Showing papers on "Docking (molecular) published in 2019"

Molecular Docking: Shifting Paradigms in Drug Discovery.

Abstract: Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

Ultra-large library docking for discovering new chemotypes

Abstract: Despite intense interest in expanding chemical space, libraries containing hundreds-of-millions to billions of diverse molecules have remained inaccessible. Here we investigate structure-based docking of 170 million make-on-demand compounds from 130 well-characterized reactions. The resulting library is diverse, representing over 10.7 million scaffolds that are otherwise unavailable. For each compound in the library, docking against AmpC β-lactamase (AmpC) and the D4 dopamine receptor were simulated. From the top-ranking molecules, 44 and 549 compounds were synthesized and tested for interactions with AmpC and the D4 dopamine receptor, respectively. We found a phenolate inhibitor of AmpC, which revealed a group of inhibitors without known precedent. This molecule was optimized to 77 nM, which places it among the most potent non-covalent AmpC inhibitors known. Crystal structures of this and other AmpC inhibitors confirmed the docking predictions. Against the D4 dopamine receptor, hit rates fell almost monotonically with docking score, and a hit-rate versus score curve predicted that the library contained 453,000 ligands for the D4 dopamine receptor. Of 81 new chemotypes discovered, 30 showed submicromolar activity, including a 180-pM subtype-selective agonist of the D4 dopamine receptor. Using a make-on-demand library that contains hundreds-of-millions of molecules, structure-based docking was used to identify compounds that, after synthesis and testing, are shown to interact with AmpC β-lactamase and the D4 dopamine receptor with high affinity.

Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective

Abstract: Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don't always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, comparison of performance for existing tools and algorithms, state of art in docking, recent trends of diseases and current drug industries, evidence from clinical trials and post-marketing surveillance are discussed. These aspects of the molecular drug designing paradigm are quite controversial and challenging and this review would be an asset to the bioinformatics and drug designing communities.

Characteristics of flavonoids as potent MERS‐CoV 3C‐like protease inhibitors

Abstract: Middle East respiratory syndrome-coronavirus (MERS-CoV) is a zoonotic virus transmitted between animals and human beings. It causes MERS with high mortality rate. However, no vaccine or specific treatment is currently available. Since antiviral activity of some flavonoids is known, we applied a flavonoid library to probe inhibitory compounds against MERS-CoV 3C-like protease (3CLpro). Herbacetin, isobavachalcone, quercetin 3-β-d-glucoside and helichrysetin were found to block the enzymatic activity of MERS-CoV 3CLpro. The binding of the four flavonoids was also confirmed independently using a tryptophan-based fluorescence method. The systematic comparison of the binding affinity of flavonoids made it possible to infer their scaffolds and functional groups required to bind with MERS-CoV 3CLpro. An induced-fit docking analysis revealed that S1 and S2 sites play a role in interaction with flavonoids. The experimental and computational study showed that flavonol and chalcone are favourite scaffolds to bind with the catalytic site of MERS-CoV 3CLpro. It was also deduced that some flavonoid derivatives with hydrophobic or carbohydrate attached to their core structures have a good inhibitory effect. Therefore, we suggest that flavonoids with these characteristics can be used as templates to develop potent MERS-CoV 3CLpro inhibitors.

AMolecular Description of Biological Membrane Components by Computer Aided Conformational Analysis

Abstract: Volume II: Conformational Analysis of Arachidonic Acid Derivatives and Their Ca Complexes in Interaction with Membranes Transconformation of Ionophore at the Lipid-Water Interface Mode of Insertion of Azole Antifungals and Sterols in Biological Membranes Conformational Analysis of Cardiovascular Drugs-Lipids Interactions: Propranolol and Amiodarone Interaction Between Aminoglycosides Antibiotics and Phospholipids in Relation to Their Toxicity Adriamycin-Mitochondrial Membrane Interaction and Cardiotoxicity Theoretical Contribution to Understanding of Ion Channels in Membranes: from Gramicidin A to Bundles of x-Helices N-Acetyl-Gramicidin A: Molecular Mechanics Study of Head-to-Head Docking Insight into Cytochrome b Structure-Function Relationship by the Combined Approaches of Genetics and Computer Modeling Molecular Dynamics Simulations on the Binding of Heparin to Apolipoprotein E: Theoretical and Experimental Consideration Identification and Computer Modeling of Functional Domains in Plasma Apolipoproteins Orientation into the Lipid Bilayer of an Asymmetric Amphipathic Helical Peptide Located at the N-Terminus of Viral Fusion Proteins

