Showing papers on "Docking (molecular) published in 2010"

Ligand docking and binding site analysis with PyMOL and Autodock/Vina.

Abstract: Docking of small molecule compounds into the binding site of a receptor and estimating the binding affinity of the complex is an important part of the structure-based drug design process. For a thorough understanding of the structural principles that determine the strength of a protein/ligand complex both, an accurate and fast docking protocol and the ability to visualize binding geometries and interactions are mandatory. Here we present an interface between the popular molecular graphics system PyMOL and the molecular docking suites Autodock and Vina and demonstrate how the combination of docking and visualization can aid structure-based drug design efforts.

Structure of the human dopamine d3 receptor in complex with a d2/d3 selective antagonist.

Abstract: Dopamine modulates movement, cognition, and emotion through activation of dopamine G protein-coupled receptors in the brain. The crystal structure of the human dopamine D3 receptor (D3R) in complex with the small molecule D2R/D3R-specific antagonist eticlopride reveals important features of the ligand binding pocket and extracellular loops. On the intracellular side of the receptor, a locked conformation of the ionic lock and two distinctly different conformations of intracellular loop 2 are observed. Docking of R-22, a D3R-selective antagonist, reveals an extracellular extension of the eticlopride binding site that comprises a second binding pocket for the aryl amide of R-22, which differs between the highly homologous D2R and D3R. This difference provides direction to the design of D3R-selective agents for treating drug abuse and other neuropsychiatric indications.

A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking

Abstract: Motivation: Accurately predicting the binding affinities of large sets of diverse protein–ligand complexes is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for analysing the outputs of molecular docking, which in turn is an important technique for drug discovery, chemical biology and structural biology. Each scoring function assumes a predetermined theory-inspired functional form for the relationship between the variables that characterize the complex, which also include parameters fitted to experimental or simulation data and its predicted binding affinity. The inherent problem of this rigid approach is that it leads to poor predictivity for those complexes that do not conform to the modelling assumptions. Moreover, resampling strategies, such as cross-validation or bootstrapping, are still not systematically used to guard against the overfitting of calibration data in parameter estimation for scoring functions. Results: We propose a novel scoring function (RF-Score) that circumvents the need for problematic modelling assumptions via non-parametric machine learning. In particular, Random Forest was used to implicitly capture binding effects that are hard to model explicitly. RF-Score is compared with the state of the art on the demanding PDBbind benchmark. Results show that RF-Score is a very competitive scoring function. Importantly, RF-Score's performance was shown to improve dramatically with training set size and hence the future availability of more high-quality structural and interaction data is expected to lead to improved versions of RF-Score. Contact:pedro.ballester@ebi.ac.uk; jbom@st-andrews.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Advances and Challenges in Protein-Ligand Docking

Abstract: Molecular docking is a widely-used computational tool for the study of molecular recognition, which aims to predict the binding mode and binding affinity of a complex formed by two or more constituent molecules with known structures. An important type of molecular docking is protein-ligand docking because of its therapeutic applications in modern structure-based drug design. Here, we review the recent advances of protein flexibility, ligand sampling, and scoring functions—the three important aspects in protein-ligand docking. Challenges and possible future directions are discussed in the Conclusion.

Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions.

Abstract: The scoring function is one of the most important components in structure-based drug design. Despite considerable success, accurate and rapid prediction of protein–ligand interactions is still a challenge in molecular docking. In this perspective, we have reviewed three basic types of scoring functions (force-field, empirical, and knowledge-based) and the consensus scoring technique that are used for protein–ligand docking. The commonly-used assessment criteria and publicly available protein–ligand databases for performance evaluation of the scoring functions have also been presented and discussed. We end with a discussion of the challenges faced by existing scoring functions and possible future directions for developing improved scoring functions.

