Showing papers on "Docking (molecular) published in 2006"

MolDock: a new technique for high-accuracy molecular docking.

Abstract: In this article we introduce a molecular docking algorithm called MolDock. MolDock is based on a new heuristic search algorithm that combines differential evolution with a cavity prediction algorithm. The docking scoring function of MolDock is an extension of the piecewise linear potential (PLP) including new hydrogen bonding and electrostatic terms. To further improve docking accuracy, a re-ranking scoring function is introduced, which identifies the most promising docking solution from the solutions obtained by the docking algorithm. The docking accuracy of MolDock has been evaluated by docking flexible ligands to 77 protein targets. MolDock was able to identify the correct binding mode of 87% of the complexes. In comparison, the accuracy of Glide and Surflex is 82% and 75%, respectively. FlexX obtained 58% and GOLD 78% on subsets containing 76 and 55 cases, respectively.

Novel procedure for modeling ligand/receptor induced fit effects.

Abstract: We present a novel protein-ligand docking method that accurately accounts for both ligand and receptor flexibility by iteratively combining rigid receptor docking (Glide) with protein structure prediction (Prime) techniques. While traditional rigid-receptor docking methods are useful when the receptor structure does not change substantially upon ligand binding, success is limited when the protein must be "induced" into the correct binding conformation for a given ligand. We provide an in-depth description of our novel methodology and present results for 21 pharmaceutically relevant examples. Traditional rigid-receptor docking for these 21 cases yields an average RMSD of 5.5 A. The average ligand RMSD for docking to a flexible receptor for the 21 pairs is 1.4 A; the RMSD is < or =1.8 A for 18 of the cases. For the three cases with RMSDs greater than 1.8 A, the core of the ligand is properly docked and all key protein/ligand interactions are captured.

A critical assessment of docking programs and scoring functions

Abstract: Docking is a computational technique that samples conformations of small molecules in protein binding sites; scoring functions are used to assess which of these conformations best complements the protein binding site. An evaluation of 10 docking programs and 37 scoring functions was conducted against eight proteins of seven protein types for three tasks: binding mode prediction, virtual screening for lead identification, and rank-ordering by affinity for lead optimization. All of the docking programs were able to generate ligand conformations similar to crystallographically determined protein/ligand complex structures for at least one of the targets. However, scoring functions were less successful at distinguishing the crystallographic conformation from the set of docked poses. Docking programs identified active compounds from a pharmaceutically relevant pool of decoy compounds; however, no single program performed well for all of the targets. For prediction of compound affinity, none of the docking programs or scoring functions made a useful prediction of ligand binding affinity.

Protein-ligand docking: current status and future challenges.

Abstract: Understanding the ruling principles whereby protein receptors recognize, interact, and associate with molecular substrates and inhibitors is of paramount importance in drug discovery efforts. Protein-ligand docking aims to predict and rank the structure(s) arising from the association between a given ligand and a target protein of known 3D structure. Despite the breathtaking advances in the field over the last decades and the widespread application of docking methods, several downsides still exist. In particular, protein flexibility-a critical aspect for a thorough understanding of the principles that guide ligand binding in proteins-is a major hurdle in current protein-ligand docking efforts that needs to be more efficiently accounted for. In this review the key concepts of protein-ligand docking methods are outlined, with major emphasis being given to the general strengths and weaknesses that presently characterize this methodology. Despite the size of the field, the principal types of search algorithms and scoring functions are reviewed and the most popular docking tools are briefly depicted. Recent advances that aim to address some of the traditional limitations associated with molecular docking are also described. A selection of hand-picked examples is used to illustrate these features.

Prediction of protein-ligand interactions. Docking and scoring: successes and gaps.

