An effective HIV-1 integrase inhibitor screening platform: Rationality validation of drug screening,conformational mobility and molecular recognition analysis for PFV integrase complex with viral DNA

As an important target for the development of novel anti-AIDS drugs, HIV-1 integrase (IN) has been widely concerned. However, the lack of a complete accurate crystal structure of HIV-1 IN greatly blocks the discovery of novel inhibitors. In this work, an effective HIV-1 IN inhibitor screening platform, namely PFV IN, was filtered from all species of INs. Next, the 40.8% similarity with HIV-1 IN, as well as the high efficiency of virtual screening and the good agreement between calculated binding free energies and experimental ones all proved PFV IN is a promising screening platform for HIV-1 IN inhibitors. Then, the molecular recognition mechanism of PFV IN by its substrate viral DNA and six naphthyridine derivatives (NRDs) inhibitors was investigated through molecular docking, molecular dynamics simulations and water-mediated interactions analyses. The functional partition of NRDs IN inhibitors could be divided into hydrophobic and hydrophilic ones, and the Mg²⁺ ions, water molecules and conserved DDE motif residues all interacted with the hydrophilic partition, while the bases in viral DNA and residues like Tyr212, Pro214 interacted with the hydrophobic one. Finally, the free energy landscape (FEL) and cluster analyses were performed to explore the molecular motion of PFV IN-DNA system. It is found that the association with NRDs inhibitors would obviously decrease the motion amplitude of PFV IN-DNA, which may be one of the most potential mechanisms of IN inhibitors. This work will provide a theoretical basis for the inhibitor design based on the structure of HIV-1 IN.     

Design of peptidomimetic inhibitors of aspartic protease of HIV-1 containing -Phe Psi Pro- core and displaying favourable ADME-related properties

Aspartic protease (PR) of HIV-1 virus represents a valid therapeutic target for the design of antiviral agents suitable for treatment of AIDS. We have designed peptidomimetic PR inhibitors containing a novel dihydroxyethylenediamine -Phe-Psi[CHOH-CHOH]-Pro- core using molecular modelling approach that predicts the inhibitory potencies (IC(50)(pre)) in terms of computed relative enzyme-inhibitor complexation Gibbs free energies (Delta Delta G(comp)). The modelling approach considers not only the enzyme-inhibitor interactions, but includes also the solvent and entropic effects affecting the enzyme inhibition. The objectives of this study were to optimize the number and type of flanking residues that occupy the S(3), S(2) and S(2'), S(3') positions in the PR binding pocket and to select potent lead candidates, which display also favourable ADME-related properties. The structure-based design was combined with a synthetic strategy used to prepare a training set of 10 analogues sharing the -Phe Psi Pro- core. This strategy couples stereochemical control with full flexibility in the choice of the flanking residues and in vitro activity assays. A QSAR model correlating calculated Delta Delta G(comp) with the measured IC(50)(exp) values for the training set was prepared and confirmed that our computational approach can serve for reliable prediction of PR inhibitory potencies of peptidomimetics. The appropriate choice of the flanking residues allowed us to design virtual lead compounds, such as FP14, FP23 and FP76, with reduced molecular weight, predicted inhibitory potencies in the picomolar range, promising ADME profiles and a potential to escape drug resistance due to favourable interactions with the PR backbone.     

Generation and validation of the first predictive pharmacophore model for cyclin-dependent kinase 9 inhibitors

A three-dimensional (3D) pharmacophore modelling approach was applied to a diverse data set of known cyclin-dependent kinase 9 (CDK9) inhibitors. Diversity sampling and principal components analysis (PCA) were employed to ensure the rational selection of representative training sets. Twelve statistically robust pharmacophore models were generated using the HypoGen algorithm. The resulting models showed high homology and indicated great convergence in ascertaining pharmacophoric features essential for CDK9 inhibitory activity. One of the best models (Hypo 6) was assessed further by external predictive capability, randomization test, as well as its performance in virtual screening. The capability of the resulting models to reliably predict the inhibitory activity of external data sets and discriminate active structures from general databases would assist the identification and optimization of novel CDK9 inhibitors.     

In silico screening and study of novel ERK2 inhibitors using 3D QSAR,docking and molecular dynamics

ERK2 is a dual specificity protein kinase, part of the Ras/Raf/MEK/ERK signal transduction cascade. It forms an interesting target for inhibition based on its relationship with cell proliferation and oncogenesis. A 3D QSAR pharmacophore model (Hypo1) with high correlation (r = 0.938) was developed for ERK2 ATP site on the basis of experimentally known inhibitors. The model included three hydrogen bonds, and one hydrophobic site. Assessment of Hypo1 through Fisher randomization, cost analysis, leave one out method and decoy test suggested that the model can reliably detect ERK2 inhibitors. Hypo1 has been used for virtual screening of potential inhibitors from ZINC, Drug Bank, NCI, Maybridge and Chembank databases. Using Hypo1 as a query, databases have been interrogated for compounds who meet the pharmacophore features. The resulting hit compounds were subject to docking and analysis. Docking and molecular dynamics analysis showed that in order to achieve a higher potency compounds have to interact with catalytic site, glycine rich loop, Hinge region, Gatekeeper region and ATP site entrance residues. We also identified catalytic site and Glycine rich loop as important regions to bind by molecules for better potency and selectivity.     