Conserved Binding Mode of Human β2 Adrenergic Receptor Inverse Agonists and Antagonist Revealed by X-ray Crystallography

Abstract: G protein-coupled receptors (GPCRs) represent a large fraction of current pharmaceutical targets, and of the GPCRs, the beta(2) adrenergic receptor (beta(2)AR) is one of the most extensively studied. Previously, the X-ray crystal structure of beta(2)AR has been determined in complex with two partial inverse agonists, but the global impact of additional ligands on the structure or local impacts on the binding site are not well-understood. To assess the extent of such ligand-induced conformational differences, we determined the crystal structures of a previously described engineered beta(2)AR construct in complex with two inverse agonists: ICI 118,551 (2.8 A), a recently described compound (2.8 A) (Kolb et al, 2009), and the antagonist alprenolol (3.1 A). The structures show the same overall fold observed for the previous beta(2)AR structures and demonstrate that the ligand binding site can accommodate compounds of different chemical and pharmacological properties with only minor local structural rearrangements. All three compounds contain a hydroxy-amine motif that establishes a conserved hydrogen bond network with the receptor and chemically diverse aromatic moieties that form distinct interactions with beta(2)AR. Furthermore, receptor ligand cross-docking experiments revealed that a single beta(2)AR complex can be suitable for docking of a range of antagonists and inverse agonists but also indicate that additional ligand-receptor structures may be useful to further improve performance for in-silico docking or lead-optimization in drug design.

Optimized Hydrophobic Interactions and Hydrogen Bonding at the Target-Ligand Interface Leads the Pathways of Drug-Designing

Abstract: Background Weak intermolecular interactions such as hydrogen bonding and hydrophobic interactions are key players in stabilizing energetically-favored ligands, in an open conformational environment of protein structures. However, it is still poorly understood how the binding parameters associated with these interactions facilitate a drug-lead to recognize a specific target and improve drugs efficacy. To understand this, comprehensive analysis of hydrophobic interactions, hydrogen bonding and binding affinity have been analyzed at the interface of c-Src and c-Abl kinases and 4-amino substituted 1H-pyrazolo [3, 4-d] pyrimidine compounds. Methodology In-silico docking studies were performed, using Discovery Studio software modules LigandFit, CDOCKER and ZDOCK, to investigate the role of ligand binding affinity at the hydrophobic pocket of c-Src and c-Abl kinase. Hydrophobic and hydrogen bonding interactions of docked molecules were compared using LigPlot program. Furthermore, 3D-QSAR and MFA calculations were scrutinized to quantify the role of weak interactions in binding affinity and drug efficacy. Conclusions The in-silico method has enabled us to reveal that a multi-targeted small molecule binds with low affinity to its respective targets. But its binding affinity can be altered by integrating the conformationally favored functional groups at the active site of the ligand-target interface. Docking studies of 4-amino-substituted molecules at the bioactive cascade of the c-Src and c-Abl have concluded that 3D structural folding at the protein-ligand groove is also a hallmark for molecular recognition of multi-targeted compounds and for predicting their biological activity. The results presented here demonstrate that hydrogen bonding and optimized hydrophobic interactions both stabilize the ligands at the target site, and help alter binding affinity and drug efficacy.

Evaluation of the performance of four molecular docking programs on a diverse set of protein-ligand complexes.

Abstract: Many molecular docking programs are available nowadays, and thus it is of great practical value to evaluate and compare their performance. We have conducted an extensive evaluation of four popular commercial molecular docking programs, including Glide, GOLD, LigandFit, and Surflex. Our test set consists of 195 protein-ligand complexes with high-resolution crystal structures (resolution

Are scoring functions in protein-protein docking ready to predict interactomes? Clues from a novel binding affinity benchmark

Abstract: The design of an ideal scoring function for protein-protein docking that would also predict the binding affinity of a complex is one of the challenges in structural proteomics. Such a scoring function would open the route to in silico, large-scale annotation and prediction of complete interactomes. Here we present a protein-protein binding affinity benchmark consisting of binding constants (K(d)'s) for 81 complexes. This benchmark was used to assess the performance of nine commonly used scoring algorithms along with a free-energy prediction algorithm in their ability to predicting binding affinities. Our results reveal a poor correlation between binding affinity and scores for all algorithms tested. However, the diversity and validity of the benchmark is highlighted when binding affinity data are categorized according to the methodology by which they were determined. By further classifying the complexes into low, medium and high affinity groups, significant correlations emerge, some of which are retained after dividing the data into more classes, showing the robustness of these correlations. Despite this, accurate prediction of binding affinity remains outside our reach due to the large associated standard deviations of the average score within each group. All the above-mentioned observations indicate that improvements of existing scoring functions or design of new consensus tools will be required for accurate prediction of the binding affinity of a given protein-protein complex. The benchmark developed in this work will serve as an indispensable source to reach this goal.

