Showing papers on "Docking (molecular) published in 2014"

Receptor–ligand molecular docking

Abstract: Docking methodology aims to predict the experimental binding modes and affinities of small molecules within the binding site of particular receptor targets and is currently used as a standard computational tool in drug design for lead compound optimisation and in virtual screening studies to find novel biologically active molecules. The basic tools of a docking methodology include a search algorithm and an energy scoring function for generating and evaluating ligand poses. In this review, we present the search algorithms and scoring functions most commonly used in current molecular docking methods that focus on protein–ligand applications. We summarise the main topics and recent computational and methodological advances in protein–ligand docking. Protein flexibility, multiple ligand binding modes and the free-energy landscape profile for binding affinity prediction are important and interconnected challenges to be overcome by further methodological developments in the docking field.

Docking Covalent Inhibitors: A Parameter Free Approach To Pose Prediction and Scoring

Abstract: Although many popular docking programs include a facility to account for covalent ligands, large-scale systematic docking validation studies of covalent inhibitors have been sparse. In this paper, we present the development and validation of a novel approach for docking and scoring covalent inhibitors, which consists of conventional noncovalent docking, heuristic formation of the covalent attachment point, and structural refinement of the protein–ligand complex. This approach combines the strengths of the docking program Glide and the protein structure modeling program Prime and does not require any parameter fitting for the study of additional covalent reaction types. We first test this method by predicting the native binding geometry of 38 covalently bound complexes. The average RMSD of the predicted poses is 1.52 A, and 76% of test set inhibitors have an RMSD of less than 2.0 A. In addition, the apparent affinity score constructed herein is tested on a virtual screening study and the characterization o...

Agonist-bound structure of the human P2Y12 receptor

Abstract: The P2Y12 receptor (P2Y12R), one of eight members of the P2YR family expressed in humans, is one of the most prominent clinical drug targets for inhibition of platelet aggregation. Although mutagenesis and modelling studies of the P2Y12R provided useful insights into ligand binding, the agonist and antagonist recognition and function at the P2Y12R remain poorly understood at the molecular level. Here we report the structures of the human P2Y12R in complex with the full agonist 2-methylthio-adenosine-5'-diphosphate (2MeSADP, a close analogue of endogenous agonist ADP) at 2.5 A resolution, and the corresponding ATP derivative 2-methylthio-adenosine-5'-triphosphate (2MeSATP) at 3.1 A resolution. These structures, together with the structure of the P2Y12R with antagonist ethyl 6-(4-((benzylsulfonyl)carbamoyl)piperidin-1-yl)-5-cyano-2-methylnicotinate (AZD1283), reveal striking conformational changes between nucleotide and non-nucleotide ligand complexes in the extracellular regions. Further analysis of these changes provides insight into a distinct ligand binding landscape in the δ-group of class A G-protein-coupled receptors (GPCRs). Agonist and non-nucleotide antagonist adopt different orientations in the P2Y12R, with only partially overlapped binding pockets. The agonist-bound P2Y12R structure answers long-standing questions surrounding P2Y12R-agonist recognition, and reveals interactions with several residues that had not been reported to be involved in agonist binding. As a first example, to our knowledge, of a GPCR in which agonist access to the binding pocket requires large-scale rearrangements in the highly malleable extracellular region, the structural and docking studies will therefore provide invaluable insight into the pharmacology and mechanisms of action of agonists and different classes of antagonists for the P2Y12R and potentially for other closely related P2YRs.

AutoDock4(Zn): an improved AutoDock force field for small-molecule docking to zinc metalloproteins.