OnionNet: a Multiple-Layer Intermolecular-Contact-Based Convolutional Neural Network for Protein-Ligand Binding Affinity Prediction.

Abstract: Open in a separate window Computational drug discovery provides an efficient tool for helping large-scale lead molecule screening. One of the major tasks of lead discovery is identifying molecules with promising binding affinities toward a target, a protein in general. The accuracies of current scoring functions that are used to predict the binding affinity are not satisfactory enough. Thus, machine learning or deep learning based methods have been developed recently to improve the scoring functions. In this study, a deep convolutional neural network model (called OnionNet) is introduced; its features are based on rotation-free element-pair-specific contacts between ligands and protein atoms, and the contacts are further grouped into different distance ranges to cover both the local and nonlocal interaction information between the ligand and the protein. The prediction power of the model is evaluated and compared with other scoring functions using the comparative assessment of scoring functions (CASF-2013) benchmark and the v2016 core set of the PDBbind database. The robustness of the model is further explored by predicting the binding affinities of the complexes generated from docking simulations instead of experimentally determined PDB structures.

Progress in molecular docking

Abstract: In recent years, since the molecular docking technique can greatly improve the efficiency and reduce the research cost, it has become a key tool in computer-assisted drug design to predict the binding affinity and analyze the interactive mode. This study introduces the key principles, procedures and the widely-used applications for molecular docking. Also, it compares the commonly used docking applications and recommends which research areas are suitable for them. Lastly, it briefly reviews the latest progress in molecular docking such as the integrated method and deep learning. Limited to the incomplete molecular structure and the shortcomings of the scoring function, current docking applications are not accurate enough to predict the binding affinity. However, we could improve the current molecular docking technique by integrating the big biological data into scoring function.

Assessing the performance of MM/PBSA and MM/GBSA methods. 9. Prediction reliability of binding affinities and binding poses for protein–peptide complexes

Abstract: A significant number of protein-protein interactions (PPIs) are mediated through the interactions between proteins and peptide segments, and therefore determination of protein-peptide interactions (PpIs) is critical to gain an in-depth understanding of the PPI network and even design peptides or small molecules capable of modulating PPIs. Computational approaches, especially molecular docking, provide an efficient way to model PpIs, and a reliable scoring function that can recognize the correct binding conformations for protein-peptide complexes is one of the most important components in protein-peptide docking. The end-point binding free energy calculation methods, such as MM/GBSA and MM/PBSA, are theoretically more rigorous than most empirical and semi-empirical scoring functions designed for protein-peptide docking, but their performance in predicting binding affinities and binding poses for protein-peptide systems has not been systematically assessed. In this study, we first evaluated the capability of MM/GBSA and MM/PBSA with different solvation models, interior dielectric constants (ein) and force fields to predict the binding affinities for 53 protein-peptide complexes. For the 19 short peptides with 5-12 residues, MM/PBSA based on the minimized structures in explicit solvent with the ff99 force field and ein = 2 yields the best correlation between the predicted binding affinities and the experimental data (rp = 0.748), while for the 34 medium-size peptides with 20-25 residues, MM/GBSA based on 1 ns of molecular dynamics (MD) simulations in implicit solvent with the ff03 force field, the GBOBC1 model and a low interior dielectric constant (ein = 1) yields the best accuracy (rp = 0.735). Then, we assessed the rescoring capability of MM/PBSA and MM/GBSA to distinguish the correct binding conformations from the decoys for 112 protein-peptide systems. The results illustrate that MM/PBSA based on the minimized structures with the ff99 or ff14SB force field and MM/GBSA based on the minimized structures with the ff03 force field show excellent capability to recognize the near-native binding poses for the short and medium-size peptides, respectively, and they outperform the predictions given by two popular protein-peptide docking algorithms (pepATTRACT and HPEPDOCK). Therefore, MM/PBSA and MM/GBSA are powerful tools to predict the binding affinities and identify the correct binding poses for protein-peptide systems.