Abstract: Computational methods have become standard in today’s medicinal chemistry tool kit. Like any tool, it is important to periodically evaluate utility and ask how function can be improved. In this section of the Journal, we call attention to the area of calculating molecular interactions, specifically docking, the positioning of a ligand in a protein binding site, and scoring, the quality assessment of docked ligands. As several recent reviews have made clear, 1-3 the technology has been productive for both finding and elaborating bioactive molecules. But has docking and scoring delivered on the promises first made over 20 years ago? To consider that question, we follow up on an extensive symposium held in Philadelphia during the 2004 Fall National Meeting of the American Chemistry Society and on subsequent meetings sponsored by the National Institutes of Health (NIH) and the National Institute of Standards and Technology (NIST) in 2005 and 2006 to address the outcomes of the American Chemical Society symposium. Speakers at the symposium were invited to contribute original manuscripts to be published with this overview to highlight the area of docking and scoring and to identify some of the major gaps yet to be addressed. 4-10

Rosettaligand : Protein-small molecule docking with full side-chain flexibility

Abstract: Protein-small molecule docking algorithms provide a means to model the structure of protein-small molecule complexes in structural detail and play an important role in drug development. In recent years the necessity of simulating protein side-chain flexibility for an accurate prediction of the protein-small molecule interfaces has become apparent, and an increasing number of docking algorithms probe different approaches to include protein flexibility. Here we describe a new method for docking small molecules into protein binding sites employing a Monte Carlo minimization procedure in which the rigid body position and orientation of the small molecule and the protein side-chain conformations are optimized simultaneously. The energy function comprises van der Waals (VDW) interactions, an implicit solvation model, an explicit orientation hydrogen bonding potential, and an electrostatics model. In an evaluation of the scoring function the computed energy correlated with experimental small molecule binding energy with a correlation coefficient of 0.63 across a diverse set of 229 protein- small molecule complexes. The docking method produced lowest energy models with a root mean square deviation (RMSD) smaller than 2 A in 71 out of 100 protein-small molecule crystal structure complexes (self-docking). In cross-docking calculations in which both protein side-chain and small molecule internal degrees of freedom were varied the lowest energy predictions had RMSDs less than 2 A in 14 of 20 test cases.

TarFisDock: a web server for identifying drug targets with docking approach

Abstract: TarFisDock is a web-based tool for automating the procedure of searching for small molecule-protein interactions over a large repertoire of protein structures. It offers PDTD (potential drug target database), a target database containing 698 protein structures covering 15 therapeutic areas and a reverse ligand-protein docking program. In contrast to conventional ligand-protein docking, reverse ligand-protein docking aims to seek potential protein targets by screening an appropriate protein database. The input file of this web server is the small molecule to be tested, in standard mol2 format; TarFisDock then searches for possible binding proteins for the given small molecule by use of a docking approach. The ligand-protein interaction energy terms of the program DOCK are adopted for ranking the proteins. To test the reliability of the TarFisDock server, we searched the PDTD for putative binding proteins for vitamin E and 4H-tamoxifen. The top 2 and 10% candidates of vitamin E binding proteins identified by TarFisDock respectively cover 30 and 50% of reported targets verified or implicated by experiments; and 30 and 50% of experimentally confirmed targets for 4H-tamoxifen appear amongst the top 2 and 5% of the TarFisDock predicted candidates, respectively. Therefore, TarFisDock may be a useful tool for target identification, mechanism study of old drugs and probes discovered from natural products. TarFisDock and PDTD are available at http://www.dddc.ac.cn/tarfisdock/.

Ensemble docking of multiple protein structures: Considering protein structural variations in molecular docking

Abstract: One approach to incorporate protein flexibility in molecular docking is the use of an ensemble consisting of multiple protein structures. Sequentially docking each ligand into a large number of protein structures is computationally too expensive to allow large-scale database screening. It is challenging to achieve a good balance between docking accuracy and computational efficiency. In this work, we have developed a fast, novel docking algorithm utilizing multiple protein structures, referred to as ensemble docking, to account for protein structural variations. The algorithm can simultaneously dock a ligand into an ensemble of protein structures and automatically select an optimal protein structure that best fits the ligand by optimizing both ligand coordinates and the conformational variable m, where m represents the m-th structure in the protein ensemble. The docking algorithm was validated on 10 protein ensembles containing 105 crystal structures and 87 ligands in terms of binding mode and energy score predictions. A success rate of 93% was obtained with the criterion of root-mean-square deviation <2.5 A if the top five orientations for each ligand were considered, comparable to that of sequential docking in which scores for individual docking are merged into one list by re-ranking, and significantly better than that of single rigid-receptor docking (75% on average). Similar trends were also observed in binding score predictions and enrichment tests of virtual database screening. The ensemble docking algorithm is computationally efficient, with a computational time comparable to that for docking a ligand into a single protein structure. In contrast, the computational time for the sequential docking method increases linearly with the number of protein structures in the ensemble. The algorithm was further evaluated using a more realistic ensemble in which the corresponding bound protein structures of inhibitors were excluded. The results show that ensemble docking successfully predicts the binding modes of the inhibitors, and discriminates the inhibitors from a set of noninhibitors with similar chemical properties. Although multiple experimental structures were used in the present work, our algorithm can be easily applied to multiple protein conformations generated by computational methods, and helps improve the efficiency of other existing multiple protein structure(MPS)-based methods to accommodate protein flexibility.