Abstract: Zinc is present in a wide variety of proteins and is important in the metabolism of most organisms Zinc metalloenzymes are therapeutically relevant targets in diseases such as cancer, heart disease, bacterial infection, and Alzheimer’s disease In most cases a drug molecule targeting such enzymes establishes an interaction that coordinates with the zinc ion Thus, accurate prediction of the interaction of ligands with zinc is an important aspect of computational docking and virtual screening against zinc containing proteins We have extended the AutoDock force field to include a specialized potential describing the interactions of zinc-coordinating ligands This potential describes both the energetic and geometric components of the interaction The new force field, named AutoDock4Zn, was calibrated on a data set of 292 crystal complexes containing zinc Redocking experiments show that the force field provides significant improvement in performance in both free energy of binding estimation as well as in r

Does a More Precise Chemical Description of Protein–Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?

Abstract: Predicting the binding affinities of large sets of diverse molecules against a range of macromolecular targets is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for exploiting and analyzing the outputs of docking, which is in turn an important tool in problems such as structure-based drug design. Classical scoring functions assume a predetermined theory-inspired functional form for the relationship between the variables that describe an experimentally determined or modeled structure of a protein–ligand complex and its binding affinity. The inherent problem of this approach is in the difficulty of explicitly modeling the various contributions of intermolecular interactions to binding affinity. New scoring functions based on machine-learning regression models, which are able to exploit effectively much larger amounts of experimental data and circumvent the need for a predetermined functional form, have already been shown to outperform a broad ra...

Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery

Abstract: The adoption of multiple conformations by proteins presents a challenge for ligand discovery using docking simulations. Now, a method for representing the conformational behaviour of a flexible protein in docking screens, which is guided by experimental crystallography data, is shown to predict protein conformation, ligand pose and aid the discovery of new ligands.

Docking studies on DNA intercalators.

Abstract: DNA is an important target for the treatment of multiple pathologies, most notably cancer. In particular, DNA intercalators have often been used as anticancer drugs. However, despite their relevance to drug discovery, only a few systematic computational studies were performed on DNA-intercalator complexes. In this work we have analyzed ligand binding sites preferences in 63 high resolution DNA-intercalator complexes available in the PDB and found that ligands bind preferentially between G and C and between the C and A base pairs (70% and 11%, respectively). Next, we examined the ability of AUTODOCK to accurately dock ligands into preformed intercalation sites. Following the optimization of the docking protocol, AUTODOCK was able to generate conformations with RMSD values 2.00 A suggests that AUTODOCK may overemphasize the hydrogen bonding term. A decision tree was built to identify ligands which are likely to be accurately docked based on their characteristics. This analysis revealed that AUTODOCK performs best for intercalators characterized by a large number of aromatic rings, low flexibility, high molecular weight, and a small number of hydrogen bond acceptors. Finally, for canonical B-DNA structures (where preformed sites are unavailable), we demonstrated that intercalation sites could be formed by inserting an anthracene moiety between the (anticipated) site-flanking base pairs and by relaxing the structure using either energy minimization or preferably molecular dynamics simulations. Such sites were suitable for the docking of different intercalators by AUTODOCK.

Molecular Mechanism of MBX2319 Inhibition of Escherichia coli AcrB Multidrug Efflux Pump and Comparison with Other Inhibitors

Abstract: Efflux pumps of the resistance nodulation division (RND) superfamily, such as AcrB, make a major contribution to multidrug resistance in Gram-negative bacteria. The development of inhibitors of the RND pumps would improve the efficacy of current and next-generation antibiotics. To date, however, only one inhibitor has been cocrystallized with AcrB. Thus, in silico structure-based analysis is essential for elucidating the interaction between other inhibitors and the efflux pumps. In this work, we used computer docking and molecular dynamics simulations to study the interaction between AcrB and the compound MBX2319, a novel pyranopyridine efflux pump inhibitor with potent activity against RND efflux pumps of Enterobacteriaceae species, as well as other known inhibitors (D13-9001, 1-[1-naphthylmethyl]-piperazine, and phenylalanylarginine-β-naphthylamide) and the binding of doxorubicin to the efflux-defective F610A variant of AcrB. We also analyzed the binding of a substrate, minocycline, for comparison. Our results show that MBX2319 binds very tightly to the lower part of the distal pocket in the B protomer of AcrB, strongly interacting with the phenylalanines lining the hydrophobic trap, where the hydrophobic portion of D13-9001 was found to bind by X-ray crystallography. Additionally, MBX2319 binds to AcrB in a manner that is similar to the way in which doxorubicin binds to the F610A variant of AcrB. In contrast, 1-(1-naphthylmethyl)-piperazine and phenylalanylarginine-β-naphthylamide appear to bind to somewhat different areas of the distal pocket in the B protomer of AcrB than does MBX2319. However, all inhibitors (except D13-9001) appear to distort the structure of the distal pocket, impairing the proper binding of substrates.