New Series of Thiazole Derivatives: Synthesis, Structural Elucidation, Antimicrobial Activity, Molecular Modeling and MOE Docking.

Abstract: Based on the extensive biological activities of thiazole derivatives against different types of diseases, we are interested in the effective part of many natural compounds, so we synthesized a new series of compounds containing di-, tri- and tetrathiazole moieties. The formation of such derivatives proceeded via reaction of 2-bromo-1-(4-methyl-2-(methylamino)thiazol-5-yl)ethan-1-one with heterocyclic amines, o-aminothiophenol and thiosemicarbazone derivatives. The structure and mechanistic pathways for all products were discussed and proved based on spectral results, in addition to conformational studies. Our aim after the synthesis is to investigate their antimicrobial activity against various types of bacteria and fungi species. Preceeding such an investigation, a molecular docking study was carried out with selected conformers, as representative examples, against three pathogen-proteins. This preliminary stage could support the biological application. The potency of these compounds as antimicrobial agents has been evaluated. The results showed that derivatives which have di- and trithiazole rings displayed high activity that exceeds the used standard antibiotic.

Molegro Virtual Docker for Docking

Abstract: Molegro Virtual Docker is a protein-ligand docking simulation program that allows us to carry out docking simulations in a fully integrated computational package. MVD has been successfully applied to hundreds of different proteins, with docking performance similar to other docking programs such as AutoDock4 and AutoDock Vina. The program MVD has four search algorithms and four native scoring functions. Considering that we may have water molecules or not in the docking simulations, we have a total of 32 docking protocols. The integration of the programs SAnDReS ( https://github.com/azevedolab/sandres ) and MVD opens the possibility to carry out a detailed statistical analysis of docking results, which adds to the native capabilities of the program MVD. In this chapter, we describe a tutorial to carry out docking simulations with MVD and how to perform a statistical analysis of the docking results with the program SAnDReS. To illustrate the integration of both programs, we describe the redocking simulation focused the cyclin-dependent kinase 2 in complex with a competitive inhibitor.

Benchmarking of different molecular docking methods for protein-peptide docking.

Abstract: Molecular docking studies on protein-peptide interactions are a challenging and time-consuming task because peptides are generally more flexible than proteins and tend to adopt numerous conformations. There are several benchmarking studies on protein-protein, protein-ligand and nucleic acid-ligand docking interactions. However, a series of docking methods is not rigorously validated for protein-peptide complexes in the literature. Considering the importance and wide application of peptide docking, we describe benchmarking of 6 docking methods on 133 protein-peptide complexes having peptide length between 9 to 15 residues. The performance of docking methods was evaluated using CAPRI parameters like FNAT, I-RMSD, L-RMSD. Firstly, we performed blind docking and evaluate the performance of the top docking pose of each method. It was observed that FRODOCK performed better than other methods with average L-RMSD of 12.46 A. The performance of all methods improved significantly for their best docking pose and achieved highest average L-RMSD of 3.72 A in case of FRODOCK. Similarly, we performed re-docking and evaluated the performance of the top and best docking pose of each method. We achieved the best performance in case of ZDOCK with average L-RMSD 8.60 A and 2.88 A for the top and best docking pose respectively. Methods were also evaluated on 40 protein-peptide complexes used in the previous benchmarking study, where peptide have length up to 5 residues. In case of best docking pose, we achieved the highest average L-RMSD of 4.45 A and 2.09 A for the blind docking using FRODOCK and re-docking using AutoDock Vina respectively. The study shows that FRODOCK performed best in case of blind docking and ZDOCK in case of re-docking. There is a need to improve the ranking of docking pose generated by different methods, as the present ranking scheme is not satisfactory. To facilitate the scientific community for calculating CAPRI parameters between native and docked complexes, we developed a web-based service named PPDbench ( http://webs.iiitd.edu.in/raghava/ppdbench/ ).

