Showing papers on "Docking (molecular) published in 2016"

Computational protein–ligand docking and virtual drug screening with the AutoDock suite

Abstract: Computational docking can be used to predict bound conformations and free energies of binding for small-molecule ligands to macromolecular targets. Docking is widely used for the study of biomolecular interactions and mechanisms, and it is applied to structure-based drug design. The methods are fast enough to allow virtual screening of ligand libraries containing tens of thousands of compounds. This protocol covers the docking and virtual screening methods provided by the AutoDock suite of programs, including a basic docking of a drug molecule with an anticancer target, a virtual screen of this target with a small ligand library, docking with selective receptor flexibility, active site prediction and docking with explicit hydration. The entire protocol will require ∼5 h.

Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods

Abstract: Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in living organisms. Proteins, an important class of biological macromolecules, realize their functions through binding to themselves or other molecules. A detailed understanding of the protein–ligand interactions is therefore central to understanding biology at the molecular level. Moreover, knowledge of the mechanisms responsible for the protein-ligand recognition and binding will also facilitate the discovery, design, and development of drugs. In the present review, first, the physicochemical mechanisms underlying protein–ligand binding, including the binding kinetics, thermodynamic concepts and relationships, and binding driving forces, are introduced and rationalized. Next, three currently existing protein-ligand binding models—the “lock-and-key”, “induced fit”, and “conformational selection”—are described and their underlying thermodynamic mechanisms are discussed. Finally, the methods available for investigating protein–ligand binding affinity, including experimental and theoretical/computational approaches, are introduced, and their advantages, disadvantages, and challenges are discussed.

Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power.

Abstract: As one of the most popular computational approaches in modern structure-based drug design, molecular docking can be used not only to identify the correct conformation of a ligand within the target binding pocket but also to estimate the strength of the interaction between a target and a ligand. Nowadays, as a variety of docking programs are available for the scientific community, a comprehensive understanding of the advantages and limitations of each docking program is fundamentally important to conduct more reasonable docking studies and docking-based virtual screening. In the present study, based on an extensive dataset of 2002 protein–ligand complexes from the PDBbind database (version 2014), the performance of ten docking programs, including five commercial programs (LigandFit, Glide, GOLD, MOE Dock, and Surflex-Dock) and five academic programs (AutoDock, AutoDock Vina, LeDock, rDock, and UCSF DOCK), was systematically evaluated by examining the accuracies of binding pose prediction (sampling power) and binding affinity estimation (scoring power). Our results showed that GOLD and LeDock had the best sampling power (GOLD: 59.8% accuracy for the top scored poses; LeDock: 80.8% accuracy for the best poses) and AutoDock Vina had the best scoring power (rp/rs of 0.564/0.580 and 0.569/0.584 for the top scored poses and best poses), suggesting that the commercial programs did not show the expected better performance than the academic ones. Overall, the ligand binding poses could be identified in most cases by the evaluated docking programs but the ranks of the binding affinities for the entire dataset could not be well predicted by most docking programs. However, for some types of protein families, relatively high linear correlations between docking scores and experimental binding affinities could be achieved. To our knowledge, this study has been the most extensive evaluation of popular molecular docking programs in the last five years. It is expected that our work can offer useful information for the successful application of these docking tools to different requirements and targets.

Docking Screens for Novel Ligands Conferring New Biology.

Abstract: It is now plausible to dock libraries of 10 million molecules against targets over several days or weeks. When the molecules screened are commercially available, they may be rapidly tested to find new leads. Although docking retains important liabilities (it cannot calculate affinities accurately nor even reliably rank order high-scoring molecules), it can often can distinguish likely from unlikely ligands, often with hit rates above 10%. Here we summarize the improvements in libraries, target quality, and methods that have supported these advances, and the open access resources that make docking accessible. Recent docking screens for new ligands are sketched, as are the binding, crystallographic, and in vivo assays that support them. Like any technique, controls are crucial, and key experimental ones are reviewed. With such controls, docking campaigns can find ligands with new chemotypes, often revealing the new biology that may be docking’s greatest impact over the next few years.