Drug Design Targeting Protein–Protein Interactions (PPIs) Using Multiple Ligand Simultaneous Docking (MLSD) and Drug Repositioning: Discovery of Raloxifene and Bazedoxifene as Novel Inhibitors of IL-6/GP130 Interface

Abstract: The IL-6/GP130/STAT3 pathway is critical for the progression of multiple types of cancers. We report here the discovery of raloxifene and bazedoxifene as novel inhibitors of IL-6/GP130 protein–protein interactions (PPIs) using multiple ligand simultaneous docking (MLSD) and drug repositioning approaches. Multiple drug scaffolds were simultaneously docked into hot spots of GP130 D1 domain by MLSD to compete with the key interacting residues of IL-6, followed by tethering to generate virtual hit compounds. Similarity searches of virtual hits on drug databases identified raloxifene and bazedoxifene as potential inhibitors of IL-6/GP130 interaction. In cancer cell assays both compounds bind to GP130 and demonstrated selective inhibition of IL-6 induced STAT3 phosphorylation and were significantly more potent than the previously reported natural product inhibitor MDL-A. The identified drugs represent a new class of lead compounds with piperidine, benzothiophene, and indole scaffolds to inhibit IL-6 induced hom...

Structure-Based Virtual Screening Approach for Discovery of Covalently Bound Ligands

Abstract: We present a fast and effective covalent docking approach suitable for large-scale virtual screening (VS) We applied this method to four targets (HCV NS3 protease, Cathepsin K, EGFR, and XPO1) with known crystal structures and known covalent inhibitors We implemented a customized “VS mode” of the Schrodinger Covalent Docking algorithm (CovDock), which we refer to as CovDock-VS Known actives and target-specific sets of decoys were docked to selected X-ray structures, and poses were filtered based on noncovalent protein–ligand interactions known to be important for activity We were able to retrieve 71%, 72%, and 77% of the known actives for Cathepsin K, HCV NS3 protease, and EGFR within 5% of the decoy library, respectively With the more challenging XPO1 target, where no specific interactions with the protein could be used for postprocessing of the docking results, we were able to retrieve 95% of the actives within 30% of the decoy library and achieved an early enrichment factor (EF1%) of 33 The poses

Contact-induced clustering of syntaxin and munc18 docks secretory granules at the exocytosis site

Abstract: Docking of secretory vesicles at the plasma membrane is a poorly understood prerequisite for exocytosis. Current models propose raft-like clusters containing syntaxin as docking receptor, but direct evidence for this is lacking. Here we provide quantitative measurements of several exocytosis proteins (syntaxin, SNAP25, munc18, munc13 and rab3) at the insulin granule release site and show that docking coincides with rapid de novo formation of syntaxin1/munc18 clusters at the nascent docking site. Formation of such clusters prevents undocking and is not observed during failed docking attempts. Overexpression of syntaxins' N-terminal Habc-domain competitively interferes with both cluster formation and successful docking. SNAP25 and munc13 are recruited to the docking site more than a minute later, consistent with munc13's reported role in granule priming rather than docking. We conclude that secretory vesicles dock by inducing syntaxin1/munc18 clustering in the target membrane, and find no evidence for preformed docking receptors.

Reverse docking: a powerful tool for drug repositioning and drug rescue.

