Prediction of potential toxicity and side effect protein targets of a small molecule by a ligand-protein inverse docking approach.

Determination of potential drug toxicity and side effect in early stages of drug development is important in reducing the cost and time of drug discovery. In this work, we explore a computer method for predicting potential toxicity and side effect protein targets of a small molecule. A ligand-protein inverse docking approach is used for computer-automated search of a protein cavity database to identify protein targets. This database is developed from protein 3D structures in the protein data bank (PDB). Docking is conducted by a procedure involving multiple conformer shape-matching alignment of a molecule to a cavity followed by molecular-mechanics torsion optimization and energy minimization on both the molecule and the protein residues at the binding region. Potential protein targets are selected by evaluation of molecular mechanics energy and, while applicable, further analysis of its binding competitiveness against other ligands that bind to the same receptor site in at least one PDB entry. Our results on several drugs show that 83% of the experimentally known toxicity and side effect targets for these drugs are predicted. The computer search successfully predicted 38 and missed five experimentally confirmed or implicated protein targets with available structure and in which binding involves no covalent bond. There are additional 30 predicted targets yet to be validated experimentally. Application of this computer approach can potentially facilitate the prediction of toxicity and side effect of a drug or drug lead.     

Protocol for fast screening of multi-target drug candidates: Application to Alzheimer’s disease

The treatment of many diseases may require drugs that are capable to attack multiple targets simultaneously. Obviously, the virtual screening of multi-target drug candidates is much more time consuming compared to the single-target case. This, in particular, concerns the last step of virtual screening where the binding free energy is computed by conventional molecular dynamics simulation. To overcome this difficulty we propose a simple protocol which is relied on the fast steered molecular dynamics simulation and on available experimental data on binding affinity of reference ligand to a given target. Namely, first we compute non-equilibrium works generated during pulling ligands from the binding site using the steered molecular dynamics method. Then as top leads we choose only those compounds that have the non-equilibrium work larger than that of a reference compound for which the binding free energy has been already known from experiment.Despite many efforts no cures for AD (Alzheimer’s disease) have been found. One of possible reasons for this failure is that drug candidates were developed for a single target, while there are exist many possible pathways to AD. Applying our new protocol to five targets including amyloid beta fibril, peroxisome proliferator-activated receptor γ, retinoic X receptor α, β- and γ-secretases, we have found two potential drugs (CID 16040294 and CID 9998128) for AD from the large PubChem database. We have also shown that these two ligands can interfere with the activity of popular Acetylcholinesterase target through strong binding towards it.     

Homology models of the mutated EGFR and their response towards quinazoline analogues

One of the most intensely studied tyrosine kinases is the epidermal growth factor receptor (EGFR). The tyrosine kinase receptors are known to be over expressed in some solid tumors and non-small cell lung cancers, causing differential susceptibility to the quinazoline inhibitors. In this study we have taken SYK tyrosine kinase coordinates from PDB database to model two new EGFR receptors with these mutations G695S and L834R and conducted all the docking studies of the inhibitors, also evaluated these two models for quality of structure using PROCHECK. Seven quinazoline analogues (gefitinib, erlotinib, CI-1033, and EKB-569 and other analogues) were selected for comparisons among the two new models. This study determined the receptor/inhibitor interactions, at that active domain binding sites consisting of 15 amino acids. We were able to calculate the energy data for each of the seven inhibitors. This data has been important in interpreting the affinity between the inhibitors evaluated against the three models of EGFR (wild-type and two mutated types). "Affinity"-based studies have indicated the order of response based on docking energy levels (Van der Waals and electrostatic interactions). The active ATP binding sites consisting of 15 amino acid residues were identified and the total energy (E(total)) which showed the affinity between the inhibitor molecules and the receptor (Van der Waals and electrostatic interactions). The selection of the quinazoline analogues was purely on their emergence as possible candidates in the drug discovery areas. This study describes the successful application of these models that we constructed for molecular docking studies to rationally design compounds predicted to bind favorably to the modeled EGFR catalytic sites.     