Docking and scoring protein interactions: CAPRI 2009.

Abstract: Protein docking algorithms are assessed by evaluating blind predictions performed during 2007-2009 in Rounds 13-19 of the community-wide experiment on critical assessment of predicted interactions (CAPRI). We evaluated the ability of these algorithms to sample docking poses and to single out specific association modes in 14 targets, representing 11 distinct protein complexes. These complexes play important biological roles in RNA maturation, G-protein signal processing, and enzyme inhibition and function. One target involved protein-RNA interactions not previously considered in CAPRI, several others were hetero-oligomers, or featured multiple interfaces between the same protein pair. For most targets, predictions started from the experimentally determined structures of the free (unbound) components, or from models built from known structures of related or similar proteins. To succeed they therefore needed to account for conformational changes and model inaccuracies. In total, 64 groups and 12 web-servers submitted docking predictions of which 4420 were evaluated. Overall our assessment reveals that 67% of the groups, more than ever before, produced acceptable models or better for at least one target, with many groups submitting multiple high- and medium-accuracy models for two to six targets. Forty-one groups including four web-servers participated in the scoring experiment with 1296 evaluated models. Scoring predictions also show signs of progress evidenced from the large proportion of correct models submitted. But singling out the best models remains a challenge, which also adversely affects the ability to correctly rank docking models. With the increased interest in translating abstract protein interaction networks into realistic models of protein assemblies, the growing CAPRI community is actively developing more efficient and reliable docking and scoring methods for everyone to use. © 2010 Wiley-Liss, Inc.

Synthesis, biological activity, and molecular modeling studies of 1H-1,2,3-triazole derivatives of carbohydrates as alpha-glucosidases inhibitors.

Abstract: A class of drugs in use for treating type II diabetes mellitus (T2D), typified by the pseudotetrasaccharide acarbose, act by inhibiting the alpha-glucosidase activity present in pancreatic secretions and in the brush border of the small intestine. Herein, we report the synthesis of a series of 4-substituted 1,2,3-triazoles conjugated with sugars, including D-xylose, D-galactose, D-allose, and D-ribose. Compounds were screened for alpha-glucosidase inhibitory activity using yeast maltase (MAL12) as a model enzyme. Methyl-2,3-O-isopropylidene-beta-D-ribofuranosides, such as the 4-(1-cyclohexenyl)-1,2,3-triazole derivative, were among the most active compounds, showing up to 25-fold higher inhibitory potency than the complex oligosaccharide acarbose. Docking studies on a MAL12 homology model disclosed a binding mode consistent with a transition-state-mimicking mechanism. Finally, the actual pharmacological potential of this triazole series was demonstrated by the reduction of postprandial blood glucose levels in normal rats. These compounds could represent new chemical scaffolds for developing novel drugs against T2D.

Complementarity between a docking and a high-throughput screen in discovering new cruzain inhibitors.

Abstract: Virtual and high-throughput screens (HTS) should have complementary strengths and weaknesses, but studies that prospectively and comprehensively compare them are rare. We undertook a parallel docking and HTS screen of 197861 compounds against cruzain, a thiol protease target for Chagas disease, looking for reversible, competitive inhibitors. On workup, 99% of the hits were eliminated as false positives, yielding 146 well-behaved, competitive ligands. These fell into five chemotypes: two were prioritized by scoring among the top 0.1% of the docking-ranked library, two were prioritized by behavior in the HTS and by clustering, and one chemotype was prioritized by both approaches. Determination of an inhibitor/cruzain crystal structure and comparison of the high-scoring docking hits to experiment illuminated the origins of docking false-negatives and false-positives. Prioritizing molecules that are both predicted by docking and are HTS-active yields well-behaved molecules, relatively unobscured by the false-...

Single-molecule pulling simulations can discern active from inactive enzyme inhibitors.

Abstract: Understanding ligand−protein recognition and interaction processes is of primary importance for structure-based drug design. Traditionally, several approaches combining docking and molecular dynamics (MD) simulations have been exploited to investigate the physicochemical properties of complexes of pharmaceutical interest. Even if the geometric properties of a modeled protein−ligand complex can be well predicted by computational methods, it is challenging to rank a series of analogues in a consistent fashion with biological data. In the unique β-hydroxyacyl-ACP dehydratase of Plasmodium falciparum (PfFabZ), the application of standard molecular docking and MD simulations was partially sufficient to shed light on the activity of previously discovered inhibitors. Complementing docking results with atomistic simulations in the steered molecular dynamics (SMD) framework, we devised an in silico approach to study molecular interactions and to compare the binding characteristics of ligand analogues. We hypothesi...

