Predicted Binding Poses Research Articles

Informatics investigations into anti-thyroid drug induced agranulocytosis associated with multiple HLA-B alleles.

IntroductionAdverse drug reactions have been linked with HLA alleles in different studies. These HLA proteins play an essential role in the adaptive immune response for the presentation of self and non-self peptides. Anti-thyroid drugs methimazole and propylthiouracil have been associated with drug induced agranulocytosis (severe lower white blood cell count) in patients with B*27:05, B*38:02 and DRB1*08:03 alleles in different populations: Taiwanese, Vietnamese, Han Chinese and Caucasian.MethodsIn this study, informatics methods were used to investigate if any sequence or structural similarities exist between the two associated HLA-B alleles, compared with a set of “control” alleles assumed not be associated, which could help explain the molecular basis of the adverse drug reaction. We demonstrated using MHC Motif Viewer and MHCcluster that the two alleles do not have a propensity to bind similar peptides, and thus at a gross level the structure of the antigen presentation region of the two alleles are not similar. We also performed multiple sequence alignment to identify polymorphisms shared by the risk but not by the control alleles and molecular docking to compare the predicted binding poses of the drug-allele combinations.ResultsTwo residues, Cys67 and Thr80, were identified from the multiple sequence alignments to be unique to these risk alleles alone. The molecular docking showed the poses of the risk alleles to favour the F-pocket of the peptide binding groove, close to the Thr80 residue, with the control alleles generally favouring a different pocket. The data are thus suggestive that Thr80 may be a critical residue in HLA-mediated anti-thyroid drug induced agranulocytosis, and thus can guide future research and risk assessment.

Isolation, Semisynthesis, and Molecular Modeling of Deoxypodophyllotoxin Analogs for an Anti‐oral Cancer Agent

The predicted binding pose of DPT in soluble tubulin and 3-dimensional structures of the ligands such as DPT, PT, Model 1, and Model 2.

Destabilization and Resisting Growth of Alpha-Synuclein Fibril by Synuclean-D

Parkinson's disease (PD) is a major health condition, but current medications provide only symptomatic relief. The disease is associated with oligomerization of α-synuclein peptides into fibrils in neurons. An effective approach to prevent PD is to use molecular modalities that prevent the growth of nascent fibrils and/or destabilize pre-formed fibrils. Here, we explore the interactions of α-synuclein fibril with SynuClean-D (SCD), a newly discovered drug against PD, by docking, atomistic molecular dynamics (MD) simulations, and enhanced sampling techniques. Our aim is to investigate the mechanism by which SCD might destabilize and/or block the growth of α-synuclein fibrils. Initially, we performed SCD docking to the fibril and found that in the best predicted binding pose, SCD is completely inserted into the fibril core. Furthermore, to investigate the effect of SCD on the fibril, multiple replicas were simulated for 400 ns each in the presence and absence of SCD. The simulations showed that SCD significantly reduces the fibril-backbone H-bonding, thus affecting fibril stability. Next, in order to examine if SCD can enter the core spontaneously, we simulated 10 flooding simulations with 20 randomly distributed copies of SCD surrounding the fibril. In 20% cases, SCD arrived into the core through the end of the fibril and bound to the same domain found by docking. Steered MD simulations and umbrella sampling free energy calculations identified defects in the fibril, caused by loose inter-peptide interactions, through which SCD can enter the interior of the fibril. Moreover, detailed occupancy and clustering analyses of the flooding simulations revealed that SCD made strong interactions with the fibril end, demonstrating its potential to also inhibit fibril growth. These findings are novel in the context of drug-amyloid fibril interactions and offer a platform to design new drug molecules targeting α-synuclein fibrils.

Kinetics and computational analysis of cholinesterase inhibition by REVERC3, a bisdemethoxycurcumin-rich Curcuma longa extract: Relevance to the treatment of Alzheimer's disease.