In silico investigation into the interactions between murine 5-HT3 receptor and the principle active compounds of ginger (Zingiber officinale)

Gingerols and shogaols are the primary non-volatile actives within ginger (Zingiber officinale). These compounds have demonstrated in vitro to exert 5-HT₃ receptor antagonism which could benefit chemotherapy-induced nausea and vomiting (CINV). The site and mechanism of action by which these compounds interact with the 5-HT₃ receptor is not fully understood although research indicates they may bind to a currently unidentified allosteric binding site. Using in silico techniques, such as molecular docking and GRID analysis, we have characterized the recently available murine 5-HT₃ receptor by identifying sites of strong interaction with particular functional groups at both the orthogonal (serotonin) site and a proposed allosteric binding site situated at the interface between the transmembrane region and the extracellular domain. These were assessed concurrently with the top-scoring poses of the docked ligands and included key active gingerols, shogaols and dehydroshogaols as well as competitive antagonists (e.g. setron class of pharmacologically active drugs), serotonin and its structural analogues, curcumin and capsaicin, non-competitive antagonists and decoys. Unexpectedly, we found that the ginger compounds and their structural analogs generally outscored other ligands at both sites. Our results correlated well with previous site-directed mutagenesis studies in identifying key binding site residues. We have identified new residues important for binding the ginger compounds. Overall, the results suggest that the ginger compounds and their structural analogues possess a high binding affinity to both sites. Notwithstanding the limitations of such theoretical analyses, these results suggest that the ginger compounds could act both competitively or non-competitively as has been shown for palonosetron and other modulators of CYS loop receptors.     

Scoring binding affinity of multiple ligands using implicit solvent and a single molecular dynamics trajectory: application to influenza neuraminidase

We explore a perturbative approach to calculation of binding free energy of multiple ligands, based on a single molecular dynamics simulation of a reference ligand-receptor complex and analysis via a hybrid force field/continuum model potential. The methodology is applied to prediction of relative binding free energies of 10 Influenza neuraminidase inhibitors, using Poisson-Boltzmann and generalised Born models of implicit solvent. These single-step MM-PB/SA and MM-GB/SA approaches predict the experimentally most potent ligand as first- or second-ranked according to total binding free energy. Ranking of inhibitors displays only moderate sensitivity to the choice of reference trajectory and ligand partial charge scheme. When ranked according to total electrostatic binding free energy, correlation with experiment improves (r(2) of 0.72); this may be related to underestimated first solvation shell effects by the implicit water models. Therefore, to increase the generality of this single-step approach as part of a potential computational compound optimisation strategy, further development of the treatment of short-range solvent interactions is warranted.     

BINANA: a novel algorithm for ligand-binding characterization

Computational chemists and structural biologists are often interested in characterizing ligand-receptor complexes for hydrogen-bond, hydrophobic, salt-bridge, van der Waals, and other interactions in order to assess ligand binding. When done by hand, this characterization can become tedious, especially when many complexes need be analyzed. In order to facilitate the characterization of ligand binding, we here present a novel Python-implemented computer algorithm called BINANA (BINding ANAlyzer), which is freely available for download at http://www.nbcr.net/binana/. To demonstrate the utility of the new algorithm, we use BINANA to confirm that the number of hydrophobic contacts between a ligand and its protein receptor is positively correlated with ligand potency. Additionally, we show how BINANA can be used to search through a large ligand-receptor database to identify those complexes that are remarkable for selected binding features, and to identify lead candidates from a virtual screen with specific, desirable binding characteristics. We are hopeful that BINANA will be useful to computational chemists and structural biologists who wish to automatically characterize many ligand-receptor complexes for key binding characteristics.     

Linear interaction energy models for β-secretase (BACE) inhibitors: Role of van der Waals,electrostatic, and continuum-solvation terms

Computing the binding affinity of a protein–ligand complex is one of the most fundamental and difficult tasks in computer-aided drug design. Many approaches for computing binding affinities can be classified as linear interaction energy (LIE) models as they rely on some type of linear fit of computed interaction energies between ligand and protein. We have examined the computed interaction energies of a series of β-secretase (BACE) inhibitors in terms of van der Waals, coulombic, and continuum-solvation contributions to ligand binding. We have also systematically examined the effect of different protonation states of the protein and ligands. We find that the binding affinities are relatively insensitive to the protonation state of the protein when neutral ligands are considered. Inclusion of charged ligands leads to large deviations in the coulomb, solvation, and even van der Waals terms. The latter is due to increased repulsive van der Waals interactions in the complex due to the strong coulomb attraction found between oppositely charged functional groups in the protein and ligand. In general, we find that the best models are obtained when the protein is judiciously charged (e.g. Asp32⁻, Arg235⁺) and the potentially charged ligands are treated as neutral.     