Contribution of Conformer Focusing to the Uncertainty in Predicting Free Energies for Protein−Ligand Binding

Abstract: When a ligand binds to a protein, it is typically not in the lowest-energy conformation for the unbound ligand and there is also a loss of conformational degrees of freedom. The free-energy change for this “conformer focusing” is addressed here formally, and the associated errors with its estimation or neglect are considered in the context of scoring functions for protein−ligand docking and computation of absolute free energies of binding. Specific applications for inhibition of HIV-1 reverse transcriptase are reported. It is concluded that the uncertainties from this source alone are sufficient to preclude the viability of current docking methodology for rank-ordering of diverse compounds in high-throughput virtual screening.

Stably Tethered Structures of Defined Compositions with Multiple Functions or Binding Specificities

Abstract: The present invention concerns methods and compositions for stably tethered structures of defined compositions with multiple functionalities and/or binding specificities. Particular embodiments concern stably tethered structures comprising a homodimer of a first monomer, comprising a dimerization and docking domain attached to a first precursor, and a second monomer comprising an anchoring domain attached to a second precursor. The first and second precursors may be virtually any molecule or structure, such as antibodies, antibody fragments, antibody analogs or mimetics, aptamers, binding peptides, fragments of binding proteins, known ligands for proteins or other molecules, enzymes, detectable labels or tags, therapeutic agents, toxins, pharmaceuticals, cytokines, interleukins, interferons, radioisotopes, proteins, peptides, peptide mimetics, polynucleotides, RNAi, oligosaccharides, natural or synthetic polymeric substances, nanoparticles, quantum dots, organic or inorganic compounds, etc. The disclosed methods and compositions provide a simple, easy to purify way to obtain any binary compound attached to any monomeric compound, or any trinary compound.

Molecular interaction of serotonin 5-HT2A receptor residues Phe339(6.51) and Phe340(6.52) with superpotent N-benzyl phenethylamine agonists.

Abstract: Experiments were conducted to examine the molecular basis for the high affinity and potency of a new class of 5-HT(2A) receptor agonists, N-benzyl phenethylamines. Competition binding assays at several serotonin receptors confirmed that an N-arylmethyl substitution was necessary for affinity increases up to 300-fold over simple N-alkyl homologs, as well as enhanced selectivity for 5-HT(2A) versus 5-HT(2C) and 5-HT(1A) receptors. PI hydrolysis functional assays confirmed that these N-benzyl phenethylamines are potent and highly efficacious agonists at the rat 5-HT(2A) receptor. Virtual docking of these compounds into a human 5-HT(2A) receptor homology model indicated that the N-benzyl moiety might be interacting with Phe339((6.51)), whereas the phenethylamine portion was likely to be interacting with Phe340((6.52)). Experiments in h5-HT(2A) receptors with Phe339((6.51))L and Phe340((6.52))L mutations seem to support this hypothesis. Dramatic detrimental effects on affinity, potency, and intrinsic activity were observed with the Phe339((6.51))L mutation for all N-benzyl analogs, whereas most N-unsubstituted phenethylamines and traditional agonists were only weakly affected, if at all. Consistent with other published studies, the Phe340((6.52))L mutation detrimentally affected affinity, potency, and intrinsic activity of nearly all compounds tested, although a strong change in intrinsic activity was not seen with most N-aryl analogs. These data further validate the topology of our h5-HT(2A) receptor homology model. It is noteworthy that this study is the first to identify a hitherto unrecognized role for residue 6.51 in agonist activation of a serotonin G protein-coupled receptor (GPCR), whereas most previous reports have suggested a varied and sometimes contradictory role in homologous GPCRs.

