Computational Design of Dengue Type-2 NS2B/NS3 Protease Inhibitor: 2D/3D QSAR of Quinoline and Its Molecular Docking

Abstract

Abstract:The reemergence of dengue outbreak demands an effective and efficient treatment especially for antiviral agent since neither vaccine nor drug is available to overcome this disease. This study designed the new model for Dengue Type-2 (DENV2) NS2B/NS3 protease inhibitor from quinoline scaffold by combining QSAR modeling with molecular docking. Genetic Function Approximation (GFA) method was used to construct the QSAR model showing good correlation coefficient (r 2 = 0.8996). The model was validated using Partial Least Square (PLS) analysis (r 2 = 0.861), Leave One Out -Cross Validation (LOO-CV with q 2 = 0.677) and external validation (r 2 = 0.854). The model was then used to design more quinoline derivatives by attaching more electron withdrawing group such as halogen and aldehyde. Ssearching for more diverse structure of protease inhibitors was carried out by screening out NCI database. Results showed that the QSAR model was able to predict the new protease inhibitor model with a nanomolar inhibition. The molecular docking was then performed to predict the binding mode of all protease inhibitor models and revealed that most of essential amino acid residues such as Ile36, His51, Ser135, Asp129, Tyr150, Gly151, Gly153 and Val154 significantly interact with the ligands. Based on the agreement between QSAR modeling and molecular docking results, the designed molecules were reported gaining insight activities as DENV2 NS2B/NS3 protease inhibitor.

Insight parameter drug design for human beta-tryptase inhibition integrated molecular docking, QSAR, molecular dynamics simulation, and pharmacophore modelling studies of alpha-keto-[1,2,4]-oxadiazoles

“…On the other hand, r 2 (adj) is the adjusted-square-correlation coefficient to determine the predictability of the model, while r 2 (pred) shows the complementary potential of the developed model [42]. The values of r 2 , r 2 (adj), and r 2 (pred) of the three selected models are all in the range of 0.81 to 0.91, which are higher than the minimum value of 0.8 for r 2 and 0.6 for r 2 (adj) and r 2 (pred) [41], [51]. Besides, q 2 reveals the predictability of the model [50].…”

“…RMS residual errors are the probability of obtaining the constant variance of values from a built model. A model with low RMS residual error has good repeatability, and the minimum value for RMS residue is between 0 to 1 [41], [51]. The RMS residual error of the three selected models is between 0.2564 to 0.2589.…”

“…A lower S.O.P. p-value indicates that the model is better, and the value should be within 0 to 5% [41]. The S.O.P.…”

“…Example of descriptors is AlogP, Extended Connectivity Fingerprint (ECFP_6), Molecular_Weight, Num_Aromatic Rings, Num_H_Acceptors, Num_H_Donors, Num_Rings, Num_Rotatable Bonds, Molecular_Fractional, Polar Surface Area and Topology Descriptors which included CHI, Kappa, etc. [41].…”

“…The full ligand set is sorted through a random index. The "Top N" ligands were then assigned to the training set based on the value of the Training Set Percentage parameter and inhibition constant of the compounds [40], [41]. The training set contained twenty-seven compounds, and the test set contained seven compounds.…”