Objectives: Cholinesterase inhibition is a common strategy to treat Alzheimer’s disease. In this study, we have investigated the cholinesterase inhibitory effects of a first-of-its-kind turmeric extract (REVERC3) having enriched content of bisdemethoxycurcumin as major active curcuminoid. Methods: The inhibition studies were performed using Ellman’s colorimetric assay. The kinetics of acetylcholinesterase and butyrylcholinesterase was determined in the presence of REVERC3 using the Lineweaver–Burk double reciprocal plots. Furthermore, we used AutoDock tools to predict the binding of bisdemethoxycurcumin with the active sites of cholinesterases. Results: REVERC3 showed 4.8- and 5.39-fold higher inhibitory potential of acetylcholinesterase and butyrylcholinesterase with IC50 values of 29.08 and 33.59 µg/mL, respectively, compared to the regular turmeric extract. The mode of binding of REVERC3 was competitive in the case of acetylcholinesterase while it was uncompetitive for the inhibition of butyrylcholinesterase. Docking analysis revealed that bisdemethoxycurcumin, the major constituent of REVERC3, has different preferences of binding in the active sites of acetylcholinesterase and butyrylcholinesterase. However, the best binding pose predictions are in line with the experimental binding mode of the cholinesterases. Conclusion: These results indicate that bisdemethoxycurcumin-enriched turmeric extract could improve the cholinergic functions via dual inhibition of cholinesterases. However, the predominant role of bisdemethoxycurcumin in REVERC3 must be further validated using preclinical studies and clinical trials.

The application of the MM/GBSA method in the binding pose prediction of FGFR inhibitors.

The success of a structure-based drug is highly dependent on a known binding pose of the protein-ligand system. However, this is not always available. In this study, we set out to explore the applicability of the popular and easy-to-use MD-based MM/GBSA method to determine the binding poses of known FGFR inhibitors. It was found that MM/GBSA combined with 100 ns of MD simulation significantly improved the success rate of docking methods from 30-40% to 70%. This work demonstrates a way that the MM/GBSA method can be more accurate than it is in ligand ranking, filling a gap in structure-based drug discovery when the binding pose is unknown.

Meglumine acridone acetate, the ionic salt of CMA and N-methylglucamine, induces apoptosis in human PBMCs via the mitochondrial pathway

Meglumine acridone acetate (MA) is used in Russia for the treatment of influenza and other acute respiratory viral infections. It was assumed, until recently, that its antiviral effect was associated with its potential ability to induce type I interferon. Advanced studies, however, have shown the failure of 10-carboxymethyl-9-acridanone (CMA) to activate human STING. As such, MA’s antiviral properties are still undergoing clarification. To gain insight into MA’s mechanisms of action, we carried out RNA-sequencing analysis of global transcriptomes in MA-treated (MA+) human peripheral blood mononuclear cells (PBMCs). In response to treatment, approximately 1,223 genes were found to be differentially expressed, among which 464 and 759 were identified as either up- or down-regulated, respectively. To clarify the cellular and molecular processes taking place in MA+ cells, we performed a functional analysis of those genes. We have shown that evident MA subcellular localizations are: at the nuclear envelope; inside the nucleus; and diffusely in perinuclear cytoplasm. Postulating that MA may be a nuclear receptor agonist, we carried out docking simulations with PPARα and RORα ligand binding domains including prediction and molecular dynamics-based analysis of potential MA binding poses. Finally, we confirmed that MA treatment enhanced nuclear apoptosis in human PBMCs. The research presented here, in our view, indicates that: (i) MA activity is mediated by nuclear receptors; (ii) MA is a possible PPARα and/or RORα agonist; (iii) MA has an immunosuppressive effect; and (iv) MA induces apoptosis through the mitochondrial signaling pathway.

D3R Grand Challenge 4: ligand similarity and MM-GBSA-based pose prediction and affinity ranking for BACE-1 inhibitors.

The Drug Design Data Resource (D3R) Grand Challenges present an opportunity to assess, in the context of a blind predictive challenge, the accuracy and the limits of tools and methodologies designed to help guide pharmaceutical drug discovery projects. Here, we report the results of our participation in the D3R Grand Challenge 4 (GC4), which focused on predicting the binding poses and affinity ranking for compounds targeting the [Formula: see text]-amyloid precursor protein (BACE-1). Our ligand similarity-based protocol using HYBRID (OpenEye Scientific Software) successfully identified poses close to the native binding mode for most of the ligands with less than 2Å RMSD accuracy. Furthermore, we compared the performance of our HYBRID-based approach to that of AutoDock Vina and DOCK 6 and found that using a reference ligand to guide the docking process is a better strategy for pose prediction and helped HYBRID to perform better here. We also conducted end-point free energy estimates on molecules dynamics based ensembles of protein-ligand complexesusing molecular mechanics combined with generalized Born surface area method (MM-GBSA). We found that the binding affinity ranking based on MM-GBSA scores have poor correlation with the experimental values. Finally, the main lessons from our participation in D3R GC4 are: (i) the generation of the macrocyclic conformers is a key step for successful pose prediction, (ii) the protonation states of the BACE-1 binding site should be treated carefully, (iii) the MM-GBSA method could not discriminate well between different predicted binding poses, and (iv) the MM-GBSA method does not perform well at predicting protein-ligand binding affinities here.

