infection in high-risk populations, and heterozygous Δ32 individuals are often long-term nonprogressors [3]. Since the discovery of CCR5 as a coreceptor for HIV-1 cell entry, there has been an increased attempt in the pharmaceutical industry to develop CCR5 antagonists [4]. This human receptor avoids problems like drug resistance and viral mutation. Some of the naturally available ligands of CCR5 are chemokines CCL3 (MIP-1α), CCL4 (MIP-1β) and CCL5 (RANTES) [5]. A number of small-molecule CCR5 antagonists tested in different phases of clinical trials and few of them tested as microbicides were reported [2]. The protein drugs have poor oral bioavailability whereas small molecules have good oral bioavailability.

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Hence, small molecules are given much importance as entry inhibitors. Microbicides are chemical substances which can be applied in vagina or rectum for preventing sexual transmission of HIV. Some of the vaginal microbicides acting as entry/fusion inhibitors such as PRO-2000, Carraguard, Cellulose Sulfate, Vivagel, BMS-806, BMS-378806, CMPD167 were reported [6]. A decade ago, there were approximately 60-80 candidate microbicides entered pre-clinical trials and they were evaluated by only in-vitro studies. Following this, a variety of CCR5 inhibitors which included specific mAbs (PRO-140), modified chemokines (NNY-RANTES, PSC-RANTES) and small molecule inhibitors (SCH-C, SCH-D, TAK-779) modified chemokine receptors which inhibited HIV in cell cultures were studied [7]. According to the latest reports, NIHfunded ASPIRE study and a multinational clinical trial called The Ring Study proved that slow release of the antiretroviral drug dapivirine from the vaginal ring provided a modest level of protection against HIV infection in women (https://www.aids.gov). Homology modeling was the main computational method for CCR5 antagonist development before 2013. Recently, the 2.7 Å-resolution crystal structure of human CCR5 bound to the HIV drug Maraviroc was reported (PDB ID: 4MBS, A chain). Before the availability of this structure, homology models of CCR5 were built for the sequence with swissprot accession number P51681 using the crystallographic structure of bovine rhodopsin (eg: PDB ID: 1F88) as the template structure. Several studies were performed on the homology models of human CCR5 structure using the XRD structure of the bovine rhodopsin receptor as the template. A homology model of CCR5 was used due the absence of a XRD structure to study the detailed interactions of CCR5 with their antagonists [8]. Since 2010, homology models of CCR5 were built based on the reported PDB structure of CXCR4 (PDB ID: 30DU A chain) as a template [9]. A study was conducted involving integrated computational tools for identification of CCR5 antagonists as HIV-1 entry inhibitors used homology model of CCR5 by taking XRD structure of CXCR4 as template [10]. A study was carried out on the interaction of small molecule inhibitors like TAK-779, AD101 and SCH-350581 with CCR5 [11]. TAK-779, a quartenary ammonium anilide was the first small molecule CCR5 antagonist reported [12], followed by SCH-C, an oximino-piperidino-piperidine amide [13] and AD-101 (SCH350581) had antiviral activity against HIV-1 infected humans. The present work aims to explore potential drug candidates to be developed as microbicides by in-silico approaches. The advantage of computational approach of drug design is that it overcomes the challenges and many practical difficulties in real time research of microbicide development. It helps to pursue research in an easy, effective manner and at a less cost. It reduces time to be spent on real time clinical research which would take years and years for a drug to be made available over the counter. Hence, the computational approaches undertaken here on CCR5 antagonists is expected to be very much useful for the research community by proposing the potential drug candidates to be developed as microbicides.