Recipes for the selection of experimental protein conformations for virtual screening.

Abstract: The use of multiple X-ray protein structures has been reported to be an efficient alternative for the representation of the binding pocket flexibility needed for accurate small molecules docking. However, the docking performance of the individual single conformations varies widely, and adding certain conformations to an ensemble is even counterproductive. Here we used a very large and diverse benchmark of 1068 X-ray protein conformations of 99 therapeutically relevant proteins, first, to compare the performance of the ensemble and single-conformation docking and, second, to find the properties of the best-performing conformers that can be used to select a smaller set of conformers for ensemble docking. The conformer selection has been validated through retrospective virtual screening experiments aimed at separating known ligand binders from decoys. We found that the conformers cocrystallized with the largest ligands displayed high selectivity for binders, and when combined in ensembles they consistently p...

DynaDock: A new molecular dynamics-based algorithm for protein-peptide docking including receptor flexibility.

Abstract: Molecular docking programs play an important role in drug development and many well-established methods exist. However, there are two situations for which the performance of most approaches is still not satisfactory, namely inclusion of receptor flexibility and docking of large, flexible ligands like peptides. In this publication a new approach is presented for docking peptides into flexible receptors. For this purpose a two step procedure was developed: first, the protein-peptide conformational space is scanned and approximate ligand poses are identified and second, the identified ligand poses are refined by a new molecular dynamics-based method, optimized potential molecular dynamics (OPMD). The OPMD approach uses soft-core potentials for the protein-peptide interactions and applies a new optimization scheme to the soft-core potential. Comparison with refinement results obtained by conventional molecular dynamics and a soft-core scaling approach shows significant improvements in the sampling capability for the OPMD method. Thus, the number of starting poses needed for successful refinement is much lower than for the other methods. The algorithm was evaluated on 15 protein-peptide complexes with 2-16mer peptides. Docking poses with peptide RMSD values <2.10 A from the equilibrated experimental structures were obtained in all cases. For four systems docking into the unbound receptor structures was performed, leading to peptide RMSD values <2.12 A. Using a specifically fitted scoring function in 11 of 15 cases the best scoring poses featured a peptide RMSD < or = 2.10 A.

Rational design of indoleamine 2,3-dioxygenase inhibitors

Abstract: Indoleamine 2,3-dioxygenase (IDO) is an important therapeutic target for the treatment of diseases such as cancer that involve pathological immune escape. We have used the evolutionary docking algorithm EADock to design new inhibitors of this enzyme. First, we investigated the modes of binding of all known IDO inhibitors. On the basis of the observed docked conformations, we developed a pharmacophore model, which was then used to devise new compounds to be tested for IDO inhibition. We also used a fragment-based approach to design and to optimize small organic molecule inhibitors. Both approaches yielded several new low-molecular weight inhibitor scaffolds, the most active being of nanomolar potency in an enzymatic assay. Cellular assays confirmed the potential biological relevance of four different scaffolds.

SwarmDock and the Use of Normal Modes in Protein-Protein Docking

Abstract: Here is presented an investigation of the use of normal modes in protein-protein docking, both in theory and in practice. Upper limits of the ability of normal modes to capture the unbound to bound conformational change are calculated on a large test set, with particular focus on the binding interface, the subset of residues from which the binding energy is calculated. Further, the SwarmDock algorithm is presented, to demonstrate that the modelling of conformational change as a linear combination of normal modes is an effective method of modelling flexibility in protein-protein docking.

Can Self-Inhibitory Peptides be Derived from the Interfaces of Globular Protein-Protein Interactions?