Modification of the cyclopropyl moiety of abacavir provides insight into the structure activity relationship between HLA-B*57:01 binding and T-cell activation.

Abacavir is associated with hypersensitivity reactions in individuals positive for the HLA-B*57:01 allele. The drug binds within the peptide binding groove of HLA-B*57:01 altering peptides displayed on the cell surface. Presentation of these HLA-abacavir-peptide complexes to T-cells is hypothesized to trigger a CD8+ T-cell response underpinning the hypersensitivity. Thus, the aim of this study was to explore the relationship between the structure of abacavir with HLA-B*57:01 binding and the CD8+ T-cell activation. Seventeen abacavir analogues were synthesized and cytokine secretion from abacavir/abacavir analogue-responsive CD8+ T-cell clones was measured using IFN-γ ELIspot. In silico docking studies were undertaken to assess the predicted binding poses of the abacavir analogues within the HLA-B*57:01 peptide binding groove. In parallel, the effect of selected abacavir analogues on the repertoire of self-peptides presented by cellular HLA-B*57:01 was characterized using mass spectrometry. Abacavir and ten analogues stimulated CD8+ T-cell IFN-γ release. Molecular docking of analogues that retained antiviral activity demonstrated a relationship between predicted HLA-B*57:01 binding orientations and the ability to induce a T-cell response. Analogues that stimulated T-cells displayed a perturbation of the natural peptides displayed by HLA-B*57:01. The antigen-specific CD8+ T-cell response was dependent on the enantiomeric form of abacavir at both cyclopropyl and cyclopentyl regions. Alteration of the chemical constitution of abacavir generates analogues that retain a degree of pharmacological activity, but have variable ability to activate T-cells. Modelling and immunopeptidome analysis delineate how drug HLA-B*57:01 binding and peptide display by antigen presenting cells relate to the activation of CD8+ T-cells.

Structure-based identification of novel CK2 inhibitors with a linear 2-propenone scaffold as anti-cancer agents

Protein kinase CK2 has emerged as an attractive cancer therapeutic target. Previous studies have highlighted the challenge of optimizing CK2 ATP-competitive inhibitors that have low druggability due to their polycyclic ring scaffolds. Therefore the development of novel inhibitors with non-polycyclic scaffolds emerges as a promising strategy for drug discovery targeting CK2. In this current study, based on the similar predicted binding poses of the linear 2-propenone scaffold of isoliquiritigenin with that of the polycyclic inhibitor CX-4945, a series of 2-propenone derivatives containing an amine-substituted five-membered heterocycle and a benzoic acid were designed, synthesized and evaluated for their in vitro CK2 inhibition and anti-cancer activity. Compound 8b was found to be the most potent CK2 inhibitor (IC50 = 0.6 μM) with the anti-proliferative activity on HepG2 cancer cells (IC50 = 14 μM), compared to the activity of isoliquiritigenin (IC50 = 17 μM and 51 μM, respectively). Molecular docking was performed to understand the binding modes of the newly designed 2-propenone derivatives with CK2. Compound 8b formed the most favorable network of hydrogen bonds with both the hinge region and positive area. Our results indicate that CK2 derivatives with a linear 2-propenone scaffold are promising candidates for anti-cancer drug discovery.

Monte Carlo on the manifold and MD refinement for binding pose prediction of protein-ligand complexes: 2017 D3R Grand Challenge.

Manifold representations of rotational/translational motion and conformational space of a ligand were previously shown to be effective for local energy optimization. In this paper we report the development of the Monte-Carlo energy minimization approach (MCM), which uses the same manifold representation. The approach was integrated into the docking pipeline developed for the current round of D3R experiment, and according to D3R assessment produced high accuracy poses for Cathepsin S ligands. Additionally, we have shown that (MD) refinement further improves docking quality. The code of the Monte-Carlo minimization is freely available at https://bitbucket.org/abc-group/mcm-demo .

