Structural requirements for potential HIV-integrase inhibitors identified using pharmacophore-based virtual screening and molecular dynamics studies
Summary (4 min read)
- Human immunodeficiency virus (HIV) is the aetiological agent of acquired immunodeficiency syndrome (AIDS) which destroys the immune system of the body leaving the victim vulnerable to infections, malignancies and neurological disorder.
- To date the highly active antiretroviral therapy 6 which is combined therapy using the above classes of inhibitors is widely used for patients with advance infection but has failed to eradicate the virus.
- Several research groups worldwide identified integrase inhibitors 11-17 using pharmacoinformatics approaches for potential application for HIV therapy.
- The pharmacophore models are widely used in the field of drug discovery by providing valuable information to study SAR and reveals the mechanism of ligand-target relationship by deducing the nature of functional groups and non-covalent bonding patterns 20 .
Materials and methods
- At present, several popular commercial and freeware packages are used for ligand-based method to derive 3D pharmacophore models and also help in estimation of biological activities.
- This is commercially available software containing several module packages and widely used in pharmacoinformatics drug discovery 25-28 .
- The 3D QSAR Pharmacophore Generation module takes input of structure and activity data for a set of potential HIV-integrase ligand to create hypotheses.
- The HypoGen allows identification of hypotheses that are common to the ‘active’ molecules of training set but absent in the ‘inactive’ molecules, whilst HipHop identifies hypotheses present both in ‘active’ and ‘inactive’ compounds.
- In the present work the HypoGen module was used to generate the hypotheses.
- It was also kept in mind that no compounds were common in any two training sets except for the most active and least active molecules.
- For each compound, the coordinates were corrected, atoms were typed and energy was minimized using the modified CHARMm force field 30, 31 .
Pharmacophore model generation
- In order to generate the pharmacophore space model the 3D QSAR Pharmacophore Model Generation module of DS was used.
- Out of BEST/FAST, the BEST method was considered to obtain multiple acceptable conformations which provides complete and enhanced coverage of conformational space with help of rigorous energy minimization and optimizing the conformations by the poling algorithm 32 .
- In final step, the remaining hypotheses improve the score with help of small perturbations 33, 35 .
- All four sets were used to develop the pharmacophore models and statistical parameters were calculated based on training & test sets molecules.
- The information concerning the structure and the biological activity of test set compounds of Set 1 is provided in Table S5 in the supplementary information, while all the data regarding the training set (Set 1) molecules are reported in Fig. Fig. 1 2D chemical structures of the training set compounds and the activity values (IC50) are given in the parentheses.
- Leave-one out (LOO) cross-validation is one of the important internally validation protocol of the selected model, in which one compound was randomly deleted from training set in each cycle and model redeveloped using the rest of the compounds with the same parameters used in original model.
- The activity of the deleted compounds was calculated based on the newly developed model.
- The above procedure applied for all molecules of the training set and predicted activity recorded.
- This parameter measure the degree of deviation of the predicted activity from the observed ones.
Cost function analysis
- To select the final pharmacophore model several statistical parameters were employed at the time of hypothesis generation, these included spacing, uncertainty, and weight variation.
- The spacing represents the minimum inter-features distance which may be permissible in the final hypothesis, while the weight variation reflects the level of magnitude explored by the hypothesis in which every feature implies some degree of magnitude of the biological activity of the compound.
- The uncertainty returns the error of prediction which signifies the standard deviation of the error cost.
- The weight cost is directly proportional to the deviation of weight variation from its input value.
- The configuration cost implies entropy of hypothesis space and it is reported that value should have <17 for a good pharmacophore model.
Test set prediction
- Any robust pharmacoinformatics model should have the capability to predict the activities of the compounds other than training set.
- In case of moderately active compounds, 17 and 6 compounds were underestimated as highly active and overestimated as least active respectively.
- The remaining 450 compounds were classified in their observed and predicted activity correctly (Table S5 in supplementary file) which suggests that the selected model was able to provide accurate estimation for the biological activities of external compounds.
- The correlation (R) between observed and estimated activity of test compounds was found to be 0.915 and the R 2 pred value of 0.847 with error of prediction (sp) of 0.697.
- The r 2 m(test) and Δr 2 m(test) were found to be 0.636 and 0.130 respectively, explaining that selected model has adequate predictive potential.