Abstract: Reverse or inverse docking is proving to be a powerful tool for drug repositioning and drug rescue. It involves docking a small-molecule drug/ligand in the potential binding cavities of a set of clinically relevant macromolecular targets. Detailed analyses of the binding characteristics lead to ranking of the targets according to the tightness of binding. This process can potentially identify novel molecular targets for the drug/ligand which may be relevant for its mechanism of action and/or side effect profile. Another potential application of reverse docking is during the lead discovery and optimization stages of the drug-discovery cycle. This review summarizes the state-of-the-art and future prospects of the reverse docking with particular emphasis on computational molecular design.

A knowledge-based scoring function for protein-RNA interactions derived from a statistical mechanics-based iterative method

Abstract: Protein-RNA interactions play important roles in many biological processes. Given the high cost and technique difficulties in experimental methods, computationally predicting the binding complexes from individual protein and RNA structures is pressingly needed, in which a reliable scoring function is one of the critical components. Here, we have developed a knowledge-based scoring function, referred to as ITScore-PR, for protein-RNA binding mode prediction by using a statistical mechanics-based iterative method. The pairwise distance-dependent atomic interaction potentials of ITScore-PR were derived from experimentally determined protein–RNA complex structures. For validation, we have compared ITScore-PR with 10 other scoring methods on four diverse test sets. For bound docking, ITScore-PR achieved a success rate of up to 86% if the top prediction was considered and up to 94% if the top 10 predictions were considered, respectively. For truly unbound docking, the respective success rates of ITScore-PR were up to 24 and 46%. ITScore-PR can be used stand-alone or easily implemented in other docking programs for protein–RNA recognition.

Identifying novel selective non-nucleoside DNA methyltransferase 1 inhibitors through docking-based virtual screening.

Abstract: The DNA methyltransferases (DNMTs) found in mammals include DNMT1, DNMT3A, and DNMT3B and are attractive targets in cancer chemotherapy. DNMT1 was the first among the DNMTs to be characterized, and it is responsible for maintaining DNA methylation patterns. A number of DNMT inhibitors have been reported, but most of them are nucleoside analogs that can lead to toxic side effects and lack specificity. By combining docking-based virtual screening with biochemical analyses, we identified a novel compound, DC_05. DC_05 is a non-nucleoside DNMT1 inhibitor with low micromolar IC50 values and significant selectivity toward other AdoMet-dependent protein methyltransferases. Through a process of similarity-based analog searching, compounds DC_501 and DC_517 were found to be more potent than DC_05. These three potent compounds significantly inhibited cancer cell proliferation. The structure-activity relationship (SAR) and binding modes of these inhibitors were also analyzed to assist in the future development of more potent and more specific DNMT1 inhibitors.

Polypharmacology rescored: Protein–ligand interaction profiles for remote binding site similarity assessment

Abstract: Detection of remote binding site similarity in proteins plays an important role for drug repositioning and off-target effect prediction. Various non-covalent interactions such as hydrogen bonds and van-der-Waals forces drive ligands' molecular recognition by binding sites in proteins. The increasing amount of available structures of protein-small molecule complexes enabled the development of comparative approaches. Several methods have been developed to characterize and compare protein-ligand interaction patterns. Usually implemented as fingerprints, these are mainly used for post processing docking scores and (off-)target prediction. In the latter application, interaction profiles detect similarities in the bound interactions of different ligands and thus identify essential interactions between a protein and its small molecule ligands. Interaction pattern similarity correlates with binding site similarity and is thus contributing to a higher precision in binding site similarity assessment of proteins with distinct global structure. This renders it valuable for existing drug repositioning approaches in structural bioinformatics. Current methods to characterize and compare structure-based interaction patterns - both for protein-small-molecule and protein-protein interactions - as well as their potential in target prediction will be reviewed in this article. The question of how the set of interaction types, flexibility or water-mediated interactions, influence the comparison of interaction patterns will be discussed. Due to the wealth of protein-ligand structures available today, predicted targets can be ranked by comparing their ligand interaction pattern to patterns of the known target. Such knowledge-based methods offer high precision in comparison to methods comparing whole binding sites based on shape and amino acid physicochemical similarity.