Ligand binding to human prostaglandin E receptor EP 4 at the lipid-bilayer interface

Abstract: Prostaglandin E receptor EP4, a G-protein-coupled receptor, is involved in disorders such as cancer and autoimmune disease. Here, we report the crystal structure of human EP4 in complex with its antagonist ONO-AE3-208 and an inhibitory antibody at 3.2 A resolution. The structure reveals that the extracellular surface is occluded by the extracellular loops and that the antagonist lies at the interface with the lipid bilayer, proximal to the highly conserved Arg316 residue in the seventh transmembrane domain. Functional and docking studies demonstrate that the natural agonist PGE2 binds in a similar manner. This structural information also provides insight into the ligand entry pathway from the membrane bilayer to the EP4 binding pocket. Furthermore, the structure reveals that the antibody allosterically affects the ligand binding of EP4. These results should facilitate the design of new therapeutic drugs targeting both orthosteric and allosteric sites in this receptor family. The structure of human prostaglandin E receptor EP4 in complex with antagonist ONO-AE3-208 and a functional antibody reveals a ligand-binding site at the interface of the lipid bilayer that is unique among GPCRs.

Plant-derived antiviral drugs as novel hepatitis B virus inhibitors: Cell culture and molecular docking study.

Abstract: Despite high anti-HBV efficacies, while the nucleoside analogs (e.g., lamivudine) lead to the emergence of drug-resistance, interferons (e.g., IFN-α causes adverse side-effects. Comparatively, various natural or plant products have shown similar or even better efficacy. Hence, new antiviral strategies must focus not only on synthetic molecules but also on potential natural compounds. In this report, we have combined the in vitro cell culture and in silico molecular docking methods to assess the novel anti-HBV activity and delineate the inhibitory mechanism of selected plant-derived pure compounds of different classes. Of the tested (2.5-50 μg/ml) twelve non-cytotoxic compounds, ten (10 μg/ml) were found to maximally inhibit HBsAg production at day 5. Compared to quercetin (73%), baccatin III (71%), psoralen (67%), embelin (65%), menisdaurin (64%) and azadirachtin (62%) that showed high inhibition of HBeAg synthesis, lupeol (52%), rutin (47%), β-sitosterol (43%) and hesperidin (41%) had moderate efficacies against HBV replication. Further assessment of quercetin in combination with the highly active compounds, enhanced its anti-HBV activity up to 10%. Being the most important drug target, a 3-D structure of HBV polymerase (Pol/RT) was modeled and docked with the active compounds, including lamivudine as standard. Docking of lamivudine indicated strong interaction with the modeled HBV Pol active-site residues that formed stable complex (∆G = −5.2 kcal/mol). Similarly, all the docked antiviral compounds formed very stable complexes with HBV Pol (∆G = −6.1 to −9.3 kcal/mol). Taken together, our data suggest the anti-HBV potential of the tested natural compounds as novel viral Pol/RT inhibitors.

Structure Based Multitargeted Molecular Docking Analysis of Selected Furanocoumarins against Breast Cancer.

Abstract: Breast cancer is one of the biggest global dilemmas and its current therapy is to target the hormone receptors by the use of partial agonists/antagonists. Potent drugs for breast cancer treatment are Tamoxifen, Trastuzumab, Paclitaxel, etc. which show adverse effects and resistance in patients. The aim of the study has been on certain phytochemicals which has potent actions on ERα, PR, EGFR and mTOR inhibition. The current study is performed by the use of molecular docking as protein-ligand interactions play a vital role in drug design. The 3D structures of ERα, PR, EGFR and mTOR were obtained from the protein data bank and docked with 23 3D PubChem structures of furanocoumarin compounds using FlexX. Drug-likeness property was checked by applying the Lipinski’s rule of five on the furanocoumarins to evaluate anti-breast cancer activity. Antagonist and inhibition assay of ERα, EGFR and mTOR respectively has been performed using appropriate in-vitro techniques. The results confirm that Xanthotoxol has the best docking score for breast cancer followed by Bergapten, Angelicin, Psoralen and Isoimperatorin. Further, the in-vitro results also validate the molecular docking analysis. This study suggests that the selected furanocoumarins can be further investigated and evaluated for breast cancer treatment and management strategies.