Pharmacophore identification and virtual screening for methionyl-tRNA synthetase inhibitors

Aminoacyl-tRNA synthetases (aaRSs) are essential enzymes involved in protein biosynthesis in all living organisms and are an unexploited antibacterial targets, as many strains of bacteria have become resistant to all established classes of antibiotics. Therefore, the main aim of this study is to discover new lead molecules which would be useful as anti-bacterial compounds. Pharmacophore models were developed by using CATALYST HypoGen with a training set of 29 diverse methionyl-tRNA synthetase (MetRS) inhibitors. The best quantitative pharmacophore hypothesis (Hypo1) obtained a correlation coefficient of 0.975, root mean square deviation (RMSD) of 0.55 and cost difference (null cost-total cost) of 70.32. This Hypo1 was validated by two methods, first by using 104 test set molecules which resulted a correlation of 0.926 between HypoGen estimated activities versus experimental activities and secondly by Cat-Scramble validation method. This validated pharmacophore model was further used for screening databases for discovery of new MetRS inhibitors. The new lead compounds were further analyzed for drug-like properties. Homology modeled structure of Staphylococcus aureus MetRS was built and molecular docking studies were performed with many inhibitors using the newly built protein structure. Finally, it was found that the new leads exhibited good estimated inhibitory activity, calculated binding properties similar to experimentally proven compounds and also favorable drug-like properties.     

Drug design: new inhibitors for HIV-1 protease based on Nelfinavir as lead

Nelfinavir (Viracept) is a potent, non-peptidic inhibitor of HIV-1 Protease, which has been marketed for the treatment of HIV infected patients. However, HIV-1 develops drug-resistance which decreases the affinity of Nelfinavir for the binding pocket of Protease. We present here three new variants of Nelfinavir, which we have designed with computational tools, with greater affinity for HIV-1 Protease than Nelfinavir itself. Accordingly, we have introduced rational modifications in Nelfinavir, optimizing its affinity to the most conserved amino acids in Protease, in order to increase the efficiency of the three new inhibitors. Minimization and molecular dynamics simulations have been carried out on four complexes, HIV-1 Protease with Nelfinavir and subsequently with the new inhibitors, respectively, in order to analyze the behavior of the systems. Additionally, we have calculated the binding free energy differences Protease:inhibitor, which gave us a quantitative idea of the new molecules inhibitory efficiency in silico.     

Applying linear interaction energy method for binding affinity calculations of podophyllotoxin analogues with tubulin using continuum solvent model and prediction of cytotoxic activity

Podophyllotoxin and its analogues have important therapeutic value in the treatment of cancer, due to their ability to induce apoptosis in cancer cells in a proliferation-independent manner. These ligands bind to colchicine binding site of tubulin near the α- and β-tubulin interface and interfere with tubulin polymerization. The binding free energies of podophyllotoxin-based inhibitors of tubulin were computed using a linear interaction energy (LIE) method with a surface generalized Born (SGB) continuum solvation model. A training set of 76 podophyllotoxin analogues was used to build a binding affinity model for estimating the free energy of binding for 36 inhibitors (test set) with diverse structural modifications. The average root mean square error (RMSE) between the experimental and predicted binding free energy values was 0.56 kcal/mol which is comparable to the level of accuracy achieved by the most accurate methods, such as free energy perturbation (FEP) or thermodynamic integration (TI). The squared correlation coefficient between experimental and SGB–LIE estimates for the free energy for the test set compounds is also significant (R² = 0.733). On the basis of the analysis of the binding energy, we propose that the three-dimensional conformation of the A, B, C and D rings is important for interaction with tubulin. On the basis of this insight, 12 analogues of varying ring modification were taken, tested with LIE methodology and then validated with their experimental potencies of tubulin polymerization inhibition. Low levels of RMSE for the majority of inhibitors establish the structure-based LIE method as an efficient tool for generating more potent and specific inhibitors of tubulin by testing rationally designed lead compounds based on podophyllotoxin derivatization.     