Information-driven protein–DNA docking using HADDOCK: it is a matter of flexibility

Abstract: Intrinsic flexibility of DNA has hampered the devel- opment of efficient proteinDNA docking methods. In this study we extend HADDOCK (High Ambiguity Driven DOCKing) (C. Dominguez, R. Boelens and A. M. J. J. Bonvin (2003) J. Am. Chem. Soc. 125, 1731-1737) to explicitly deal with DNA flexibility. HADDOCK uses non-structural experimental data to drive the docking during a rigid-body energy mini- mization, and semi-flexible and water refinement stages. The latter allow for flexibility of all DNA nucleotides and the residues of the protein at the predicted interface. We evaluated our approach on the monomeric repressorDNA complexes formed by bacteriophage 434 Cro, the Escherichia coli Lac headpiece and bacteriophage P22 Arc. Starting from unbound proteins and canonical B-DNA we cor- rectly predict the correct spatial disposition of the complexes and the specific conformation of the DNA in the published complexes. This information is subsequently used to generate a library of pre- bent and twisted DNA structures that served as input for a second docking round. The resulting top ranking solutions exhibit high similarity to the published complexes in terms of root mean square deviations, intermolecular contacts and DNA con- formation. Our two-stage docking method is thus able to successfully predict proteinDNA com- plexes from unbound constituents using non- structural experimental data to drive the docking.

Methods for the prediction of protein-ligand binding sites for structure-based drug design and virtual ligand screening.

Abstract: Structure Based Drug Design (SBDD) is a computational approach to lead discovery that uses the three-dimensional structure of a protein to fit drug-like molecules into a ligand binding site to modulate function. Identifying the location of the binding site is therefore a vital first step in this process, restricting the search space for SBDD or virtual screening studies. The detection and characterisation of functional sites on proteins has increasingly become an area of interest. Structural genomics projects are increasingly yielding protein structures with unknown functions and binding sites. Binding site prediction was pioneered by pocket detection, since the binding site is often found in the largest pocket. More recent methods involve phylogenetic analysis, identifying structural similarity with proteins of known function and identifying regions on the protein surface with a potential for high binding affinity. Binding site prediction has been used in several SBDD projects and has been incorporated into several docking tools. We discuss different methods of ligand binding site prediction, their strengths and weaknesses, and how they have been used in SBDD.

Inhibition of protein–protein interactions: The discovery of druglike β-catenin inhibitors by combining virtual and biophysical screening

Abstract: The interaction between beta-catenin and Tcf family members is crucial for the Wnt signal transduction pathway, which is commonly mutated in cancer. This interaction extends over a very large surface area (4800 A(2)), and inhibiting such interactions using low molecular weight inhibitors is a challenge. However, protein surfaces frequently contain "hot spots," small patches that are the main mediators of binding affinity. By making tight interactions with a hot spot, a small molecule can compete with a protein. The Tcf3/Tcf4-binding surface on beta-catenin contains a well-defined hot spot around residues K435 and R469. A 17,700 compounds subset of the Pharmacia corporate collection was docked to this hot spot with the QXP program; 22 of the best scoring compounds were put into a biophysical (NMR and ITC) screening funnel, where specific binding to beta-catenin, competition with Tcf4 and finally binding constants were determined. This process led to the discovery of three druglike, low molecular weight Tcf4-competitive compounds with the tightest binder having a K(D) of 450 nM. Our approach can be used in several situations (e.g., when selecting compounds from external collections, when no biochemical functional assay is available, or when no HTS is envisioned), and it may be generally applicable to the identification of inhibitors of protein-protein interactions.