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2. MATERIALS AND M ETHODS 2.1. Data Set A ligand library of 345 diverse CCR5 antagonists with known experimental inhibitory concentration (IC50) values were retrieved from published literature (Supplementary Material) and their structures were generated using freeware like Chemsketch (http://www.acdlabs.com) and Chemdraw (http://www.cambridgesoft.com/software/chemdraw). The CCR5 antagonists 1 to 39 [14] 40 to 70 [15], 71 to 77 [16], 78 to 87 [17], 88 to 106 [18], 107 to 122 [19-20], 123 to 179 [21-22], 180 to 226 [23], 227 to 237 [24], 238 to 266 [25], 267 to 291 [26], 292 to 300 [27], 301 to 345 [28] were retrieved. The biological data reported as pIC50 for binding MIP-1α to CCR5 in a common antiviral assay was derived from the above mentioned literatures. pIC50 is the dependent variable that characterizes the biological parameter for the developed docking model. A few of the microbicides such as Vicriviroc, Maraviroc and TAK-779 were taken as reference microbicides for this study [29-30]. 2.2. Ligand Preparation The ligand structures were modelled and optimized using ACD/Chemsketch and saved in .mol file format (http://www.acdlabs.com/download). Ligands were prepared by Ligprep module [31] using default parameters and were assigned an appropriate bond order. Ligprep converted 2D to 3D structure by including tautomeric, stereo chemical and ionization variations as well as energy minimization and flexible filters to generate fully customized ligand libraries that are optimized for computational analysis. The structures were ionized at neutral pH 7.0. Compounds with atom types that were not recognized by Ligprep were eliminated from the data set. For each processed input structure, it produced a single low-energy 3D structure with correct chiralities, unwanted molecules removed and added hydrogen's. 2.3. Protein Preparation The three dimensional structures of CCR5 receptor was obtained from Protein Data Bank (PDB) (PDB ID: 4MBS, A chain). The 2.7 Å-resolution crystal structure of human CCR5 bound to the HIV drug Maraviroc was reported in 2013. The active site residues of the CCR5 was found to be Gln 194, Thr 195, Ile 198, Phe 109, Phe 112, Tyr 108, Trp 86, Tyr 89, Trp 248, Thr 259, Leu 255, Tyr 251, Glu 283, Met 287, Tyr 37, Met 279 [32]. The protein preparation was carried out using the protein preparation wizard of Schrodinger Suite 2012 [33]. The protein preparation consisted of fixing structures, deleting unwanted chains and waters, fixing hetero groups and finally optimizing the fixed structure. Hydrogen atoms were added by applying an all atom force field. The force field applied was OPLS_2005 and the RMSD of the atom displacement for terminating the minimization was specified as 0.30 Å. The grid was generated for prepared proteins i.e. an all atom structure with appropriate bond orders and formal charges considering the active site residues. The maximum size of the enclosing box was set to 50 Å. The CCR5 receptor grid size was set to be 12, 14 and 6 and XYZ coordinates as 52.6811, 7.6063 and -17.569 which were saved in the (.grd)

Computational Screening of CCR5 Inhibitors as Potential Entry Inhibitor

format. The force field applied was OPLS_2005. All the parameters and the grid size enclosing active site residues were kept as default. The center of the box was specified by the selection of the above mentioned active site residues. 2.4. Pharmacophore Modelling The pharmacophore modelling for CCR5 antagonists was carried out using the PHASE module [34] of Schrodinger molecular modelling package. The common pharmacophore hypothesis was identified by dividing the dataset into active and inactive sets. The ligands with property pIC50 – Exp were selected as the experimental activity variable in order to build model. The pIC50 value was calculated using the formula pIC50 = -log IC50 . The most active and inactive compounds were considered for developing common pharmacophore hypothesis based upon the pIC50 values. The maximum and minimum pIC50 value was found to be 2.347 and -4.230. The molecules were divided into active and inactive sets based upon the maximum and minimum pIC50 values. The dataset was randomly divided into 60% of the molecules in the training set (active) and 40% of the molecules in the test set (inactive). The training set consists of molecules used for pharmacophore model generation and test set consists of molecules used for validating the obtained pharmacophore models. Hence the molecules with pIC50 values -0.7 and above were considered active while the molecules with pIC50 values -1.0 and below were considered inactive. The inactives eliminated hypothesis that did not provide good explanation of the activity on the basis of pharmacophore alone. Active set determined the pool of pharmacophore models generated and the initial scores assigned to them. The pharmacophore features in the ligand conformations used for hypothesis generation included Hydrogen bond acceptor (A), Hydrogen bond donor (D), Hydrophobic group (H), Positively ionisable (P), Negatively ionisable (N), and aromatic rings (R) defined by a set of chemical structure patterns. The pharmacophore of active ligands that contain identical sets of features with very similar spatial arrangements were grouped together to give rise to a common pharmacophore hypothesis (CPH). A common 5-point or 5 site pharmacophore with a terminal box size of 1Å was considered. The parameters mentioned in this method section are default. In this manner, many CPH’s were generated and the best one was taken up for further studies. The most active ligand was taken as the reference ligand showing highest activity and fitness score 3. The inactive/non-modelled molecules in the dataset were aligned, based on the matching of at least three of the pharmacophore features. The maximum and minimum numbers of sites were set to be 5 and 3 respectively. A common pharmacophore is matched to a subset of active ligands when the actives are highly diverse. The various CPH generated were ranked according to the survival score. The hypotheses were scored using default parameters like site, vector, volume, selectivity, number of matches and energy terms. A common pharmacophore model HHPRR.3 for CCR5 antagonists was generated after the creation and identification of pharmacophoric sites in all the molecules of the dataset. The parameters mentioned here are default.

