Accuracy of binding mode prediction with a cascadic stochastic tunneling method

We investigate the accuracy of the binding modes predicted for 83 complexes of the high-resolution subset of the ASTEX/CCDC receptor-ligand database using the atomistic FlexScreen approach with a simple forcefield-based scoring function. The median RMS deviation between experimental and predicted binding mode was just 0.83 A. Over 80% of the ligands dock within 2 A of the experimental binding mode, for 60 complexes the docking protocol locates the correct binding mode in all of ten independent simulations. Most docking failures arise because (a) the experimental structure clashed in our forcefield and is thus unattainable in the docking process or (b) because the ligand is stabilized by crystal water.     

CoMSIA and docking study of rhenium based estrogen receptor ligand analogs

OPLS all atom force field parameters were developed in order to model a diverse set of novel rhenium based estrogen receptor ligands whose relative binding affinities (RBA) to the estrogen receptor alpha isoform (ERalpha) with respect to 17beta-estradiol were available. The binding properties of these novel rhenium based organometallic complexes were studied with a combination of Comparative Molecular Similarity Indices Analysis (CoMSIA) and docking. A total of 29 estrogen receptor ligands consisting of 11 rhenium complexes and 18 organic ligands were docked inside the ligand-binding domain (LBD) of ERalpha utilizing the program Gold. The top ranked pose was used to construct CoMSIA models from a training set of 22 of the estrogen receptor ligands which were selected at random. In addition scoring functions from the docking runs and the polar volume (PV) were also studied to investigate their ability to predict RBA ERalpha. A partial least-squares analysis consisting of the CoMSIA steric, electrostatic and hydrophobic indices together with the polar volume proved sufficiently predictive having a correlation coefficient, r(2), of 0.94 and a cross-validated correlation coefficient, q(2), utilizing the leave-one-out method of 0.68. Analysis of the scoring functions from Gold showed particularly poor correlation to RBA ERalpha which did not improve when the rhenium complexes were extracted to leave the organic ligands. The combined CoMSIA and polar volume model ranked correctly the ligands in order of increasing RBA ERalpha, illustrating the utility of this method as a prescreening tool in the development of novel rhenium based estrogen receptor ligands.     

A QM protein–ligand investigation of antipsychotic drugs with the dopamine D2 Receptor (D2R)

The dopamine D2 Receptor (D2R) is a member of the G-Protein-Coupled Receptor family and plays a critical role in neurotransmission activities in the human brain. Dysfunction in dopamine receptor signaling may lead to mental health illnesses such as schizophrenia and Parkinson’s disease. D2R is the target protein of the commonly used antipsychotic drugs such as risperidone, clozapine, aripiprazole, olanzapine, ziprasidone, and quetiapine. Due to their significant side effects and non-selective profiles, the discovery of novel drugs has become a challenge for researchers working in this field. Recently, our group has focused on the interactions of these drug molecules in the active site of the D2R using different in silico approaches. We here compare the performances of different approaches in estimating the drug binding affinities using quantum chemical approaches. Conformations of drug molecules (ligands) at the binding site of the D2R taken from the preliminary docking studies and molecular dynamics simulations were used to generate protein–ligand interaction models. In a first approach, the BSSE-corrected interaction energies of the ligands with the most critical amino acid Asp114 and with the other amino acids closest to ligands in the binding cavity were calculated separately by density functional theory method in implicit water environment at the M06-2X/6-31 g(d,p) level of the theory. In a second approach, ligand binding affinities were calculated by taking into consideration not only the interaction energies but also deformation and desolvation energies of ligands with surrounding amino acid residues, in a radius of 5 Å of the protein-bound ligand. The quantum mechanically obtained results were compared with the experimentally obtained binding affinity values. We concluded that although H-bond interactions of ligands with Asp114 are the most dominant interaction in the binding site, if van der Waals and steric interactions of ligands which have cumulative effect on the ligand binding are not included in the calculations, the interaction energies are overestimated.     