Targeting protein-protein interactions with small molecules: challenges and perspectives for computational binding epitope detection and ligand finding

Abstract: A promising way to interfere with biological processes is through the control of protein-protein interactions by means of small molecules that modulate the formation of protein-protein complexes Although the feasibility of this approach has been demonstrated in principle by recent results, many of the small-molecule modulators known to date have not been found by rational design approaches In large part this is due to the challenges that one faces in dealing with protein binding epitopes compared to, eg, enzyme binding pockets Recent advances in the understanding of the energetics and dynamics of protein binding interfaces and methodological developments in the field of structure-based drug design methods may open up a way to apply rational design approaches also for finding protein-protein interaction modulators These advances and developments include (I) computational approaches to dissect binding interfaces in terms of energetic contributions of single residues (to identify "hot spot" residues), (II) prediction of potential binding sites from unbound protein structures, (III) recognition of allosteric binding sites as alternatives to directly targeting interfaces, (IV) docking approaches that consider protein flexibility and improved descriptions of the solvent influence on electrostatic interactions, and (V) data-driven docking approaches Here, we will summarize these developments with a particular emphasis on their applicability to screen for or design small-molecule modulators of protein-protein interactions

Structure-based optimization of azole antifungal agents by CoMFA, CoMSIA, and molecular docking

Abstract: In a continuing effort to develop highly potent azole antifungal agents, the three-dimensional quantitative structure-activity relationship methods, CoMFA and CoMSIA, were applied using a set of novel azole antifungal compounds. The binding mode of the compounds at the active site of lanosterol 14alpha-demethylase was further explored using the flexible docking method. Various hydrophobic, van der Waals, pi-pi stacking, and hydrogen bonding interactions were observed between the azoles and the enzyme. Based on results from the molecular modeling, a receptor-based pharmacophore model was established to guide the rational optimization of the azole antifungal agents. Thus, a total of 57 novel azoles were designed and synthesized by a three-step optimization process. In vitro antifungal assay revealed that the antifungal activities of these novel azoles were greatly improved, which confirmed the reliability of the model from molecular modeling.

An iterative knowledge-based scoring function to predict protein-ligand interactions: II. Validation of the scoring function.

Abstract: We have developed an iterative knowledge-based scoring function (ITScore) to describe protein-ligand inter- actions. Here, we assess ITScore through extensive tests on native structure identification, binding affinity prediction, and virtual database screening. Specifically, ITScore was first applied to a test set of 100 protein-ligand complexes constructed by Wang et al. (J Med Chem 2003, 46, 2287), and compared with 14 other scoring functions. The results show that ITScore yielded a high success rate of 82% on identifying native-like binding modes under the criterion of rmsd ≤2 A for each top-ranked ligand conformation. The success rate increased to 98% if the top five confor- mations were considered for each ligand. In the case of binding affinity prediction, ITScore also obtained a good correlation for this test set (R = 0.65). Next, ITScore was used to predict binding affinities of a second diverse test set of 77 protein-ligand complexes prepared by Muegge and Martin (J Med Chem 1999, 42, 791), and compared with four other widely used knowledge-based scoring functions. ITScore yielded a high correlation of R 2 = 0.65 (or R = 0.81) in the affinity prediction. Finally, enrichment tests were performed with ITScore against four target proteins using the compound databases constructed by Jacobsson et al. (J Med Chem 2003, 46, 5781). The results were compared with those of eight other scoring functions. ITScore yielded high enrichments in all four database screen- ing tests. ITScore can be easily combined with the existing docking programs for the use of structure-based drug design.

eHiTS: an innovative approach to the docking and scoring function problems.

Abstract: Virtual Ligand Screening (VLS) has become an integral part of the drug design process for many pharmaceutical companies. In protein structure based VLS the aim is to find a ligand that has a high binding affinity to the target receptor whose 3D structure is known. This review will describe the docking tool eHiTS. eHiTS is an exhaustive and systematic docking tool which contains many automated features that simplify the drug design workflow. A description of the unique docking algorithm and novel approach to scoring used within eHiTS is presented. In addition a validation study is presented that demonstrates the accuracy and wide applicability of eHiTS in re-docking bound ligands into their receptors.

Parameter estimation for scoring protein-ligand interactions using negative training data.