Modeling, Molecular Dynamics Simulation, and Mutation Validation for Structure of Cannabinoid Receptor 2 Based on Known Crystal Structures of GPCRs

Abstract: The cannabinoid receptor 2 (CB2) plays an important role in the immune system. Although a few of GPCRs crystallographic structures have been reported, it is still challenging to obtain functional transmembrane proteins and high resolution X-ray crystal structures, such as for the CB2 receptor. In the present work, we used 10 reported crystal structures of GPCRs which had high sequence identities with CB2 to construct homology-based comparative CB2 models. We applied these 10 models to perform a prescreen by using a training set consisting of 20 CB2 active compounds and 980 compounds randomly selected from the National Cancer Institute (NCI) database. We then utilized the known 170 cannabinoid receptor 1 (CB1) or CB2 selective compounds for further validation. Based on the docking results, we selected one CB2 model (constructed by β1AR) that was most consistent with the known experimental data, revealing that the defined binding pocket in our CB2 model was well-correlated with the training and testing data studies. Importantly, we identified a potential allosteric binding pocket adjacent to the orthosteric ligand-binding site, which is similar to the reported allosteric pocket for sodium ion Na(+) in the A2AAR and the δ-opioid receptor. Our studies in correlation of our data with others suggested that sodium may reduce the binding affinities of endogenous agonists or its analogs to CB2. We performed a series of docking studies to compare the important residues in the binding pockets of CB2 with CB1, including antagonist, agonist, and our CB2 neutral compound (neutral antagonist) XIE35-1001. Then, we carried out 50 ns molecular dynamics (MD) simulations for the CB2 docked with SR144528 and CP55940, respectively. We found that the conformational changes of CB2 upon antagonist/agonist binding were congruent with recent reports of those for other GPCRs. Based on these results, we further examined one known residue, Val113(3.32), and predicted two new residues, Phe183 in ECL2 and Phe281(7.35), that were important for SR144528 and CP55940 binding to CB2. We then performed site-directed mutation experimental study for these residues and validated the predictions by radiometric binding affinity assay.

Acetylcholinesterase inhibitors with photoswitchable inhibition of β-amyloid aggregation.

Abstract: Photochromic cholinesterase inhibitors were obtained from cis-1,2-α-dithienylethene-based compounds by incorporating one or two aminopolymethylene tacrine groups. All target compounds are potent acetyl- (AChE) and butyrylcholinesterase (BChE) inhibitors in the nanomolar concentration range. Compound 11b bearing an octylene linker exhibited interactions with both the catalytic active site (CAS) and the peripheral anionic site (PAS) of AChE. Yet upon irradiation with light, the mechanism of interaction varied from one photochromic form to another, which was investigated by kinetic studies and proved "photoswitchable". The AChE-induced β-amyloid (Aβ) aggregation assay gave further experimental support to this finding: Aβ1-40 aggregation catalyzed by the PAS of AChE might be inhibited by compound 11b in a concentration-dependent manner and seems to occur only with one photochromic form. Computational docking studies provided potential binding modes of the compound. Docking studies and molecular dynamics (MD) simulations for the ring-open and -closed form indicate a difference in binding. Although both forms can interact with the PAS, more stable interactions are observed for the ring-open form based upon stabilization of a water molecule network within the enzyme, whereas the ring-closed form lacks the required conformational flexibility for an analogous binding mode. The photoswitchable inhibitor identified might serve as valuable molecular tool to investigate the different biological properties of AChE as well as its role in pathogenesis of AD in in vitro assays.

Synthesis and evaluation of analogues of N-phthaloyl-l-tryptophan (RG108) as inhibitors of DNA methyltransferase 1.