In silico binding free energy predictability with π-π interaction energy-augmented scoring function: benzimidazole Raf inhibitors as a case study

The ability to estimate binding affinities of ligands precisely is of paramount importance in designing drugs. Docking programs are used primarily to predict the binding mode of ligands to receptors. However, current scoring functions as used in docking programs are not reliable enough to predict binding affinities of ligands without any further calculations. In the present study, we investigate the usefulness of adding π-π interaction energies between ring groups of residues and ligands to the scoring function for docking. It is found that such addition helps ranking ligand activities more correctly. LMP2 calculation is used to measure π-π interaction energies between ring groups. The result of this simple addition shows possibility of π-π interaction generalization in scoring functions.     

Improvements,trends, and new ideas in molecular docking: 2012–2013 in review

Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher‐level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods. Copyright © 2015 John Wiley & Sons, Ltd.     

A model binding site for testing scoring functions in molecular docking

Prediction of interaction energies between ligands and their receptors remains a major challenge for structure-based inhibitor discovery. Much effort has been devoted to developing scoring schemes that can successfully rank the affinities of a diverse set of possible ligands to a binding site for which the structure is known. To test these scoring functions, well-characterized experimental systems can be very useful. Here, mutation-created binding sites in T4 lysozyme were used to investigate how the quality of atomic charges and solvation energies affects molecular docking. Atomic charges and solvation energies were calculated for 172,118 molecules in the Available Chemicals Directory using a semi-empirical quantum mechanical approach by the program AMSOL. The database was first screened against the apolar cavity site created by the mutation Leu99Ala (L99A). Compared to the electronegativity-based charges that are widely used, the new charges and desolvation energies improved ranking of known apolar ligands, and better distinguished them from more polar isosteres that are not observed to bind. To investigate whether the new charges had predictive value, the non-polar residue Met102, which forms part of the binding site, was changed to the polar residue glutamine. The structure of the resulting Leu99Ala and Met102Gln double mutant of T4 lysozyme (L99A/M102Q) was determined and the docking calculation was repeated for the new site. Seven representative polar molecules that preferentially docked to the polar versus the apolar binding site were tested experimentally. All seven bind to the polar cavity (L99A/M102Q) but do not detectably bind to the apolar cavity (L99A). Five ligand-bound structures of L99A/M102Q were determined by X-ray crystallography. Docking predictions corresponded to the crystallographic results to within 0.4A RMSD. Improved treatment of partial atomic charges and desolvation energies in database docking appears feasible and leads to better distinction of true ligands. Simple model binding sites, such as L99A and its more polar variants, may find broad use in the development and testing of docking algorithms.     

E-pharmacophore-based virtual screening to identify GSK-3β inhibitors

Glycogen synthase kinase-3β (GSK-3β) is a serine/threonine kinase which has attracted significant attention during recent years in drug design studies. The deregulation of GSK-3β increased the loss of hippocampal neurons by triggering apoptosis-mediating production of neurofibrillary tangles and alleviates memory deficits in Alzheimer’s disease (AD). Given its role in the formation of neurofibrillary tangles leading to AD, it has been a major therapeutic target for intervention in AD, hence was targeted in the present study. Twenty crystal structures were refined to generate pharmacophore models based on energy involvement in binding co-crystal ligands. Four common e-pharmacophore models were optimized from the 20 pharmacophore models. Shape-based screening of four e-pharmacophore models against nine established small molecule databases using Phase v3.9 had resulted in 1800 compounds having similar pharmacophore features. Rigid receptor docking (RRD) was performed for 1800 compounds and 20 co-crystal ligands with GSK-3β to generate dock complexes. Interactions of the best scoring lead obtained through RRD were further studied with quantum polarized ligand docking (QPLD), induced fit docking (IFD) and molecular mechanics/generalized Born surface area. Comparing the obtained leads to 20 co-crystal ligands resulted in 18 leads among them, lead1 had the lowest docking score, lower binding free energy and better binding orientation toward GSK-3β. The 50?ns MD simulations run confirmed the stable nature of GSK-3β-lead1 docking complex. The results from RRD, QPLD, IFD and MD simulations confirmed that lead1 might be used as a potent antagonist for GSK-3β.     