Calculate protein-ligand binding affinities with the extended linear interaction energy method: application on the Cathepsin S set in the D3R Grand Challenge 3.

We participatedin the Cathepsin S (CatS) sub-challenge of the Drug Design Data Resource (D3R) Grand Challenge 3 (GC3) in 2017 to blindly predict the binding poses of 24 CatS-bound ligands, the binding affinity ranking of 136 ligands, and the binding free energies of a subset of 33 ligands in Stage 1A and Stage 2. Our submitted predictions ranked relatively well compared to the submissions from other participants. Here we present our methodologies used in the challenge. For the binding pose prediction, we employed the Glide module in the Schrodinger Suite 2017 and AutoDock Vina. For the binding affinity/free energy prediction, we carried out molecular dynamics simulations of the complexes in explicit water solvent with counter ions, and then estimated the binding free energies with our newly developed model of extended linear interaction energy (ELIE), which is inspired by two other popular end-point approaches: the linear interaction energy(LIE) method, and the molecular mechanics with Poisson-Boltzmann surface area solvation method(MM/PBSA). Our studies suggest that ELIE is a good trade-off between efficiency and accuracy, and it is appropriate for filling the gap between the high-throughput docking and scoring methods and the rigorous but much more computationally demanding methods like free energy perturbation(FEP) or thermodynamics integration(TI) in computer-aided drug design (CADD)projects.

Binding Affinity via Docking: Fact and Fiction.

In 1982, Kuntz et al. published an article with the title “A Geometric Approach to Macromolecule-Ligand Interactions”, where they described a method “to explore geometrically feasible alignment of ligands and receptors of known structure”. Since then, small molecule docking has been employed as a fast way to estimate the binding pose of a given compound within a specific target protein and also to predict binding affinity. Remarkably, the first docking method suggested by Kuntz and colleagues aimed to predict binding poses but very little was specified about binding affinity. This raises the question as to whether docking is the right tool to estimate binding affinity. The short answer is no, and this has been concluded in several comprehensive analyses. However, in this opinion paper we discuss several critical aspects that need to be reconsidered before a reliable binding affinity prediction through docking is realistic. These are not the only issues that need to be considered, but they are perhaps the most critical ones. We also consider that in spite of the huge efforts to enhance scoring functions, the accuracy of binding affinity predictions is perhaps only as good as it was 10–20 years ago. There are several underlying reasons for this poor performance and these are analyzed. In particular, we focus on the role of the solvent (water), the poor description of H-bonding and the lack of the systems’ true dynamics. We hope to provide readers with potential insights and tools to overcome the challenging issues related to binding affinity prediction via docking.

Plasticity of the Binding Site of Renin: Optimized Selection of Protein Structures for Ensemble Docking.

Protein flexibility poses a major challenge to docking of potential ligands in that the binding site can adopt different shapes. Docking algorithms usually keep the protein rigid and only allow the ligand to be treated as flexible. However, a wrong assessment of the shape of the binding pocket can prevent a ligand from adapting a correct pose. Ensemble docking is a simple yet promising method to solve this problem: Ligands are docked into multiple structures, and the results are subsequently merged. Selection of protein structures is a significant factor for this approach. In this work we perform a comprehensive and comparative study evaluating the impact of structure selection on ensemble docking. We perform ensemble docking with several crystal structures and with structures derived from molecular dynamics simulations of renin, an attractive target for antihypertensive drugs. Here, 500 ns of MD simulations revealed binding site shapes not found in any available crystal structure. We evaluate the importance of structure selection for ensemble docking by comparing binding pose prediction, ability to rank actives above nonactives (screening utility), and scoring accuracy. As a result, for ensemble definition k-means clustering appears to be better suited than hierarchical clustering with average linkage. The best performing ensemble consists of four crystal structures and is able to reproduce the native ligand poses better than any individual crystal structure. Moreover this ensemble outperforms 88% of all individual crystal structures in terms of screening utility as well as scoring accuracy. Similarly, ensembles of MD-derived structures perform on average better than 75% of any individual crystal structure in terms of scoring accuracy at all inspected ensembles sizes.