Abstract: DNA methyltransferases (DNMT) are promising drug targets in cancer provided that new, more specific, and chemically stable inhibitors are discovered. Among the non-nucleoside DNMT inhibitors, N-phthaloyl-l-tryptophan 1 (RG108) was first identified as inhibitor of DNMT1. Here, 1 analogues were synthesized to understand its interaction with DNMT. The indole, carboxylate, and phthalimide moieties were modified. Homologated and conformationally constrained analogues were prepared. The latter were synthesized from prolinohomotryptophan derivatives through a methodology based amino–zinc–ene–enolate cyclization. All compounds were tested for their ability to inhibit DNMT1 in vitro. Among them, constrained compounds 16–18 and NPys derivatives 10–11 were found to be at least 10-fold more potent than the reference compound. The cytotoxicity on the tumor DU145 cell line of the most potent inhibitors was correlated to their inhibitory potency. Finally, docking studies were conducted in order to understand their bindi...

Discovery of pyrazolopyridones as a novel class of noncovalent DprE1 inhibitor with potent anti-mycobacterial activity.

Abstract: A novel pyrazolopyridone class of inhibitors was identified from whole cell screening against Mycobacterium tuberculosis (Mtb). The series exhibits excellent bactericidality in vitro, resulting in a 4 log reduction in colony forming units following compound exposure. The significant modulation of minimum inhibitory concentration (MIC) against a Mtb strain overexpressing the Rv3790 gene suggested the target of pyrazolopyridones to be decaprenylphosphoryl-β-d-ribose-2′-epimerase (DprE1). Genetic mapping of resistance mutation coupled with potent enzyme inhibition activity confirmed the molecular target. Detailed biochemical characterization revealed the series to be a noncovalent inhibitor of DprE1. Docking studies at the active site suggest that the series can be further diversified to improve the physicochemical properties without compromising the antimycobacterial activity. The pyrazolopyridone class of inhibitors offers an attractive non-nitro lead series targeting the essential and vulnerable DprE1 enz...

A computational approach to enzyme design: predicting ω-aminotransferase catalytic activity using docking and MM-GBSA scoring.

Abstract: Enzyme design is an important area of ongoing research with a broad range of applications in protein therapeutics, biocatalysis, bioengineering, and other biomedical areas; however, significant challenges exist in the design of enzymes to catalyze specific reactions of interest. Here, we develop a computational protocol using an approach that combines molecular dynamics, docking, and MM-GBSA scoring to predict the catalytic activity of enzyme variants. Our primary focuses are to understand the molecular basis of substrate recognition and binding in an S-stereoselective ω-aminotransferase (ω-AT), which naturally catalyzes the transamination of pyruvate into alanine, and to predict mutations that enhance the catalytic efficiency of the enzyme. The conversion of (R)-ethyl 5-methyl-3-oxooctanoate to (3S,5R)-ethyl 3-amino-5-methyloctanoate in the context of several ω-AT mutants was evaluated using the computational protocol developed in this work. We correctly identify the mutations that yield the greatest imp...

A novel allosteric mechanism in the cysteine peptidase cathepsin K discovered by computational methods

Abstract: Allosteric modifiers have the potential to fine-tune enzyme activity. Therefore, targeting allosteric sites is gaining increasing recognition as a strategy in drug design. Here we report the use of computational methods for the discovery of the first small-molecule allosteric inhibitor of the collagenolytic cysteine peptidase cathepsin K, a major target for the treatment of osteoporosis. The molecule NSC13345 is identified by high-throughput docking of compound libraries to surface sites on the peptidase that are connected to the active site by an evolutionarily conserved network of residues (protein sector). The crystal structure of the complex shows that NSC13345 binds to a novel allosteric site on cathepsin K. The compound acts as a hyperbolic mixed modifier in the presence of a synthetic substrate, it completely inhibits collagen degradation and has good selectivity for cathepsin K over related enzymes. Altogether, these properties qualify our methodology and NSC13345 as promising candidates for allosteric drug design.