Latest developments in molecular docking: 2010–2011 in review

The aim of docking is to accurately predict the structure of a ligand within the constraints of a receptor binding site and to correctly estimate the strength of binding. We discuss, in detail, methodological developments that occurred in the docking field in 2010 and 2011, with a particular focus on the more difficult, and sometimes controversial, aspects of this promising computational discipline. The main developments in docking in this period, covered in this review, are receptor flexibility, solvation, fragment docking, postprocessing, docking into homology models, and docking comparisons. Several new, or at least newly invigorated, advances occurred in areas such as nonlinear scoring functions, using machine‐learning approaches. This review is strongly focused on docking advances in the context of drug design, specifically in virtual screening and fragment‐based drug design. Where appropriate, we refer readers to exemplar case studies. Copyright © 2013 John Wiley & Sons, Ltd.     

Ligands differentially modulate the protein interactions of the human estrogen receptors alpha and beta

The interactions of human estrogen receptor subtypes ERalpha and ERbeta with DNA and a 210 amino acid residue fragment of the coactivator protein SRC-1 bearing three nuclear receptor interaction motifs were investigated quantitatively using fluorescence anisotropy in the presence of agonist and antagonist ligands. ERalpha and ERbeta were found to bind in a similar manner to DNA, and both salt and temperature affected the affinity and/or stoichiometry of these interactions. The agonist ligands estradiol, estrone and estriol did not modify the binding of ERalpha to the fluorescein-labeled target estrogen response element. However, in the case of ERbeta, these ligands led to the formation of some higher-order protein-DNA complexes and a small decrease in affinity. The partial agonist 4-hydroxytamoxifen had little effect on either ER subtype, whereas the pure antagonist ICI 182,780 led to the cooperative formation of protein-DNA complexes of higher order than dimer, as further demonstrated by competition experiments and gel mobility-shift assays. In addition to DNA binding, the interaction of both ER subtypes with the Alexa488-labeled SRC-1 coactivator fragment was investigated by fluorescence anisotropy. The agonist ligands estrone, estradiol, estriol, genistein and ethynyl estradiol exhibited distinct capacities for inducing the recruitment of SRC-1 that were not correlated with their affinity for the receptor. Moreover, estrone and genistein exhibited subtype specificity in that they induced SRC-1 recruitment to ERbeta with much higher efficiency than in the case of ERalpha. The differential coactivator recruitment capacities of the ER agonists and their receptor subtype coactivator recruitment specificity may be linked to the molecular structure of the agonists with respect to their interactions with a specific histidine residue located at the back of the ligand-binding pocket. Altogether, these quantitative in vitro studies of ER interactions reveal the complex energetic and stoichiometric consequences of changes in the chemical structures of these proteins and their ligands.     

Docking studies suggest ligand-specific δ-opioid receptor conformations

An automated docking procedure was used to study binding of a series of δ-selective ligands to three models of the δ-opioid receptor. These models are thought to represent the three ligand-specific receptor conformations. Docking results are in agreement with point mutation studies and suggest that different ligands—agonists and antagonists—may bind to the same binding site under different receptor conformations. Docking to different receptor models (conformations) also suggests that by changing to a receptor-specific conformation, the receptor may open or close different binding sites to other ligands. Figure  Ligands 5 (green) and 6 (orange) in bindingpocket BP1 of the R1 δ-opioid receptor model     

Exploring the binding pocket for pyridopyrimidine ligands at the CCK1 receptor by molecular docking