Combined spectroscopic, molecular docking and quantum mechanics study of β-casein and p-coumaric acid interactions following thermal treatment

The molecular nature of interactions between β-casein and p-coumaric acid was studied following exposure of their solutions to ultra-high temperature (UHT at 145 °C). Interactions were characterised by employing multi-spectroscopic methods, molecular docking and quantum mechanics calculations. FTIR demonstrates that the ligand lies in the vicinity of the protein, hence inverting the absorbance spectrum of the complex. This outcome changes the conformational characteristics of the protein leading to a flexible and open structure that accommodates the phenolic microconstituent. Results are supported by UV–vis, CD and fluorescence quenching showing considerable shifts in spectra with complexation. Molecular docking indicates that there is at least a hydrogen bond between p-coumaric acid and the peptide backbone of isoleucine (Ile27). Quantum mechanics calculations further argue that changes in experimental observations are also due to a covalent interaction in the protein-phenolic adduct, which according to the best predicted binding pose involves the side chain of lysine 47.

Protein-Ligand Docking in Drug Design: Performance Assessment and Binding-Pose Selection.

Main goal in drug discovery is the identification of drug-like compounds capable to modulate specific biological targets. Thus, theprediction of reliable binding poses of candidate ligands, through molecular docking simulations, represents a key step to be pursued in structure-based drug design (SBDD). Since the increasing number of resolved three-dimensional ligand-protein structures, together with the expansion of computational power and software development, the comprehensive and systematic use of experimental data can be proficiently employed to validate the docking performance.This allows to select and refine the protocol to adopt when predicting the binding pose of trial compounds in a target. Given the availability of multiple docking software, a comparative docking assessment in an early research stage represents a must-use step to minimize fails in molecular modeling. This chapter describes how to perform a docking assessment, using freely available tools, in a semiautomated fashion.

Task-Specific Scoring Functions for Predicting Ligand Binding Poses and Affinity and for Screening Enrichment.

Molecular docking, scoring, and virtual screening play an increasingly important role in computer-aided drug discovery. Scoring functions (SFs) are typically employed to predict the binding conformation (docking task), binding affinity (scoring task), and binary activity level (screening task) of ligands against a critical protein target in a disease's pathway. In most molecular docking software packages available today, a generic binding affinity-based (BA-based) SF is invoked for all three tasks to solve three different, but related, prediction problems. The limited predictive accuracies of such SFs in these three tasks has been a major roadblock toward cost-effective drug discovery. Therefore, in this work, we develop BT-Score, an ensemble machine-learning (ML) SF of boosted decision trees and thousands of predictive descriptors to estimate BA. BT-Score reproduced BA of out-of-sample test complexes with correlation of 0.825. Even with this high accuracy in the scoring task, we demonstrate that the docking and screening performance of BT-Score and other BA-based SFs is far from ideal. This has motivated us to build two task-specific ML SFs for the docking and screening problems. We propose BT-Dock, a boosted-tree ensemble model trained on a large number of native and computer-generated ligand conformations and optimized to predict binding poses explicitly. This model has shown an average improvement of 25% over its BA-based counterparts in different ligand pose prediction scenarios. Similar improvement has also been obtained by our screening-based SF, BT-Screen, which directly models the ligand activity labeling task as a classification problem. BT-Screen is trained on thousands of active and inactive protein-ligand complexes to optimize it for finding real actives from databases of ligands not seen in its training set. In addition to the three task-specific SFs, we propose a novel multi-task deep neural network (MT-Net) that is trained on data from the three tasks to simultaneously predict binding poses, affinities, and activity levels. We show that the performance of MT-Net is superior to conventional SFs and on a par with or better than models based on single-task neural networks.

Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2.