Structural basis for antiarrhythmic drug interactions with the human cardiac sodium channel

Abstract: The human voltage-gated sodium channel, hNaV1.5, is responsible for the rapid upstroke of the cardiac action potential and is target for antiarrhythmic therapy. Despite the clinical relevance of hNaV1.5-targeting drugs, structure-based molecular mechanisms of promising or problematic drugs have not been investigated at atomic scale to inform drug design. Here, we used Rosetta structural modeling and docking as well as molecular dynamics simulations to study the interactions of antiarrhythmic and local anesthetic drugs with hNaV1.5. These calculations revealed several key drug binding sites formed within the pore lumen that can simultaneously accommodate up to two drug molecules. Molecular dynamics simulations identified a hydrophilic access pathway through the intracellular gate and a hydrophobic access pathway through a fenestration between DIII and DIV. Our results advance the understanding of molecular mechanisms of antiarrhythmic and local anesthetic drug interactions with hNaV1.5 and will be useful for rational design of novel therapeutics.

Synthesis and Biological Investigation of Phenothiazine-Based Benzhydroxamic Acids as Selective Histone Deacetylase 6 Inhibitors

Abstract: The phenothiazine system was identified as a favorable cap group for potent and selective histone deacetylase 6 (HDAC6) inhibitors. Here, we report the preparation and systematic variation of phenothiazines and their analogues containing a benzhydroxamic acid moiety as the zinc-binding group. We evaluated their ability to selectively inhibit HDAC6 by a recombinant HDAC enzyme assay, by determining the protein acetylation levels in cells by western blotting (tubulin vs histone acetylation), and by assessing their effects on various cancer cell lines. Structure–activity relationship studies revealed that incorporation of a nitrogen atom into the phenothiazine framework results in increased potency and selectivity for HDAC6 (more than 500-fold selectivity relative to the inhibition of HDAC1, HDAC4, and HDAC8), as rationalized by molecular modeling and docking studies. The binding mode was confirmed by co-crystallization of the potent azaphenothiazine inhibitor with catalytic domain 2 from Danio rerio HDAC6.

Ensemble Docking in Drug Discovery: How Many Protein Configurations from Molecular Dynamics Simulations are Needed To Reproduce Known Ligand Binding?

Abstract: Ensemble docking in drug discovery or chemical biology uses dynamical simulations of target proteins to generate binding site conformations for docking campaigns. We show that 600 ns molecular dynamics simulations of four G-protein-coupled receptors in their membrane environments generate ensembles of protein configurations that, collectively, are selected by 70?99% of the known ligands of these proteins. Therefore, the process of ligand recognition by conformational selection can be reproduced by combining molecular dynamics and docking calculations. Clustering of the molecular dynamics trajectories, however, does not necessarily identify the protein conformations that are most often selected by the ligands.

Inhibition of acetylcholinesterase and butyrylcholinesterase with uracil derivatives: kinetic and computational studies.

Abstract: Acetylcholinesterase (AChE) and Butyrylcholinesterase (BuChE) inhibitors are interesting compounds for different therapeutic applications, among which Alzheimer’s disease. Here, we investigated the inhibition of these cholinesterases with uracil derivatives. The mechanism of inhibition of these enzymes was observed to be due to obstruction of the active site entrance by the inhibitors scaffold. Molecular docking and molecular dynamics (MD) simulations demonstrated the possible key interactions between the studied ligands and amino acid residues at different regions of the active sites of AChE and BuChE. Being diverse of the classical AChE and BuChE inhibitors, the investigated uracil derivatives may be used as lead molecules for designing new therapeutically effective enzyme inhibitors.