Pyridopyrimidine-based analogues are among the most highly potent and selective antagonists of cholecystokinin receptor subtype-1 (CCK1R) described to date. To better understand the structural and chemical features responsible for the recognition mechanism, and to explore the binding pocket of these compounds, we performed automated molecular docking using GOLD2.2 software on some derivatives with structural diversity, and propose a putative binding conformation for each compound. The docking protocol was guided by the key role of the Asn333 residue, as revealed by site directed mutagenesis studies. The results suggest two putative binding modes located in the same pocket. Both are characterized by interaction with the main residues revealed by experiment, Asn333 and Arg336, and differ in the spatial position of the Boc-Trp moiety of these compounds. Hydrophobic contacts with residues Thr117, Phe107, Ile352 and Ile329 are also in agreement with experimental data. Despite the poor correlation obtained between the estimated binding energies and the experimental activity, the proposed models allow us to suggest a plausible explanation of the observed binding data in accordance with chemical characteristics of the compounds, and also to explain the observed diastereoselectivity of this family of antagonists towards CCK1R. The most reasonable selected binding conformations could be the starting point for future studies. Figure Superimposition of the two putative binding conformations revealed by molecular docking for pyridopyrimidine-based CCK1 antagonists     

The relaxed complex method: Accommodating receptor flexibility for drug design with an improved scoring scheme

An extension of the new computational methodology for drug design, the "relaxed complex" method (J.-H. Lin, A. L. Perryman, J. R. Schames, and J. A. McCammon, Journal of the American Chemical Society, 2002, vol. 24, pp. 5632-5633), which accommodates receptor flexibility, is described. This relaxed complex method recognizes that ligand may bind to conformations that occur only rarely in the dynamics of the receptor. We have shown that the ligand-enzyme binding modes are very sensitive to the enzyme conformations, and our approach is capable of finding the best ligand enzyme complexes. Rapid docking serves as an efficient initial filtering method to screen a myriad of docking modes to a limited set, and it is then followed by more accurate scoring with the MM/PBSA (Molecular Mechanics/Poisson Boltzmann Surface Area) approach to find the best ligand-receptor complexes. The MM/PBSA scorings consistently indicate that the calculated binding modes that are most similar to those observed in the x-ray crystallographic complexes are the ones with the lowest free energies.     

An evaluation of automated in silico ligand docking of amino acid ligands to Family C G-protein coupled receptors

Family C G-protein coupled receptors (GPCRs) consist of the metabotropic glutamate receptors (mGluRs), the calcium-sensing receptor (CaSR), the T1R taste receptors, the GABA(B) receptor, the V2R pheromone receptors, and several chemosensory receptors. A common feature of Family C receptors is the presence of an amino acid binding pocket. The objective of this study was to evaluate the ability of the automatic docking program FlexX to predict the favored amino acid ligand at several Family C GPCRs. The docking process was optimized using the crystal structure of mGluR1 and the 20 amino acids were docked into homology models of the CaSR, the 5.24 chemosensory receptor, and the GPRC6A amino acid receptor. Under optimized docking conditions, glutamate was docked in the binding pocket of mGluR1 with a root mean square deviation of 1.56 angstroms from the co-crystallized glutamate structure and was ranked as the best ligand with a significantly better FlexX score compared to all other amino acids. Ligand docking to a homology model of the 5.24 receptor gave generally correct predictions of the favored amino acids, while the results obtained with models of GPRC6A and the CaSR showed that some of the favored amino acids at these receptors were correctly predicted, while a few other top scoring amino acids appeared to be false positives. We conclude that with certain caveats, FlexX can be successfully used to predict preferred ligands at Family C GPCRs.     

Three-dimensional models of alpha(2A)-adrenergic receptor complexes provide a structural explanation for ligand binding.