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2.5. QSAR Studies The 3D QSAR modelling for CCR5 antagonists was carried out using the PHASE module of Schrodinger package [34]. PHASE QSAR models were based on partial least squares (PLS) regression. The various CPH’s generated were evaluated and validated by means of correlation values obtained by correlating experimental and predicted activities by randomly selecting 60% as training set and 40% as test set molecules from the dataset of ligands using Partial Least Square (PLS) analysis. The PLS regression analysis correlated the experimental and predicted biological activities and thus evaluated the reliability of models. It also cross validated the reliability of models by “leave one out” (LOO) procedure where one molecule was left out from the dataset and its activity was predicted by the model generated from the rest of the dataset. QSAR models were generated that were atom-based. The atom classes used for building 3D QSAR models included: (i) D: hydrogen-bond donor; (ii) H: hydrophobic or non-polar; (iii) W; electron-withdrawing (hydrogen bond acceptors) (iv) P: Positively ionizable; (v) R: Aromatic rings. The atom-based QSAR models were built using default parameters by setting Grid spacing to 1.0 and maximum PLS factors to 3. When the data for the PLS regression models filled the QSAR results table, each regression was considered as a QSAR model. Finally, the QSAR models were visualized to find out the structure – activity relationship. Here, the hypothesis HHPRR.3 for CCR5 antagonists was used for QSAR model generation. 2.6. Molecular Docking Molecular docking was carried out using Glide (Grid based Ligand Docking with Energetics) [35]. The molecular docking between CCR5 antagonists and its receptor was carried out using previously calculated grids considering the above mentioned active site residues of crystal structure of CCR5 (PDB ID:4MBS A Chain). The library of small molecules used for docking was initially screened using Standard Precision (SP) docking followed by Extra Precision (XP) docking and the XP descriptor information for the results files were saved. Ultimately, optimization of the ligand structures in the field of the receptor was done and then the ligands were scored mainly in terms of glide score, H-bond score and the number of hydrogen bonds. 2.7. Molecular Dynamics Simulation Studies Molecular dynamics simulation of the CCR5_ Antagonist 216 docked complex structure was carried out using GROMACS 5.1 package [36] to investigate the structural stability of the protein-ligand complex. Steepest descent algorithm was used for carrying out the energy minimization process with the maximum of 50,000 steps by applying GROMOS96 43a1 force field with an energy tolerance of 1000 KJmol-1 nm-1. SPC (simple point charge) water model in 1nm sized cubic box was used for the solvation of the system. Neutralization of the system was done by adding 14 chloride (Cl-) ions and the periodic boundary conditions were applied in all the directions. Linear constraint algorithm (LINCS) [37] was used to constrain all bond lengths in the system. Particle mesh Ewald methods (PME) [38] were used for computing the long-range