Linear interaction energy models for beta-secretase (BACE) inhibitors: Role of van der Waals, electrostatic, and continuum-solvation terms

Computing the binding affinity of a protein–ligand complex is one of the most fundamental and difficult tasks in computer-aided drug design. Many approaches for computing binding affinities can be classified as linear interaction energy (LIE) models as they rely on some type of linear fit of computed interaction energies between ligand and protein. We have examined the computed interaction energies of a series of β-secretase (BACE) inhibitors in terms of van der Waals, coulombic, and continuum-solvation contributions to ligand binding. We have also systematically examined the effect of different protonation states of the protein and ligands. We find that the binding affinities are relatively insensitive to the protonation state of the protein when neutral ligands are considered. Inclusion of charged ligands leads to large deviations in the coulomb, solvation, and even van der Waals terms. The latter is due to increased repulsive van der Waals interactions in the complex due to the strong coulomb attraction found between oppositely charged functional groups in the protein and ligand. In general, we find that the best models are obtained when the protein is judiciously charged (e.g. Asp32⁻, Arg235⁺) and the potentially charged ligands are treated as neutral.     

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.     

Novel ligands that target the mitochondrial membrane protein mitoNEET

Ligands of the thiazolidinedione (TZD) class of compounds, pioglitazone (Actos?) and rosiglitazone (Avandia?) are currently approved for treatment of type 2 diabetes and are known to bind to the PPAR-γ nuclear receptor subtype. Recent evidence suggesting PPAR-γ independent action of the TZDs led to the discovery of a novel integral outer mitochondrial membrane protein, mitoNEET. In spite of the several reported X-ray crystal structures of the unbound form of mitoNEET, the location and nature of the mitoNEET ligand binding sites (LBS) remain unknown. In this study, a molecular blind docking (BD) method was used to discover potential mitoNEET LBS and novel ligands, utilizing the program AutoDock Vina (v 1.0.2). Validation of BD was performed on the PPAR-γ receptor (PDB ID: 1ZGY) with the test compound rosiglitazone, demonstrating that the binding conformation of rosiglitazone determined by AutoDock Vina matches well with that of the cocrystallized ligand (root mean square deviation of the heavy atoms 1.45?). The locations and a general ligand binding interaction model for the LBS were determined, leading to the discovery of novel mitoNEET ligands. An in vitro fluorescence binding assay utilizing purified recombinant mitoNEET protein was used to determine the binding affinity of a predicted mitoNEET ligand, and the data obtained is in good agreement with AutoDock Vina results. The discovery of potential mitoNEET ligand binding sites and novel ligands, opens up the possibility for detailed structural studies of mitoNEET-ligand complexes, as well as rational design of novel ligands specifically targeted for mitoNEET.     

Analysis of CYP2D6 substrate interactions by computational methods

Cytochrome P450 CYP2D6 is involved in the oxidation of well over 150 drugs and, in general, those which contain a basic nitrogen atom in the molecule. To clarify how the residues of CYP2D6 are utilized for orientating a wide range of its specific substrates and distinguishing them from a variety of other organic compounds, docking studies by AutoDock and molecular dynamics (MD) simulations were conducted. Specific ligands were docked to both the homology model and crystal structures optimally to estimate the site of reaction on the ligand molecule and the binding energy for the complex, which were generally in good agreement with the experimental data. MD simulation for the CYP2D6-propranolol complex was then carried out to reveal the amino acid residues interacting with the substrate at the active site. Phe-120, Glu-216, Asp-301, and Phe-483 are identified as the substrate-binding residues in agreement with previously reported site-directed mutagenesis data and the crystal structure reported recently (PDB code: 2F9Q). As well as these residues, our theoretical prediction suggests that Phe-219 and Glu-222 are also important residues for mediating oxidation of substrates, especially propranolol.     

Structural basis for ligand recognition at the benzodiazepine binding site of GABAA alpha 3 receptor, and pharmacophore-based virtual screening approach

Given the heterogeneity of GABA(A) receptor, the pharmacological significance of identifying subtype selective modulators is increasingly being recognized. Thus, drugs selective for GABA(A) alpha(3) receptors are expected to display fewer side effects than the drugs presently in clinical use. Hence we carried out 3D QSAR (three-dimensional quantitative structure-activity relationship) studies on a series of novel GABA(A) alpha(3) subtype selective modulators to gain more insight into subtype affinity. To identify the 3D functional attributes required for subtype selectivity, a chemical feature-based pharmacophore, primarily based on selective ligands representing diverse structural classes was generated. The obtained pseudo receptor model of the benzodiazepine binding site revealed a binding mode akin to "Message-Address" concept. Scaffold hopping was carried out across multi-conformational May Bridge database for the identification of novel chemotypes. Further a focused data reduction approach was employed to choose a subset of enriched compounds based on "Drug likeness" and "Similarity-based" methods. These results taken together could provide impetus for rational design and optimization of more selective and high affinity leads with a potential to have decreased adverse effects.     