Computer-aided drug design has become an integral part of drug discovery and development in the pharmaceutical and biotechnology industry, and is nowadays extensively used in the lead identification and lead optimization phases. The drug design data resource (D3R) organizes challenges against blinded experimental data to prospectively test computational methodologies as an opportunity for improved methods and algorithms to emerge. We participated in Grand Challenge 2 to predict the crystallographic poses of 36 Farnesoid X Receptor (FXR)-bound ligands and the relative binding affinities for two designated subsets of 18 and 15 FXR-bound ligands. Here, we present our methodology for pose and affinity predictions and its evaluation after the release of the experimental data. For predicting the crystallographic poses, we used docking and physics-based pose prediction methods guided by the binding poses of native ligands. For FXR ligands with known chemotypes in the PDB, we accurately predicted their binding modes, while for those with unknown chemotypes the predictions were more challenging. Our group ranked #1st (based on the median RMSD) out of 46 groups, which submitted complete entries for the binding pose prediction challenge. For the relative binding affinity prediction challenge, we performed free energy perturbation (FEP) calculations coupled with molecular dynamics (MD) simulations. FEP/MD calculations displayed a high success rate in identifying compounds with better or worse binding affinity than the reference (parent) compound. Our studies suggest that when ligands with chemical precedent are available in the literature, binding pose predictions using docking and physics-based methods are reliable; however, predictions are challenging for ligands with completely unknown chemotypes. We also show that FEP/MD calculations hold predictive value and can nowadays be used in a high throughput mode in a lead optimization project provided that crystal structures of sufficiently high quality are available.

Machine learning accelerates MD-based binding pose prediction between ligands and proteins.

MotivationFast and accurate prediction of protein–ligand binding structures is indispensable for structure-based drug design and accurate estimation of binding free energy of drug candidate molecules in drug discovery. Recently, accurate pose prediction methods based on short Molecular Dynamics (MD) simulations, such as MM-PBSA and MM-GBSA, among generated docking poses have been used. Since molecular structures obtained from MD simulation depend on the initial condition, taking the average over different initial conditions leads to better accuracy. Prediction accuracy of protein–ligand binding poses can be improved with multiple runs at different initial velocity.ResultsThis paper shows that a machine learning method, called Best Arm Identification, can optimally control the number of MD runs for each binding pose. It allows us to identify a correct binding pose with a minimum number of total runs. Our experiment using three proteins and eight inhibitors showed that the computational cost can be reduced substantially without sacrificing accuracy. This method can be applied for controlling all kinds of molecular simulations to obtain best results under restricted computational resources.Availability and implementationCode and data are available on GitHub at https://github.com/tsudalab/bpbi.Supplementary information Supplementary data are available at Bioinformatics online.

Binding pose and affinity prediction in the 2016 D3R Grand Challenge 2 using the Wilma-SIE method.

The Farnesoid X receptor (FXR) exhibits significant backbone movement in response to the binding of various ligands and can be a challenge for pose prediction algorithms. As part of the D3R Grand Challenge 2, we tested Wilma-SIE, a rigid-protein docking method, on a set of 36 FXR ligands for which the crystal structures had originally been blinded. These ligands covered several classes of compounds. To overcome the rigid protein limitations of the method, we used an ensemble of publicly available structures for FXR from the PDB. The use of the ensemble allowed Wilma-SIE to predict poses with average and median RMSDs of 2.3 and 1.4Å, respectively. It was quite clear, however, that had we used a single structure for the receptor the success rate would have been much lower. The most successful predictions were obtained on chemical classes for which one or more crystal structures of the receptor bound to a molecule of the same class was available. In the absence of a crystal structure for the class, observing a consensus binding mode for the ligands of the class using one or more receptor structures of other classes seemed to be indicative of a reasonable pose prediction. Affinity prediction proved to be more challenging with generally poor correlation with experimental IC50s (Kendall tau ~ 0.3). Even when the 36 crystal structures were used the accuracy of the predicted affinities was not appreciably improved. A possible cause of difficulty is the internal energy strain arising from conformational differences in the receptor across complexes, which may need to be properly estimated and incorporated into the SIE scoring function.

Soluble Epoxide Hydrolase Inhibitory Activity of Components Isolated from Apios americana Medik.

A new compound 1, 5-methoxy-2,5,7,4′-tetrahydroxy-coumaronochromone, along with seven known compounds (2–8), were isolated from Apios americana using open column chromatography. Their structures were established based on an analysis of 1D and 2D NMR, and MS spectra. Among these, two compounds 1 and 2 showed inhibitory activity on soluble epoxide hydrolase (sEH) at a concentration below 50 μM. The respective competitive (1) and mixed (2) inhibitors were revealed to have Ki values of 21.0 ± 0.8 and 14.5 ± 1.5 μM, based on the Dixon plot. The potential inhibitor (2) was visually presented in a predicted binding pose in the receptor by molecular docking. Additionally, molecular dynamics were performed for a detailed understanding of their complex by Gromacs 4.6.5 package.