We have compared bacteriorhodopsin-based (alpha(2A)-AR(BR)) and rhodopsin-based (alpha(2A)-AR(R)) models of the human alpha(2A)-adrenengic receptor (alpha(2A)-AR) using both docking simulations and experimental receptor alkylation studies with chloroethylclonidine and 2-aminoethyl methanethiosulfonate hydrobromide. The results indicate that the alpha(2A)-AR(R) model provides a better explanation for ligand binding than does our alpha(2A)-AR(BR) model. Thus, we have made an extensive analysis of ligand binding to alpha(2A)-AR(R) and engineered mutant receptors using clonidine, para-aminoclonidine, oxymetazoline, 5-bromo-N-(4, 5-dihydro-1H-imidazol-2-yl)-6-quinoxalinamine (UK14,304), and norepinephrine as ligands. The representative docked ligand conformation was chosen using extensive docking simulations coupled with the identification of favorable interaction sites for chemical groups in the receptor. These ligand-protein complex studies provide a rational explanation at the atomic level for the experimentally observed binding affinities of each of these ligands to the alpha(2A)-adrenergic receptor.     

CScore: a simple yet effective scoring function for protein-ligand binding affinity prediction using modified CMAC learning architecture

Protein-ligand docking is a computational method to identify the binding mode of a ligand and a target protein, and predict the corresponding binding affinity using a scoring function. This method has great value in drug design. After decades of development, scoring functions nowadays typically can identify the true binding mode, but the prediction of binding affinity still remains a major problem. Here we present CScore, a data-driven scoring function using a modified Cerebellar Model Articulation Controller (CMAC) learning architecture, for accurate binding affinity prediction. The performance of CScore in terms of correlation between predicted and experimental binding affinities is benchmarked under different validation approaches. CScore achieves a prediction with R = 0.7668 and RMSE = 1.4540 when tested on an independent dataset. To the best of our knowledge, this result outperforms other scoring functions tested on the same dataset. The performance of CScore varies on different clusters under the leave-cluster-out validation approach, but still achieves competitive result. Lastly, the target-specified CScore achieves an even better result with R = 0.8237 and RMSE = 1.0872, trained on a much smaller but more relevant dataset for each target. The large dataset of protein-ligand complexes structural information and advances of machine learning techniques enable the data-driven approach in binding affinity prediction. CScore is capable of accurate binding affinity prediction. It is also shown that CScore will perform better if sufficient and relevant data is presented. As there is growth of publicly available structural data, further improvement of this scoring scheme can be expected.     

Flexible docking using tabu search and an empirical estimate of binding affinity

This article describes the implementation of a new docking approach. The method uses a Tabu search methodology to dock flexibly ligand molecules into rigid receptor structures. It uses an empirical objective function with a small number of physically based terms derived from fitting experimental binding affinities for crystallographic complexes. This means that docking energies produced by the searching algorithm provide direct estimates of the binding affinities of the ligands. The method has been tested on 50 ligand-receptor complexes for which the experimental binding affinity and binding geometry are known. All water molecules are removed from the structures and ligand molecules are minimized in vacuo before docking. The lowest energy geometry produced by the docking protocol is within 1.5 Å root-mean square of the experimental binding mode for 86% of the complexes. The lowest energies produced by the docking are in fair agreement with the known free energies of binding for the ligands. Proteins 33:367–382, 1998. © 1998 Wiley-Liss, Inc.     

Computational screening of dipeptidyl peptidase IV inhibitors from micoroalgal metabolites by pharmacophore modeling and molecular docking