electrostatics in the system with a Fourier spacing of 0.16 nm and cutoff of 1.2 nm. V-rescale weak coupling method was implemented for the regulation of the temperature (310 K). Both ensembles of equilibration were carried out by using ParrinelloRahman [39] coupling method. In NVT ensemble (constant number of particles (N), volume (V) and temperature (T), a constant temperature of 310K and a coupling constant of 0.1 ps for 100 ps were used, whereas for NPT ensemble (constant number of particles (N), pressure (P) and temperature (T), a constant pressure of 1 bar and the same coupling constant parameter were used. This preequilibrated system was further taken for 5ns (25,00,000 steps) production molecular dynamics simulation (MDS) with a time step of 2 fs. As experimentally determined crystal structures are already in minimized state, 5 ns simulation time is more than enough in order to predict the ligand stability. Every 2 ps, coordinates of the structure were saved and analyzed using the analytical tool available in the GROMACS package. The computation was performed using Intel (R) Xeon (R) CPU 2.00 GHz, Ubuntu a Linux based operating system.5. 3. RESULTS AND DISCUSSION 3.1. Pharmacophore & Atom Based 3D-QSAR Modeling Ligand based drug design relies on knowledge of other molecules that bind to the biological target of interest. The molecules may be used to derive a pharmacophore which defines the minimum necessary structural characteristics a molecule must possess in order to bind to the target [40]. The best five point common pharmacophore hypothesis HHPRR.3 was generated for the active data set (pIC50 > -0.7) with the pharmacophore features 2 hydrophobic, 1 positively ionizable and 2 aromatic rings which is represented in Fig. (2). In an earlier study, pharmacophore modeling and 3DQSAR studies on N3-Phenylpyrazinones as corticotropinreleasing factor 1 receptor antagonists was studied in which they generated a six point pharmacophore hypothesis AADHHR.47 which yielded a statistically significant 3DQSAR model with 0.803 as R2 value [41]. The results of our study matched very well with the above mentioned results following the same methodology. The variant HHPRR.3 with the Survival Score of 3.527, correlation regression coefficient R2, Pearson–R 0.6109, cross validated R2 0.6548 and F value 155.1 was selected to be the common pharmacophore hypothesis. The best five pharmacophore hypothesis whose survival score is above 3 are represented in Table 1. The most active compound of the CPH is shown in Fig. (1A) and all active molecules/modeled molecules aligned to it are shown in Fig. (1B) respectively. The training set molecules overlaid on the pharmacophore model HHPRR.3 very well producing a very good alignment which was used for building QSAR model. The distances and angles between various predicted pharmacophore features for hypothesis HHPRR.3 is also represented in Fig. (2A and 2B) respectively. The best CPH generated for the selected CCR5 antagonists was subjected to QSAR model development and its

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validation using PHASE statistical analysis. The inactive/non-modeled molecules in the dataset were aligned, based on the matching of at least three of the pharmacophore features. The hypotheses were sorted by survival score. The reference ligand ie., antagonist no. 25, which had a fitness score 3.0 was considered as the maximum possible score with the selected scoring options. The greater the fitness score, the greater the activity prediction of the compound. The fitness function checks feature mapping as well as it contains a distance term for measuring the distance that separates the feature on the molecule from the centroid of the hypothesis feature [41]. In a previous study, survival score was kept as a criterion to ascertain the quality of alignment [42]. Likewise, in our study, the variant HHPRR.3 with highest survival score 3.527 was chosen to be the common pharmacophore hypothesis for CCR5 antagonists. The regression analysis was performed by constructing a series of models by increasing the PLS factors from 1 to 3. In an earlier study also, a QSAR model was built by maximum number of PLS factor 3 and 24 four featured probable CPHs were generated from the list of variants [43]. In a study reported earlier, Partial Least Squares (PLS) methodology was performed to quantify the relationships between the Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) descriptors and the biological activities. The reliability of the models was evaluated by cross-validation analysis using “leave one out” methodology. A cross-validated coefficient, Q2, determined the model predictive power and the non-crossvalidated Pearson coefficient, R2, was calculated. The robustness and the statistical confidence of the derived models were assessed by bootstrapping analysis. The predictive R2 was calculated to assess the quality of 3D-QSAR models’ predictive abilities [44]. Apart from the survival score analysis, the QSAR model was validated by predicted activity of training set molecules. The models having a high correlation value between observed and predicted activity for training set molecules were considered. It was further validated by cross validated correlation value between predicted activities for test set molecules. actions. The correlation graph denoting regression coefficient, R2 value was 0.7483 for training set molecules represented in Fig. (3A). The PHASE statistical analysis using PHASE Partial Least Square (PLS) analysis for CCR5 test set molecules are shown in Fig. (3B). The validity of each of the models was predicted from the calculated correlation coefficient for the randomly chosen test set comprising of diverse structures. The squared correlation for the test set Q2 determines the effective predictability of the final QSAR model and the Q2 value was found to be 0.6548. For a reliable model, the squared predictive correlation coefficient should exceed 0.60 [45-46]. In a similar study, pharmacophore hypotheses were generated by CATALYST/HipHop using non-sugar derivatives for further use in the screening of new lead derivatives. The compounds were taken as training set to generate pharmacophore models by HipHop methodology. One molecule was taken as the reference which mapped all the features of the hypothesis while the other molecules were allowed to map partially on the hypothesis. They generated a five featured hypothesis and their