- Decoy set validation of pharmacophore model is one of the important approaches to evaluate the screening capability of the model.
- The hypo1 was screened by a set of 900 HIVintegrase decoys obtained by DecoyFinder1.1 amalgamated with 80 active HIV-integrase inhibitors.
- The ROC plot was derived for the model and given in Fig. 5 which indicated that actives and decoys are well-classified.
- Average EF 1 % value for pharmacophore model was found to be 9.80 which indicated that model has identified active compounds very well and the top 1% hit is enriched with active compounds.
- The molecular docking study gives the accurate and preferred orientations of the molecule at the receptor site of the macromolecule.
- The crystal structure of HIV integrase (PDB ID: 1QS4) was collected from RCSBProtein Data Bank.
- In order to validate the docking protocol, selfdocking 54 is one of the important techniques in which bound ligand is docked at the catalytic site of protein molecule and the conformer of the original bound ligand is superimposed to the docked poses to calculate root mean square deviation (RMSD) values.
- The RMSD values was found 1.406Å, which indicated that the protocol was selected in the docking method was validated.
- Asp116 was also found to be important to form one of each hydrogen bond and bump interactions with NSC651812.
- In order to perform MD simulation and part of the analysis of the trajectories the AMBER 12 48 was used for the selected docked poses.
- The generalized amber force field was used for preparation of both ligand and receptor.
- Each system was minimized for 500 steps of each conjugate gradient and steepest descent method.
- In order to compare the drug-likeness of the screened compounds with existing Food and Drug Administration (FDA) approved HIV integrase inhibitors different parameters including dockscore, estimated activity, fit value, molecular weight, logP, violation of Lipinski’s rule of five, molecular volume, molecular refractivity, number of H-bonds and number of bump interactions were analysed.
- LogP measures the hydrophobicity of the molecules.
Results and Discussion
- The HypoGen module was used to develop the pharmacophore model based on training set (ntr = 30) compounds selected from the whole dataset.
- The Feature mapping protocol of DS was used to select the pharmacophoric features, ‘HBA’, ‘HBD’, ’H’ and ‘R’ as required for chemical features and were given as input to the 3D QSAR pharmacophore generation along with keeping minimum and maximum feature value ‘0’ and ‘5’ respectively.
- From Table 1 it is delineated that the high correlation coefficient, less rmsd, highest cost difference and minimum error values were observed for Hypo1 in comparison to other hypotheses.
- The predicted activity of the training set molecules explained that one active compound was overestimated as moderately active and two moderately active molecules were underestimated as active compounds.
- Therefore it can be postulated that to design or synthesize new chemical entities of HIV integrase inhibitors HBA and R factors with critical inter-feature distances (Fig. 2c) will be crucial factors.
Validation Correlation Total cost
- From Table 3, the average of correlation coefficient for all 19 trials was found to be 0.642.
- It was also observed that the total costs of randomized runs were much higher than the total cost of Hypo 1.
- The above discussion undoubtedly demonstrated that the selected pharmacophore model was not produced by chance.
- In order to find potential molecules that are HIV integrase inhibitors virtual screening is a powerful technique and also effective as an alternative to high-throughput screening methodologies.
- ‘Maximum Hits’ was set to 600 for each screening method.
- After deletion of redundant molecules, the remaining 1121 compounds were fitted to the pharmacophore model by the Ligand Pharmacophore Mapping protocol of DS with maximum omitted feature set to ‘0’.
- Furthermore the Lipinski’s rule of five 52 and Veber’s 53 rule were checked for 13 compounds.
- The remaining 5 compounds further were taken into consideration for molecular docking study in the active site of HIV integrase (PDB ID: 1QS4).
- In order to analyse stability of molecular docked complexes of HIVintegrase with H13, NSC91705 and NSC651812 molecular dynamics studies were performed.
- Both molecular docking and lowest energy complexes of MD simulation of screened compounds explain the importance Asp64 and Asp116 amino residues at the active site cavity.
- In order to compare the drug-likeness of screened compounds with FDA approved HIV-integrase inhibitors different parameters of H13, Dolutegravir, Elvitegravir, Raltegravir, NSC91705 and NSC651812 were calculated and reported in Table 4.
- Pharmacophore-based virtual screening studies were carried out to identify potential molecules for therapeutic application in HIV/AIDS.