systemsDock: a web server for network pharmacology-based prediction and analysis

Abstract: We present systemsDock, a web server for network pharmacology-based prediction and analysis, which permits docking simulation and molecular pathway map for comprehensive characterization of ligand selectivity and interpretation of ligand action on a complex molecular network. It incorporates an elaborately designed scoring function for molecular docking to assess protein-ligand binding potential. For large-scale screening and ease of investigation, systemsDock has a user-friendly GUI interface for molecule preparation, parameter specification and result inspection. Ligand binding potentials against individual proteins can be directly displayed on an uploaded molecular interaction map, allowing users to systemically investigate network-dependent effects of a drug or drug candidate. A case study is given to demonstrate how systemsDock can be used to discover a test compound's multi-target activity. systemsDock is freely accessible at http://systemsdock.unit.oist.jp/.

Is It Reliable to Use Common Molecular Docking Methods for Comparing the Binding Affinities of Enantiomer Pairs for Their Protein Target

Abstract: Molecular docking is a computational chemistry method which has become essential for the rational drug design process. In this context, it has had great impact as a successful tool for the study of ligand-receptor interaction modes, and for the exploration of large chemical datasets through virtual screening experiments. Despite their unquestionable merits, docking methods are not reliable for predicting binding energies due to the simple scoring functions they use. However, comparisons between two or three complexes using the predicted binding energies as a criterion are commonly found in the literature. In the present work we tested how wise is it to trust the docking energies when two complexes between a target protein and enantiomer pairs are compared. For this purpose, a ligand library composed by 141 enantiomeric pairs was used, including compounds with biological activities reported against seven protein targets. Docking results using the software Glide (considering extra precision (XP), standard precision (SP), and high-throughput virtual screening (HTVS) modes) and AutoDock Vina were compared with the reported biological activities using a classification scheme. Our test failed for all modes and targets, demonstrating that an accurate prediction when binding energies of enantiomers are compared using docking may be due to chance. We also compared pairs of compounds with different molecular weights and found the same results.

Interplay of multiple interaction forces: Binding of tyrosine kinase inhibitor nintedanib with human serum albumin

Abstract: In this study, we have investigated the binding affinity of the newly approved tyrosine kinase inhibitor nintedanib (NIB) with human serum albumin under simulated physiological condition. The obtained results demonstrate that fluorescence intensity of human serum albumin (HSA) gets quenched by NIB and quenching occurs in static manner. Binding parameters calculated from modified Stern-Volmer equation shows that the drug binds to human serum albumin with a binding constant in the order of 10(3), with the number of binding sites approximately equal to one. Synchronous fluorescence data deciphered the change in the microenvironment of tryptophan (Trp) residue in HSA. Circular dichroism data showed an increase in helical content upon drug binding. Dynamic light scattering measurements deciphered the reduction in hydrodynamic radii of the protein, further differential scanning calorimetry results shows that nintedanib increase the thermostability of HSA. Molecular docking results demonstrated that major binding forces involved in the complex formation are hydrogen bonding and hydrophobic interaction.

CSM-lig: a web server for assessing and comparing protein–small molecule affinities.

Abstract: Determining the affinity of a ligand for a given protein is a crucial component of drug development and understanding their biological effects. Predicting binding affinities is a challenging and difficult task, and despite being regarded as poorly predictive, scoring functions play an important role in the analysis of molecular docking results. Here, we present CSM-Lig (http://structure.bioc.cam.ac.uk/csm_lig), a web server tailored to predict the binding affinity of a protein-small molecule complex, encompassing both protein and small-molecule complementarity in terms of shape and chemistry via graph-based structural signatures. CSM-Lig was trained and evaluated on different releases of the PDBbind databases, achieving a correlation of up to 0.86 on 10-fold cross validation and 0.80 in blind tests, performing as well as or better than other widely used methods. The web server allows users to rapidly and automatically predict binding affinities of collections of structures and assess the interactions made. We believe CSM-lig would be an invaluable tool for helping assess docking poses, the effects of multiple mutations, including insertions, deletions and alternative splicing events, in protein-small molecule affinity, unraveling important aspects that drive protein-compound recognition.

GalaxyRefineComplex: Refinement of protein-protein complex model structures driven by interface repacking.