Table 1.


5

Five point pharmacophore hypotheses for CCR5 antagonists.

Hypothesis

Survival Score

SD

R-squared

F

P

RMSE

Q-squared

Pearson-R

HHPRR.3

3.527

0.5247

0.7483

155.1

1.87E-17

0.9097

0.6548

0.6109

AHPRR.30

3.466

0.6031

0.6253

112.9

3.39E-18

1.2283

0.701

0.7035

HHPRR.36

3.321

0.7087

0.6152

203

4.15E-28

1.1181

0.539

0.7217

HNPPR.134

3.235

0.8117

0.6109

96.1

5.92E-16

1.0169

0.7018

0.6585

HNPPR.105

3.18

0.7487

0.6923

132.2

2.02E-19

0.9643

0.507

0.6905

It shows the F values represent the statistically significant regression, P values indicate the significance level of variance ratio and Q-squared value suggests the robustness of models, R-squared value denotes the correlation coefficient, SD represents the standard errors occurring in hypotheses, Pearson R denotes correlation between the predicted and observed activity for the test set, RMSE stands for Root Mean Square Error.

Fig. (1). The best five point pharmacophore hypothesis HHPRR.3 (A) The most active data set compound of the Pharmacophore hypothesis HHPRR.3 (B) Training set compounds onto the ligand conformation hypothesis HHPRR.3. Color codes for the pharmacophoric features - Green sphere for hydrophobic (H); Orange ring for aromatic ring (R); Blue sphere for positively ionizable (P).

ranking was carried out. The first hypothesis was considered for further analysis because of its best score and fitness value 5 [47]. Based upon the atom-based PHASE 3D-QSAR, the volume occlusion maps represented by color codes was generated. The Fig. (4) shows the colour maps for CCR5. These maps denote the regions of favorable and unfavorable interactions. Based upon the sign of the coefficient values, color coding was represented. The default color code is blue for positive coefficients and red for negative coefficients. Positive coefficients indicate an increase in activity whereas negative coefficients indicate a decrease. Hence the blue colored region indicating hydrophobicity in the region could improve the activity of novel 4-hydroxypiperidine derivatives CCR5 antagonists taken for study whereas the red color

indicates the disfavoring of the placement of hydrophobic groups. In a study, 3D-QSAR analysis of 1,3,4-trisubstituted pyrrolidine-based CCR5 receptor inhibitors was carried out to investigate the interactions between CCR5 receptor and their inhibitors [48]. As per our study, the results were found to be in good agreement with the earlier results which included correlation coefficient (R2), cross correlation coefficient (Q2), 3D-QSAR maps and the binding modes of the ccr5 antagonists. By the same method mentioned above, a model was developed which showed a strong correlative and predictive capability having a cross validated correlation coefficient [49]. Based upon the above cited earlier studies, our results were found to be in good agreement with their results.


Ramachandran et al.

Fig. (2). Pharmacophore features of the best common pharmacophore hypothesis HHPRR.3 (A) Distance calculations (B) Angle calculations.

Fig. (3). QSAR scatter plot. (A) The graph representing the training set correlation coefficient (R2=0.7483) of the Phase hypothesis HHPRR.3 (B) Test set correlation coefficient (Q2=0.6548) of the Phase hypothesis HHPRR.3.