Structure-based design of inhibitors of NS3 serine protease of hepatitis C virus

We have designed small focused combinatorial library of hexapeptide inhibitors of NS3 serine protease of the hepatitis C virus (HCV) by structure-based molecular design complemented by combinatorial optimisation of the individual residues. Rational residue substitutions were guided by the structure and properties of the binding pockets of the enzyme's active site. The inhibitors were derived from peptides known to inhibit the NS3 serine protease by using unusual amino acids and alpha-ketocysteine or difluoroaminobutyric acid, which are known to bind to the S1 pocket of the catalytic site. Inhibition constants (Ki) of the designed library of inhibitors were predicted from a QSAR model that correlated experimental Ki of known peptidic inhibitors of NS3 with the enthalpies of enzyme-inhibitor interaction computed via molecular mechanics and the solvent effect contribution to the binding affinity derived from the continuum model of solvation. The library of the optimised inhibitors contains promising drug candidates-water-soluble anionic hexapeptides with predicted Ki* in the picomolar range.     

Computational and experimental approaches assess the interactions between bovine beta-lactoglobulin and synthetic compounds of pharmacological interest

Extending a previous investigation, the ability of binding to the model calycin beta-lactoglobulin (BLG) was evaluated both in silico and in vitro for several fluorine-containing (semi-)synthetic molecules of pharmacological and pharmaceutical interest (antibiotics, vastatins, steroid drugs). Simulation procedures included molecular docking according to a Montecarlo-simulated annealing protocol and molecular dynamics; heteronuclear NMR and denaturant gradient gel electrophoresis were the selected experimental techniques. For the tested drugs, ranking of the binding affinity was consistently assessed by computation and by experiment. The affinity for BLG increased in the sequence: 5-fluorosalycilic acid相似文献   

Structure-based design of hERG-neutral antihypertensive oxazalone and imidazolone derivatives

Angiotensin II receptor type 1 (AT1) antagonists are the most recent drug class against hypertension. Recently first crystal structure of AT1 receptor is deposited to the protein data bank (PDB ID: 4YAY). In this work, several molecular screening methods such as molecular docking and de novo design studies were performed and it is found that oxazolone and imidazolone derivatives reveal similar/better interaction energy profiles compared to the FDA approved sartan molecules at the binding site of the AT1 receptor. A database consisting of 3500-fragments were used to enumerate de novo designed imidazolone and oxazolone derivatives and hereby more than 50000 novel small molecules were generated. These derivatives were then used in high throughput virtual screening simulations (Glide/HTVS) to find potent hit molecules. In addition, virtual screening of around 18 million small drug-like compounds from ZINC database were screened at the binding pocket of the AT1 receptor via Glide/HTVS method. Filtered structures were then used in more sophisticated molecular docking simulations protocols (i.e., Glide/SP; Glide/XP; Glide/IFD; Glide/QPLD, and GOLD). However, the K⁺ ion channel/drug interactions should also be considered in studies implemented in molecular level against their cardiovascular risks. Thus, selected compounds with high docking scores via all diverse docking algorithms are also screened at the pore domain regions of human ether-a-go-go-related gene (hERG1) K⁺ channel to remove the high affinity hERG1 blocking compounds. High docking scored compounds at the AT1 with low hERG1 affinity is considered for long molecular dynamics (MD) simulations. Post-processing analysis of MD simulations assisted for better understanding of molecular mechanism of studied compounds at the binding cavity of AT1 receptor. Results of this study can be useful for designing of novel and safe AT1 inhibitors.     

Application of the fragment molecular orbital method analysis to fragment-based drug discovery of BET (bromodomain and extra-terminal proteins) inhibitors

The molecular interactions of inhibitors of bromodomains (BRDs) were investigated. BRDs are protein interaction modules that recognizing ε-N-acetyl-lysine (εAc-Lys) motifs found in histone tails and are promising protein–protein interaction (PPI) targets. First, we analyzed a peptide ligand containing εAc-Lys to evaluate native PPIs. We then analyzed tetrahydroquinazoline-6-yl-benzensulfonamide derivatives found by fragment-based drug design (FBDD) and examined their interactions with the protein compared with the peptide ligand in terms of the inter-fragment interaction energy. In addition, we analyzed benzodiazepine derivatives that are high-affinity ligands for BRDs and examined differences in the CH/π interactions of the amino acid residues. We further surveyed changes in the charges of the amino acid residues among individual ligands, performed pair interaction energy decomposition analysis and estimated the water profile within the ligand binding site. Thus, useful insights for drug design were provided. Through these analyses and considerations, we show that the FMO method is a useful drug design tool to evaluate the process of FBDD and to explore PPI inhibitors.     

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.