Abstract: Protein-protein docking methods have been widely used to gain an atomic-level understanding of protein interactions. However, docking methods that employ low-resolution energy functions are popular because of computational efficiency. Low-resolution docking tends to generate protein complex structures that are not fully optimized. GalaxyRefineComplex takes such low-resolution docking structures and refines them to improve model accuracy in terms of both interface contact and inter-protein orientation. This refinement method allows flexibility at the protein interface and in the overall docking structure to capture conformational changes that occur upon binding. Symmetric refinement is also provided for symmetric homo-complexes. This method was validated by refining models produced by available docking programs, including ZDOCK and M-ZDOCK, and was successfully applied to CAPRI targets in a blind fashion. An example of using the refinement method with an existing docking method for ligand binding mode prediction of a drug target is also presented. A web server that implements the method is freely available at http://galaxy.seoklab.org/refinecomplex.

Using reverse docking for target identification and its applications for drug discovery

Abstract: Introduction: In contrast to traditional molecular docking, inverse or reverse docking is used for identifying receptors for a given ligand among a large number of receptors. Reverse docking can be used to discover new targets for existing drugs and natural compounds, explain polypharmacology and the molecular mechanism of a substance, find alternative indications of drugs through drug repositioning, and detecting adverse drug reactions and drug toxicity.Areas covered: In this review, the authors examine how reverse docking methods have evolved over the past fifteen years and how they have been used for target identification and related applications for drug discovery. They discuss various aspects of target databases, reverse docking tools and servers.Expert opinion: There are several issues related to reverse docking methods such as target structure dataset construction, computational efficiency, how to include receptor flexibility, and most importantly, how to properly normalize the docking scor...

Molecular Docking and In Silico ADMET Study Reveals Acylguanidine 7a as a Potential Inhibitor of β-Secretase

Abstract: Amyloidogenic pathway in Alzheimer's disease (AD) involves breakdown of APP by β-secretase followed by γ-secretase and results in formation of amyloid beta plaque. β-secretase has been a promising target for developing novel anti-Alzheimer drugs. To test different molecules for this purpose, test ligands like acylguanidine 7a, rosiglitazone, pioglitazone, and tartaric acid were docked against our target protein β-secretase enzyme retrieved from Protein Data Bank, considering MK-8931 (phase III trial, Merck) as the positive control. Docking revealed that, with respect to their free binding energy, acylguanidine 7a has the lowest binding energy followed by MK-8931 and pioglitazone and binds significantly to β-secretase. In silico ADMET predictions revealed that except tartaric acid all other compounds had minimal toxic effects and had good absorption as well as solubility characteristics. These compounds may serve as potential lead compound for developing new anti-Alzheimer drug.

Synthesis and Biological Investigation of Oxazole Hydroxamates as Highly Selective Histone Deacetylase 6 (HDAC6) Inhibitors

Abstract: Histone deacetylase 6 (HDAC6) catalyzes the removal of an acetyl group from lysine residues of several non-histone proteins. Here we report the preparation of thiazole-, oxazole-, and oxadiazole-containing biarylhydroxamic acids by a short synthetic procedure. We identified them as selective HDAC6 inhibitors by investigating the inhibition of recombinant HDAC enzymes and the protein acetylation in cells by Western blotting (tubulin vs histone acetylation). The most active compounds exhibited nanomolar potency and high selectivity for HDAC6. For example, an oxazole hydroxamate inhibits HDAC6 with an IC50 of 59 nM and has a selectivity index of >200 against HDAC1 and HDAC8. This is the first report showing that the nature of a heterocycle directly connected to a zinc binding group (ZBG) can be used to modulate subtype selectivity and potency for HDAC6 inhibitors to such an extent. We rationalize the high potency and selectivity of the oxazoles by molecular modeling and docking.

A new, improved hybrid scoring function for molecular docking and scoring based on AutoDock and AutoDock Vina

Abstract: Automated docking is one of the most important tools for structure-based drug design that allows prediction of ligand binding poses and also provides an estimate of how well small molecules fit in the binding site of a protein. A new scoring function based on AutoDock and AutoDock Vina has been introduced. The new hybrid scoring function is a linear combination of the two scoring function components derived from a multiple linear regression fitting procedure. The scoring function was built on a training set of 2412 protein-ligand complexes from pdbbind database (www.pdbbind.org.cn, version 2012). A test set of 313 complexes that appeared in the 2013 version was used for validation purposes. The new hybrid scoring function performed better than the original functions, both on training and test sets of protein-ligand complexes, as measured by the non-parametric Pearson correlation coefficient, R, mean absolute error (MAE), and root-mean-square error (RMSE) between the experimental binding affinities and the docking scores. The function also gave one of the best results among more than 20 scoring functions tested on the core set of the pdbbind database. The new AutoDock hybrid scoring function will be implemented in modified version of AutoDock.

SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions

Abstract: Background: Docking allows to predict ligand binding to proteins, since the 3D-structure for the target is available. Several docking studies have been carried out to identify potential ligands for drug targets. Many of these studies resulted in the leads that were later developed as drugs. Objective: Our goal here is to describe the development of an integrated computational tool to assess docking accuracy and build new scoring functions to predict ligandbinding affinity. Method: We carried out docking simulations using MVD program for a data set available on CSAR 2014 database (coagulation factor Xa) for which ligand-binding information and structures are available. These docking results were analyzed using SAnDReS available at www.sandres.net. Machine learning methods were applied to build new scoring functions and our results were compared with previously published benchmarks. Results: Our integrated docking strategy generated poses with docking accuracy higher than previously published benchmarks. In addition, the new scoring function developed using SAnDReS shows better performance than well-established scoring functions such the ones available in Autodock, Autodock- Vina, Gold, Glide, and MVD. Conclusion: The big data generated during docking lacked an integrated computational tool for statistical analysis of the influence of structural parameters on docking and scoring function performance. Here we describe methods to evaluate docking results using SAnDReS, a computational environment for statistical analysis of docking results and development of scoring functions. We believe that SAnDReS is a computational tool with potential to improve accuracy in docking projects.

Accelerated structure-based design of chemically diverse allosteric modulators of a muscarinic G protein-coupled receptor

Abstract: Design of ligands that provide receptor selectivity has emerged as a new paradigm for drug discovery of G protein-coupled receptors, and may, for certain families of receptors, only be achieved via identification of chemically diverse allosteric modulators. Here, the extracellular vestibule of the M2 muscarinic acetylcholine receptor (mAChR) is targeted for structure-based design of allosteric modulators. Accelerated molecular dynamics (aMD) simulations were performed to construct structural ensembles that account for the receptor flexibility. Compounds obtained from the National Cancer Institute (NCI) were docked to the receptor ensembles. Retrospective docking of known ligands showed that combining aMD simulations with Glide induced fit docking (IFD) provided much-improved enrichment factors, compared with the Glide virtual screening workflow. Glide IFD was thus applied in receptor ensemble docking, and 38 top-ranked NCI compounds were selected for experimental testing. In [(3)H]N-methylscopolamine radioligand dissociation assays, approximately half of the 38 lead compounds altered the radioligand dissociation rate, a hallmark of allosteric behavior. In further competition binding experiments, we identified 12 compounds with affinity of ≤30 μM. With final functional experiments on six selected compounds, we confirmed four of them as new negative allosteric modulators (NAMs) and one as positive allosteric modulator of agonist-mediated response at the M2 mAChR. Two of the NAMs showed subtype selectivity without significant effect at the M1 and M3 mAChRs. This study demonstrates an unprecedented successful structure-based approach to identify chemically diverse and selective GPCR allosteric modulators with outstanding potential for further structure-activity relationship studies.

Function-specific virtual screening for GPCR ligands using a combined scoring method.

Abstract: The ability of scoring functions to correctly select and rank docking poses of small molecules in protein binding sites is highly target dependent, which presents a challenge for structure-based drug discovery. Here we describe a virtual screening method that combines an energy-based docking scoring function with a molecular interaction fingerprint (IFP) to identify new ligands based on G protein-coupled receptor (GPCR) crystal structures. The consensus scoring method is prospectively evaluated by: 1) the discovery of chemically novel, fragment-like, high affinity histamine H1 receptor (H1R) antagonists/inverse agonists, 2) the selective structure-based identification of s2-adrenoceptor (s2R) agonists and 3) the experimental validation and comparison of the combined and individual scoring approaches. Systematic retrospective virtual screening simulations allowed the definition of scoring cut-offs for the identification of H1R and s2R ligands and the selection of an optimal s-adrenoceptor crystal structure for the discrimination between s2R agonists and antagonists. The consensus approach resulted in the experimental validation of 53% of the s2R and 73% of the H1R virtual screening hits with up to nanomolar affinities and potencies. The selective identification of s2R agonists shows the possibilities of structure-based prediction of GPCR ligand function by integrating protein-ligand binding mode information.