3.2. Molecular Docking CCR5 antagonists were developed using the homology model of CCR5 till the year 2013 due to the unavailability of XRD structure of CCR5. Several studies were performed on the homology models of human CCR5 structure using the Xray structure of the bovine rhodopsin receptor as the template and also based on the reported XRD structure of CXCR4 as a template. When the 2.7 Å-resolution crystal structure of human CCR5 bound to the HIV drug Maraviroc has been de-

posited in the Protein Data Bank (PDB), it has been used for research purpose. The reported structure revealed a ligandbinding site that was distinct from the proposed major recognition sites for chemokines and gp120, providing insights into the mechanism of the allosteric inhibition of chemokine signaling and HIV entry. The high-resolution crystal structure of CCR5 has been used for structure-based drug discovery for the treatment of HIV-1 infection [50].



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pocket making no contacts with the ECL’s. In our results, molecular interaction of CCR5 antagonists given in table 3 interacted with the residues Ala 159, Lys 197, Ile 198, Ile 200, Leu 201 comprising the same ligand binding pocket of maraviroc present in helices V and VI of CCR5 receptor. Most of the other docked antagonists were found to interact in the similar fashion exhibiting allosteric mechanism. The docking results also disclosed that each of the compound formed one or two hydrogen bonds interacting with the helical regions. The glide scores for the CCR5 antagonist’s dataset taken for this study was found to be much better when compared to microbicide compounds selected for this study. Thus, the CCR5 antagonists can be proposed as compounds to be developed as microbicides.

Fig. (4). The QSAR model showing the active features of the common pharmacophore model generated for the best hypothesis HHPRR.3. Blue colors indicate favorable regions, while red color indicates unfavorable regions for the activity.

The microbicide compounds selected for this study showed inhibitory activity against CCR5 by interacting with various residues of CCR5. The microbicides such as Maraviroc interacted with residues TRP 190; TAK-779 interacted with residues SER 180 and Vicriviroc interacted with residues ARG 305 and they produced dock score of -2.14, - 5.29 and - 4.29 respectively (Table 2) with the formation of hydrogen bonds. The docking of the 345 CCR5 antagonists into the receptor active site had good binding affinity with the receptor. Glide score range was determined to be in the range -9.0 to -8.9 and top ten poses with best glide scores are given in Table.3. The docked conformation having lowest docking energy was selected as the most probable binding conformation. The CCR5 antagonists 216, 218, 93, 162, 176, 225, 161, 194, 165 and 20 were found to be the top scoring docked poses where antagonists 216, 218, 225, 194 are antagonists containing 4-(pyrazolyl) piperidine side chains, antagonist 93 is a 1,3,4-Trisubstituted Pyrrolidine CCR5 antagonist; antagonists 162, 176, 161, 165 are Guanylhydrazone derivatives, antagonist 20 is a 4- hydroxypiperidine derivatives. In our docking results Fig. (5) The docked conformation having lowest docking energy was selected as the most probable binding conformation. The CCR5 antagonists 216, 218, 93, 162,176, 225, 161, 194, 165 and 20 were found to be the top scoring docked poses where antagonists 216, 218, 225, 194 are antagonists containing 4-(pyrazolyl) piperidine side chains, antagonist 93 is a 1,3,4-Trisubstituted Pyrrolidine CCR5 antagonist; antagonists 162, 176, 161, 165 are Guanylhydrazone derivatives, antagonist 20 is a 4hydroxypiperidine derivatives. In our docking results (Fig. 5) shows the best interaction of antagonist 216 with CCR5 receptor. They showed interaction with mainly residues like Ala 159, Lys 197, Ile 198, Ile 200, Leu 201 and the microbicides interacted with residues Arg 305, Trp 190 and Ser 180. It was reported that in the CCR5/ Maraviroc structure, the ligand occupies the bottom of a pocket defined by residues from helices I, II, III, V, VI and VII and interacted with residues Tyr 37, Tyr 89, Thr 195, Tyr 251,Thr 259 and Glu 283. Maraviroc occupied a deeper and large area of the