Abstract: In this study, we assess on a large scale the possibility of deriving self-inhibitory peptides from protein domains with globular architectures. Such inhibitory peptides would inhibit interactions of their origin domain by mimicking its mode of binding to cognate partners, and could serve as promising leads for rational design of inhibitory drugs. For our large-scale analysis, we analyzed short linear segments that were cut out of protein interfaces in silico in complex structures of protein-protein docking Benchmark 3.0 and CAPRI targets from rounds 1-19. Our results suggest that more than 50% of these globular interactions are dominated by one short linear segment at the domain interface, which provides more than half of the original interaction energy. Importantly, in many cases the derived peptides show strong energetic preference for their original binding mode independently of the context of their original domain, as we demonstrate by extensive computational peptide docking experiments. As an in depth case study, we computationally design a candidate peptide to inhibit the EphB4-EphrinB2 interaction based on a short peptide derived from the G-H loop in EphrinB2. Altogether, we provide an elaborate framework for the in silico selection of candidate inhibitory molecules for protein-protein interactions. Such candidate molecules can be readily subjected to wet-laboratory experiments and provide highly promising starting points for subsequent drug design.

SnugDock: paratope structural optimization during antibody-antigen docking compensates for errors in antibody homology models.

Abstract: High resolution structures of antibody-antigen complexes are useful for analyzing the binding interface and to make rational choices for antibody engineering. When a crystallographic structure of a complex is unavailable, the structure must be predicted using computational tools. In this work, we illustrate a novel approach, named SnugDock, to predict high-resolution antibody-antigen complex structures by simultaneously structurally optimizing the antibody-antigen rigid-body positions, the relative orientation of the antibody light and heavy chains, and the conformations of the six complementarity determining region loops. This approach is especially useful when the crystal structure of the antibody is not available, requiring allowances for inaccuracies in an antibody homology model which would otherwise frustrate rigid-backbone docking predictions. Local docking using SnugDock with the lowest-energy RosettaAntibody homology model produced more accurate predictions than standard rigid-body docking. SnugDock can be combined with ensemble docking to mimic conformer selection and induced fit resulting in increased sampling of diverse antibody conformations. The combined algorithm produced four medium (Critical Assessment of PRediction of Interactions-CAPRI rating) and seven acceptable lowest-interface-energy predictions in a test set of fifteen complexes. Structural analysis shows that diverse paratope conformations are sampled, but docked paratope backbones are not necessarily closer to the crystal structure conformations than the starting homology models. The accuracy of SnugDock predictions suggests a new genre of general docking algorithms with flexible binding interfaces targeted towards making homology models useful for further high-resolution predictions.

Structure-based drug metabolism predictions for drug design.

Abstract: Significant progress has been made in structure-based drug design by pharmaceutical companies at different stages of drug discovery such as identifying new hits, enhancing molecule binding affinity in hit-to-lead, and reducing toxicities in lead optimization. Drug metabolism is a major consideration for modifying drug clearance and also a primary source for drug metabolite-induced toxicity. With major cytochrome P450 structures identified and characterized recently, structure-based drug metabolism prediction becomes increasingly attractive. In silico methods based on molecular and quantum mechanics such as docking, molecular dynamics and ab initio chemical reactivity calculations bring us closer to understand drug metabolism and predict drug–drug interactions. In this study, we review important progress in drug metabolism and common in silico techniques adopted to predict drug regioselectivity, stereoselectivity, reactive metabolites, induction, inhibition and mechanism-based inactivation, as well as their implementation in hit-to-lead drug discovery.

Conformational transitions upon ligand binding: holo-structure prediction from apo conformations.

Abstract: Biological function of proteins is frequently associated with the formation of complexes with small-molecule ligands. Experimental structure determination of such complexes at atomic resolution, however, can be time-consuming and costly. Computational methods for structure prediction of protein/ligand complexes, particularly docking, are as yet restricted by their limited consideration of receptor flexibility, rendering them not applicable for predicting protein/ligand complexes if large conformational changes of the receptor upon ligand binding are involved. Accurate receptor models in the ligand-bound state (holo structures), however, are a prerequisite for successful structure-based drug design. Hence, if only an unbound (apo) structure is available distinct from the ligand-bound conformation, structure-based drug design is severely limited. We present a method to predict the structure of protein/ligand complexes based solely on the apo structure, the ligand and the radius of gyration of the holo structure. The method is applied to ten cases in which proteins undergo structural rearrangements of up to 7.1 A backbone RMSD upon ligand binding. In all cases, receptor models within 1.6 A backbone RMSD to the target were predicted and close-to-native ligand binding poses were obtained for 8 of 10 cases in the top-ranked complex models. A protocol is presented that is expected to enable structure modeling of protein/ligand complexes and structure-based drug design for cases where crystal structures of ligand-bound conformations are not available.