Curcumin specifically binds to the human calcium–calmodulin-dependent protein kinase IV: fluorescence and molecular dynamics simulation studies

Abstract: Calcium–calmodulin-dependent protein kinase IV (CAMK4) plays significant role in the regulation of calcium-dependent gene expression, and thus, it is involved in varieties of cellular functions such as cell signaling and neuronal survival. On the other hand, curcumin, a naturally occurring yellow bioactive component of turmeric possesses wide spectrum of biological actions, and it is widely used to treat atherosclerosis, diabetes, cancer, and inflammation. It also acts as an antioxidant. Here, we studied the interaction of curcumin with human CAMK4 at pH 7.4 using molecular docking, molecular dynamics (MD) simulations, fluorescence binding, and surface plasmon resonance (SPR) methods. We performed MD simulations for both neutral and anionic forms of CAMK4-curcumin complexes for a reasonably long time (150 ns) to see the overall stability of the protein–ligand complex. Molecular docking studies revealed that the curcumin binds in the large hydrophobic cavity of kinase domain of CAMK4 through several hydrop...

Design, Synthesis, Fungicidal Activity, and Unexpected Docking Model of the First Chiral Boscalid Analogues Containing Oxazolines.

Abstract: Chirality greatly influences the biological and pharmacological properties of a pesticide and will contribute to unnecessary environmental loading and undesired ecological impact No structure and activity relationship (SAR) of enantiopure succinate dehydrogenase inhibitors (SDHIs) was documented during the structure optimization of boscalids On the basis of commercial SDHIs, oxazoline natural products, and versatile oxazoline ligands in organic synthesis, the first effort was devoted to explore the chiral SDHIs and the preliminary mechanism thereof Fine-tuning furnished chiral nicotinamides 4ag as a more promising fungicidal candidate against Rhizoctonia solani, Botrytis cinerea, and Sclerotinia sclerotiorum, with EC50 values of 058, 042, and 210 mg/L, respectively In vivo bioassay and molecular docking were investigated to explore the potential in practical application and plausible novelty in action mechanism, respectively The unexpected molecular docking model showed the different chiral effect

Docking and ADMET prediction of few GSK-3 inhibitors divulges 6-bromoindirubin-3-oxime as a potential inhibitor.

Abstract: GSK-3 is a member of cellular kinases with diversified functions such as cellular differentiation, metabolic signaling, neuronal functions and apoptosis. It has been validated as an important therapeutic target in Alzheimer's disease and type 2 diabetes. Few molecules targeting GSK-3 are currently in clinical trials. In this study, we have compared certain docking and computational ADME (Absorption, Distribution, Metabolism, Excretion) parameters of a few GSK-3 targeted ligands (Indirubin, Hymenialdisine, Meridianins, 6-bromoindirubin-3-oxime) against two control molecules (Tideglusib and LY-2090314) to derive and analyze the basic drug-like properties of the test compounds. Docking between the GSK-3 and various ligands was done using AutoDock while ADME parameters were derived from ADMET server PreADMET and admetSAR. Various docked images were retrieved from docking, indicating the docking sites in the target protein. Out of four compounds tested, 6-bromoindirubin-3-oxime (6-BIO) was found as the best docking and ADME parameters, followed by Hymenialdisine (HMD). The LigPlot interaction results show two residues Leu (188) and Thr (138) to be common at the interaction site. The LD50 of 6-BIO is better than one of the control ligands while very similar to the other. Some of the parameters were very similar to the control ligands, thus, making it a suitable candidate among the test ligands. From this in-silico study, we concluded that 6-BIO is a potent drug candidate which could be further tested in vitro and in vivo to establish a drug molecule. Since, 6-BIO is a chemically modified form of the basic molecule Indirubin, we can hypothesize that certain other modified indirubins could be tested as GSK-3 targeted ligands.