In a study, involving integrated computational tools used for identification of CCR5 antagonists as HIV-1entry inhibitors, a receptor grid box was defined around the active site residues, namely Phe 85, Trp 89, Leu 104, Tyr 108, Ile 198, Tyr 251 and Glu 283 of the crystal structure of CXCR4 and majority of the compounds occupied the spaces between the residues Tyr 89, Trp94, Glu 283, Leu 104, Thr 195, Tyr 251, Phe 109, Ile 198 and Trp 248 [10]. Maraviroc also showed interaction with some of the above mentioned residues present in crystal structure of CCR5. Hence, the active site residues of CCR5 and CXCR4 are found to be similar. As mentioned in an earlier study, interaction of small molecule inhibitors like TAK-779, AD101 and SCH-350581 with CCR5 which showed antiviral activity against HIV-1 infected humans was carried out [11]. These molecules interacted with the majority of the residues clustered in transmembrane helices of CCR5 H1, H2, H3 and H7 [51-52]. Similarly in another study, the interaction mechanism of CCR5 antagonists SCH-C and Aplaviroc was noted. Aplaviroc binded in a shallow pocket underneath the extracellular β hairpin loop whereas SCH-C bound in a pocket lying in the transmembrane bundle up to the middle of helix 3 [53]. In the above said study, the ligands showed interaction with transmembrane helices as well as extracellular loops of CCR5 model. Based upon the above cited literatures, many studies have been undertaken to explore the interactive mechanism of several small molecules with XRD structure of CCR5 as well as with its homology model. Several studies have been carried out for different groups of CCR5 antagonists and their analogues using the same computational approached we have used here. The present study intends to identify a CCR5 antagonist with more inhibitory activity and with very good binding affinity against the crystallized structure of CCR5 from a dataset of chemically diverse 345 CCR5 antagonists taken for this study. 3.3. Molecular Dynamics Simulation The docked complex of CCR5_Antagonist 216 was subjected to molecular dynamics simulation studies for the time period of 5 ns. The stability of the complex was evaluated with the help of RMSD and RMSF plots. The RMSD plot showed that the CCR5_Antagonist 216 complex was more stable up to 5 ns simulation time period. The backbone RMSD of the CCR5_Antagonist 216


Table 2.

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Dock score of Microbicides acting as CCR5 antagonists.

Ligand

GScore

HBond Score

Number of Hydrogen bonds

Interacting Residues

H Bond distance (A0 )

TAK-779

-5.29

-0.13

1

SER 180

2.341

Vicriviroc

-4.29

-0.03

1

ARG305

2.459

Maraviroc

-2.14

-0.7

1

TRP 190

1.838

This table represents the glide score, hydrogen bond score, interacting residues, number of hydrogen bonds and their distance for the selected microbicides acting against CCR5.

Table 3.

Dock score of best ten CCR5 antagonists.

Antagonist Name

Glide Score

Number of Hydrogen bonds

Interacting Residues

H Bond distance (A0 )

CCR5_Antagonist216

-9.982

2

LYS197

1.9, 2.5

CCR5_Antagonist218

-9.674

2

LYS197

2.1, 2.1

CCR5_Antagonist93

-9.538

2

LYS 197, ILE 198

1.6, 2.3

CCR5_Antagonist162

-9.465

1

ILE 200

2.8, 2.9

CCR5_Antagonist176

-9.207

1

ALA 159

2.0

CCR5_Antagonist225

-9.080

2

ILE198, ILE200

1.8, 2.2

CCR5_Antagonist161

-9.073

1

ILE200

2.1

CCR5_Antagonist194

-9.052

1

LYS197

1.8

CCR5_Antagonist165

-8.987

2

ILE200, LEU201

1.9, 2.6

CCR5_Antagonist20

-8.900

1

ILE198

1.8

Tabular representation of dock score of best ten CCR5 antagonists obtained out of 345 docked antagonists. The highlighted compound has the best glide score than the rest of the compounds.