- Hypotheses were validated using R 2 pred, sp, R 2 m(test), Δr 2 m(test), Fischer’s randomization and decoy set, and finally Hypo1 was selected as the best model.
- In the molecular docking study, a number of binding interactions were observed between final screened compounds and catalytic amino acid residues of HIV-integrase.
- RMSD, RMSF, potential energy and total energy were recorded of the most active compound and final screened molecules.
Did you find this useful? Give us your feedback
Cites methods from "Structural requirements for potenti..."
...Detailed protocol of the MD simulation using AMBER (Case et al., 2014) is described in our previous publication (Islam & Pillay, 2016)....
Cites methods from "Structural requirements for potenti..."
...Docking protocol validation The validation of the docking protocol is essential to analyse the prediction ability of the proposed method ....
Cites background from "Structural requirements for potenti..."
...…become a powerful tool to discover potential hits that can bind to a particular receptor site and block or trigger the activity of a target protein (Islam & Pillay, 2016; Kumar, Sinha, Sharma, Purohit, & Padwad, 2019; Purohit, Kumar, & Hallan, 2018; Rajendran, 2016; Rajendran, Gopalakrishnan, &…...
"Structural requirements for potenti..." refers background or methods in this paper
...The implication of the hypothesis was calculated as per equation (1)....
...In order to check the predictivity and applicability as well as robustness of the pharmacoinformatics model, the pharmacophore hypotheses developed were validated by five different methods, (1) internal validation, (2) cost function analysis, (3) Fischer’s randomization test, (4) test set prediction and (5) decoy set....
...[1 (1 ) / ] Significance a b = − + (1) Where, a denotes the number of hypotheses with a total cost less than the best hypothesis, whereas b implies a collection of HypoGen runs and random runs....
Related Papers (5)
Frequently Asked Questions (10)
Q1. What are the contributions mentioned in the paper "Structural requirements for potential hiv-integrase inhibitors identified using pharmacophore-based virtual screening and molecular dynamics studies†" ?
Pillay et al. this paper used a pharmacophore-based virtual screening and molecular dynamics studies to identify potential HIV-integrase inhibitors.
Q2. What methods were used to validate the pharmacophore hypotheses?
In order to check the predictivity and applicability as well as robustness of the pharmacoinformatics model, the pharmacophore hypotheses developed were validated by five different methods, (1) internal validation, (2) cost function analysis, (3) Fischer’s randomization test, (4) test set prediction and (5) decoy set.
Q3. What is the significance of the two HB acceptors in the pharmacophore?
Importance of two HB acceptor and one ring aromatic sites in the pharmacophore can be correlated with binding mode of the most active and screened compounds.
Q4. What are the main statistical parameters used to determine the predictive ability of the training set molecules?
Two important statistical parameters, the LOO cross-validated correlation coefficient (Q 2 ) and error of estimation (se) were calculated based upon predicted activity of training compounds.
Q5. How many compounds were fitted to the pharmacophore model?
After deletion of redundant molecules, the remaining 1121 compounds were fitted to the pharmacophore model by the Ligand Pharmacophore Mapping protocol of DS with maximum omitted feature set to ‘0’.
Q6. What is the purpose of virtual screening?
In order to find potential molecules that are HIV integrase inhibitors virtual screening is a powerful technique and also effective as an alternative to high-throughput screening methodologies.
Q7. What are the parameters used to determine the EF of the decoys?
Based on five parameters decoys were selected and these included molecular weight, number of rotational bonds, hydrogen-bond donor count, hydrogen-bond acceptor count, and the octanol–water partition coefficient of the active inhibitors.
Q8. What are the guidelines for the selection of the training set?
The guidelines reported as a) molecules should be selected to provide clear and brief information with structure features and range of activity, b) at least 16 diverse molecules for training set should be considered to ensure the statistical significance and avoid chance correlation, c) the training set must include the most and the least active molecules and d) the biological activity data of the molecules should have spanned at least 4 orders of magnitude.
Q9. What is the protein structure for molecular docking study?
It is reported that a protein structure may be suitable for molecular docking study with the low resolution (<2.5Å) and R-factor (<0.28) 45 .
Q10. What were the parameters used to validate the pharmacophore model?
Different statistical parameters including the accuracy and enrichment factor (EF) were calculated to validate the pharmacophore model.