Dipeptidyl peptidase IV (DPP‐IV) catalyzes conversion of GLP‐1 (glucagon like peptide 1) to inert structure, which results in insufficient secretion of insulin and increase in postprandial blood glucose level. The present study attempts to identify novel inhibitors from bioactive metabolites present in microalgae against DPP‐IV through virtual screening, molecular docking, and pharmacophore modeling for the active target. Possible binding modes of all 60 ligands against DPP‐IV receptor were constructed using MTiOpenScreen virtual screening server. Pharmacophore model was built based on identified 38 DPP‐IV test ligands by using the web‐based PharmaGist program which encompasses hydrogen‐bond acceptors, hydrophobic groups, spatial features, and aromatic rings. The pharmacophore model having highest scores was selected to screen active DPP‐IV ligands. Highest scoring model was used as a query in ZincPharmer screening. All identified ligands were filtered, based on the Lipinski's rule‐of‐five and were subjected to docking studies. In the process of docking analyses, we considered different bonding modes of one ligand with multiple active cavities of DPP‐IV with the help of AutoDock 4.0. The docking analyses indicate that the bioactive constituents, namely, β‐stigmasterol, barbamide, docosahexaenoic acid, arachidonic acid, and harman showed the best binding energies on DPP‐IV receptor and hydrogen bonding with ASP545, GLY741, TYR754, TYR666, ARG125, TYR547, SER630, and LYS554 residues. This study concludes that docosahexaenoic acid, arachidonic acid, β‐stigmasterol, barbamide, harman, ZINC58564986, ZINC56907325, ZINC69432950, ZINC69431828, ZINC73533041, ZINC84287073, ZINC69849395, and ZINC10508406 act as possible DPP‐IV inhibitors.     

FLIPDock: docking flexible ligands into flexible receptors

Conformational changes of biological macromolecules when binding with ligands have long been observed and remain a challenge for automated docking methods. Here we present a novel protein-ligand docking software called FLIPDock (Flexible LIgand-Protein Docking) allowing the automated docking of flexible ligand molecules into active sites of flexible receptor molecules. In FLIPDock, conformational spaces of molecules are encoded using a data structure that we have developed recently called the Flexibility Tree (FT). While the FT can represent fully flexible ligands, it was initially designed as a hierarchical and multiresolution data structure for the selective encoding of conformational subspaces of large biological macromolecules. These conformational subspaces can be built to span a range of conformations important for the biological activity of a protein. A variety of motions can be combined, ranging from domains moving as rigid bodies or backbone atoms undergoing normal mode-based deformations, to side chains assuming rotameric conformations. In addition, these conformational subspaces are parameterized by a small number of variables which can be searched during the docking process, thus effectively modeling the conformational changes in a flexible receptor. FLIPDock searches the variables using genetic algorithm-based search techniques and evaluates putative docking complexes with a scoring function based on the AutoDock3.05 force-field. In this paper, we describe the concepts behind FLIPDock and the overall architecture of the program. We demonstrate FLIPDock's ability to solve docking problems in which the assumption of a rigid receptor previously prevented the successful docking of known ligands. In particular, we repeat an earlier cross docking experiment and demonstrate an increased success rate of 93.5%, compared to original 72% success rate achieved by AutoDock over the 400 cross-docking calculations. We also demonstrate FLIPDock's ability to handle conformational changes involving backbone motion by docking balanol to an adenosine-binding pocket of protein kinase A.     

Binding affinity prediction of novel estrogen receptor ligands using receptor-based 3-D QSAR methods

We have recently reported the development of a 3-D QSAR model for estrogen receptor ligands showing a significant correlation between calculated molecular interaction fields and experimentally measured binding affinity. The ligand alignment obtained from docking simulations was taken as basis for a comparative field analysis applying the GRID/GOLPE program. Using the interaction field derived with a water probe and applying the smart region definition (SRD) variable selection procedure, a significant and robust model was obtained (q²_LOO=0.921, SDEP=0.345). To further analyze the robustness and the predictivity of the established model several recently developed estrogen receptor ligands were selected as external test set. An excellent agreement between predicted and experimental binding data was obtained indicated by an external SDEP of 0.531. Two other traditionally used prediction techniques were applied in order to check the performance of the receptor-based 3-D QSAR procedure. The interaction energies calculated on the basis of receptor–ligand complexes were correlated with experimentally observed affinities. Also ligand-based 3-D QSAR models were generated using program FlexS. The interaction energy-based model, as well as the ligand-based 3-D QSAR models yielded models with lower predictivity. The comparison with the interaction energy-based model and with the ligand-based 3-D QSAR models, respectively, indicates that the combination of receptor-based and 3-D QSAR methods is able to improve the quality of prediction.