Fig. (5). Molecular interaction of CCR5_Antagonist 216 with CCR5 receptor. Hot pink color represents receptor, cyan color is compound, orange color is interacting residue LYS197 and yellow color dotted lines are hydrogen bond interactions.

complex is around 0.4 nm over the 5 ns simulation time. The RMS fluctuations of the protein backbone residues having a high peak denoting much fluctuation in the region of residues (Leu137, Ile316 and Glu329) ranging from 0.5 to 0.6 suggesting as that these residues involving in the formation of loop. The RMSD and RMSF plots are represented in Fig. (6A and 6B) respectively. The potential energy of

CCR5_Antagonist 216 complex ranges in between1.586e+06 KJ/mol to -1.584e+06 KJ/mol. The potential energy plot is shown in Fig. (6C) suggesting that the energy of the CCR5_Antagonist 216 complex is minimized and equilibrated well over 5 ns simulation time. Two hydrogen bond interactions were observed in the CCR5_Antagonist 216 complex at the starting of simulation and it is increased



9

Fig. (6). CCR5_Antagonist 216 complex results of dynamics. (A) Backbone RMSD (B) RMS fluctuation (C) Potential energy of the CCR5_Antagonist216 complex over the simulation period of 5ns (D) Total H-bonds interactions found between the protein and ligand of CCR5_Antagonist216 complex over 5ns simulation time.

to 4 hydrogen bond interaction at the end of the simulation suggesting that the ligand is bounding well in the binding region. The hydrogen bond interactions found at the end of 5ns simulation are, 2 H-Bonds with Lys197, 1H-Bonds each with Ile200 and Leu201 respectively. The total H-bond interactions of CCR5_Antagonist 216 complex is shown in Fig. (6D). CONCLUSION Computational approaches like pharmacophore modeling and 3D QSAR studies was undertaken to investigate the structural characteristics of CCR5 antagonists contributing to their activity. Docking was carried out to sort the antagonists which showed better interaction with the receptor protein. The interaction of CCR5 antagonists were found to be relatively much better than the microbicides taken for this study. The best five point pharmacophore hypothesis was used for developing 3D QSAR model and the five point CPH was found to contain 2 hydrophobic features, one positive feature and two aromatic rings. The atom based QSAR model and molecular interaction studies provided an insight on the structural modifications which could enhance the activity of antagonists and produce good receptor-ligand interactions. The molecular dynamics simulation was also carried out to analyze the stability of interactions between the CCR5 antagonist 216 complex and it was found to be reliable. Hence, the present study intends to study the CCR5 antagonists included in the dataset and screen the best compound out of

them using docking and 3D-QSAR studies. These mechanisms shed light on the binding mode of CCR5 antagonists and the essential pharmacophore features required for its antagonist activity. To summarize the present study, pharmacophore modeling, 3D QSAR studies, docking and molecular dynamics simulation was carried out for a dataset of 345 CCR5 antagonists comprising different classes of CCR5 entry inhibitors. The characterization of enzyme-ligand interactions reveal the allosteric binding mode and the inhibitory mechanism of the CCR5 antagonists selected for the study. Moreover, the molecular dynamics simulation confirmed the stability of enzyme-ligand complex. Thus, the 3D QSAR models developed and the molecular interaction study of the CCR5 receptor and its antagonists gives us an idea to modify the structure of the most active CCR5 antagonists in order to increase their biological activity. This study has compared chemically diverse 345 CCR5 antagonists from different sources mentioned in the datset and has screened a group of molecules from the dataset which can be considered suitable for microbicide development. Thus, the in silico approaches has proposed lead compounds acting as entry inhibitors for microbicide development in an easy manner and in less time than the traditional methods. This research is limited to computational analysis and the results obtained needed to be further validated by in-vitro and in-vivo studies by designing and synthesizing new small molecules based upon the pharmacophore model, QSAR model and docked model developed in this study.


Ramachandran et al.

CONFLICT OF INTEREST The authors confirm that this article content has no conflict of interest.

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ACKNOWLEDGEMENTS

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We would like to thank Indian Council of Medical Re- search for funding this research by providing Senior Research Fellowship to Radhika.R and Department of Biotechnology – Bioinformatics Facility (DBT-BIF) for providing lab facilities. SUPPLEMENTARY MATERIAL Supplementary material is available on the publishers web site along with the published article.

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