Applying linear interaction energy method for binding affinity calculations of podophyllotoxin analogues with tubulin using continuum solvent model and prediction of cytotoxic activity

Podophyllotoxin and its analogues have important therapeutic value in the treatment of cancer, due to their ability to induce apoptosis in cancer cells in a proliferation-independent manner. These ligands bind to colchicine binding site of tubulin near the α- and β-tubulin interface and interfere with tubulin polymerization. The binding free energies of podophyllotoxin-based inhibitors of tubulin were computed using a linear interaction energy (LIE) method with a surface generalized Born (SGB) continuum solvation model. A training set of 76 podophyllotoxin analogues was used to build a binding affinity model for estimating the free energy of binding for 36 inhibitors (test set) with diverse structural modifications. The average root mean square error (RMSE) between the experimental and predicted binding free energy values was 0.56 kcal/mol which is comparable to the level of accuracy achieved by the most accurate methods, such as free energy perturbation (FEP) or thermodynamic integration (TI). The squared correlation coefficient between experimental and SGB–LIE estimates for the free energy for the test set compounds is also significant (R² = 0.733). On the basis of the analysis of the binding energy, we propose that the three-dimensional conformation of the A, B, C and D rings is important for interaction with tubulin. On the basis of this insight, 12 analogues of varying ring modification were taken, tested with LIE methodology and then validated with their experimental potencies of tubulin polymerization inhibition. Low levels of RMSE for the majority of inhibitors establish the structure-based LIE method as an efficient tool for generating more potent and specific inhibitors of tubulin by testing rationally designed lead compounds based on podophyllotoxin derivatization.     

A3 adenosine receptor: Homology modeling and 3D-QSAR studies

Adenosine receptors (AR) belong to the superfamily of G-protein-coupled receptors (GPCRs). They are divided into four subtypes (A1, A2A, A2B, and A3) and can be distinguished on the basis of their distinct molecular structures, distinct tissues distribution, and selectivity for adenosine analogs. The hA3R, the most recently identified adenosine receptor, is involved in a variety of intracellular signaling pathways and physiological functions. Expression of hA3R was reported to be elevated in cancerous tissues and A3 antagonists could be proposed for therapeutic treatments of tumor. By using the crystal structure of hA2A adenosine receptor, recently published, we were able to obtain a model for A3R, further optimized using nanosecond scale molecular dynamics simulation. One hundred twenty two active and selective compounds were docked into this model and used as training set to generate pharmacophore models. These last address the prevalent features to be used for the search of new inhibitors. Therefore, it was employed as template to screen the ZINC database in the attempt to find new potent and selective human A3R antagonists. Our theoretical model of hA3 adenosine receptor was used to evaluate and quantify the structure-activity relationship of known antagonists. Moreover the obtained 3D-QSAR model allowed to identify new potential inhibitors.     

Consensus scoring model for the molecular docking study of mTOR kinase inhibitor

The discovery of mammalian target of rapamycin (mTOR) kinase inhibitors has always been a research hotspot of antitumor drugs. Consensus scoring used in the docking study of mTOR kinase inhibitors usually improves hit rate of virtual screening. Herein, we attempt to build a series of consensus scoring models based on a set of the common scoring functions. In this paper, twenty-five kinds of mTOR inhibitors (16 clinical candidate compounds and 9 promising preclinical compounds) are carefully collected, and selected for the molecular docking study used by the Glide docking programs within the standard precise (SP) mode. The predicted poses of these ligands are saved, and revaluated by twenty-six available scoring functions, respectively. Subsequently, consensus scoring models are trained based on the obtained rescoring results by the partial least squares (PLS) method, and validated by Leave-one-out (LOO) method. In addition, three kinds of ligand efficiency indices (BEI, SEI, and LLE) instead of pIC₅₀ as the activity could greatly improve the statistical quality of build models. Two best calculated models 10 and 22 using the same BEI indice have following statistical parameters, respectively: for model 10, training set R² = 0.767, Q² = 0.647, RMSE = 0.024, and for test set R² = 0.932, RMSE = 0.026; for model 22, raining set R² = 0.790, Q² = 0.627, RMSE = 0.023, and for test set R² = 0.955, RMSE = 0.020. These two consensus scoring model would be used for the docking virtual screening of novel mTOR inhibitors.     

Determination of the protonation state for the catalytic dyad in β-secretase when bound to hydroxyethylamine transition state analogue inhibitors: A molecular dynamics simulation study

BACE1 is an aspartyl protease of pharmacological interest for its direct participation in Alzheimer’s disease (AD) through β-amyloid peptide production. Two aspartic acid residues are present in the BACE1 catalytic region which can adopt multiple protonation states depending on the chemical nature of its inhibitors, i.e., monoprotonated, diprotonated and di-deprotonated states. In the present study a series of protein-ligand molecular dynamics (MD) simulations was carried out to identify the most feasible protonation state adopted by the catalytic dyad in the presence of hydroxyethylamine transition state analogue inhibitors. The MD trajectories revealed that the di-deprotonated state is most prefered in the presence of hydroxyethilamine (HEA) family inhibitors. This appears as a result after evaluating, for all 9 protonation state configurations during the simulation time, the deviations of a set of distances and dihedral angles measured on the ligand, protein and protein-ligand complex with reference to an X-ray experimental BACE1/HEA crystallographic structure. These results will help to clarify the phenomena related to the HEAs inhibitory pathway, and improve HEAs databases’ virtual screening and ligand design processes targeting β-secretase protein.