Abstract: Surflex-Dock employs an empirically derived scoring function to rank putative protein-ligand interactions by flexible docking of small molecules to proteins of known structure. The scoring function employed by Surflex was developed purely on the basis of positive data, comprising noncovalent protein-ligand complexes with known binding affinities. Consequently, scoring function terms for improper interactions received little weight in parameter estimation, and an ad hoc scheme for avoiding protein-ligand interpenetration was adopted. We present a generalized method for incorporating synthetically generated negative training data, which allows for rigorous estimation of all scoring function parameters. Geometric docking accuracy remained excellent under the new parametrization. In addition, a test of screening utility covering a diverse set of 29 proteins and corresponding ligand sets showed improved performance. Maximal enrichment of true ligands over nonligands exceeded 20-fold in over 80% of cases, with enrichment of greater than 100-fold in over 50% of cases.

Discovery of two novel, small-molecule inhibitors of DNA methylation

Abstract: DNA methyltransferases are promising targets for cancer therapy. In many cancer cells promoters of tumor suppressor genes are hypermethylated, which results in gene inactivation. It has been shown that DNA methyltransferase inhibitors can suppress tumor growth and have significant therapeutic value. However, the established inhibitors are limited in their application due to their substantial cytotoxicity. To discover novel compounds for the inhibition of human DNA methyltransferases, we have screened a set of small molecules available from the NCI database. Using a 3-dimensional model of the human DNA methyltransferase 1 and a modified docking and scoring procedure, we have identified a small list of molecules with high affinities for the active site of the enzyme. The two highest scoring structures were found to inhibit DNA methyltransferase activity in vitro and in vivo. The newly discovered inhibitors validate our screening procedure and also provide a useful basis for further rational drug development.

Catalytic site prediction and virtual screening of cytochrome P450 2D6 substrates by consideration of water and rescoring in automated docking.

Abstract: Automated docking strategies successfully applied to binding mode predictions of ligands in Cyt P450 crystal structures in an earlier study (de Graaf et al. J. Med. Chem. 2005, 7, 2308-2318) were used for the catalytic site prediction (CSP) of 65 substrates in a CYP2D6 homology model. The consideration of water molecules at predicted positions in the active site and the rescoring of pooled docking poses from four different docking programs (AutoDock, FlexX, GOLD-Goldscore, and GOLD-Chemscore) with the SCORE scoring function enabled the successful prediction of experimentally reported sites of catalysis of more than 80% of the substrates. Three docking algorithms (FlexX, GOLD-Goldscore, and GOLD-Chemscore) were subsequently used in combination with six scoring functions (Chemscore, DOCK, FlexX, GOLD, PMF, and SCORE) to assess the ability of docking-based virtual screening methods to prioritize known CYP2D6 substrates seeded into a drug-like chemical database (in the absence and presence of active-site water molecules). Finally, the optimal docking strategy in terms of virtual screening accuracy, GOLD-Chemscore with the consideration of active-site water (60% of known substrates recovered in the top 5% of the ranked drug-like database), was verified experimentally; it was successfully used to identify high-affinity CYP2D6 ligands among a larger proprietary database.

Structure-Based Drug Design and Structural Biology Study of Novel Nonpeptide Inhibitors of Severe Acute Respiratory Syndrome Coronavirus Main Protease

Abstract: Severe acute respiratory syndrome coronavirus (SARS-CoV) main protease (M(pro)), a protein required for the maturation of SARS-CoV, is vital for its life cycle, making it an attractive target for structure-based drug design of anti-SARS drugs. The structure-based virtual screening of a chemical database containing 58,855 compounds followed by the testing of potential compounds for SARS-CoV M(pro) inhibition leads to two hit compounds. The core structures of these two hits, defined by the docking study, are used for further analogue search. Twenty-one analogues derived from these two hits exhibited IC50 values below 50 microM, with the most potent one showing 0.3 microM. Furthermore, the complex structures of two potent inhibitors with SARS-CoV M(pro) were solved by X-ray crystallography. They bind to the protein in a distinct manner compared to all published SARS-CoV M(pro) complex structures. They inhibit SARS-CoV M(pro) activity via intensive H-bond network and hydrophobic interactions, without the formation of a covalent bond. Interestingly, the most potent inhibitor induces protein conformational changes, and the inhibition mechanisms, particularly the disruption of catalytic dyad (His41 and Cys145), are elaborated.