A fast and efficient method to generate biologically relevant conformations

Klebe, G.; Mietzner, Thomas

doi:10.1007/bf00123667

Cited by 164 publications

(125 citation statements)

References 45 publications

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“…b Total number of complexes with the specified number of rotatable bonds in test set 1. result. The extensive MINUMBA 45 conformer library used by FlexX to explore conformational space during the incremental build-up procedure may better represent the sulfonamide group. This failure is not inherent in the MD sampling method of CDOCKER, but rather, these results point to a systematic error in the underlying force field for this particular group.…”

Section: Discussionmentioning

confidence: 99%

“…Interactions take place for complimentary matches between protein and ligand sites. Ligand flexibility is treated by an incremental build-up method that uses the MINUMBA 45 conformer library to grow ligands during the docking process. Here the FlexX method as implemented in SYBYL 27 was applied to test set 1 using most of the default parameters.…”

Section: Methodsmentioning

confidence: 99%

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Lessons in Molecular Recognition: The Effects of Ligand and Protein Flexibility on Molecular Docking Accuracy

et al. 2003

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The key to success for computational tools used in structure-based drug design is the ability to accurately place or "dock" a ligand in the binding pocket of the target of interest. In this report we examine the effect of several factors on docking accuracy, including ligand and protein flexibility. To examine ligand flexibility in an unbiased fashion, a test set of 41 ligand-protein cocomplex X-ray structures were assembled that represent a diversity of size, flexibility, and polarity with respect to the ligands. Four docking algorithms, DOCK, FlexX, GOLD, and CDOCKER, were applied to the test set, and the results were examined in terms of the ability to reproduce X-ray ligand positions within 2.0Å heavy atom root-mean-square deviation. Overall, each method performed well (>50% accuracy) but for all methods it was found that docking accuracy decreased substantially for ligands with eight or more rotatable bonds. Only CDOCKER was able to accurately dock most of those ligands with eight or more rotatable bonds (71% accuracy rate). A second test set of structures was gathered to examine how protein flexibility influences docking accuracy. CDOCKER was applied to X-ray structures of trypsin, thrombin, and HIV-1-protease, using protein structures bound to several ligands and also the unbound (apo) form. Docking experiments of each ligand to one "average" structure and to the apo form were carried out, and the results were compared to docking each ligand back to its originating structure. The results show that docking accuracy falls off dramatically if one uses an average or apo structure. In fact, it is shown that the drop in docking accuracy mirrors the degree to which the protein moves upon ligand binding.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Lessons in Molecular Recognition: The Effects of Ligand and Protein Flexibility on Molecular Docking Accuracy

et al. 2003

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“…One such study of 80 protein-ligand complexes revealed similar torsion angle distributions for fragments when the bound and unbound distributions were compared. 14,15 Several conformer generation programs employ empirically derived torsion libraries to help ensure that only energetically favorable conformations are produced. The transferability of CSD torsion angle distributions to biologically active ligand conformations is borne out in the success of these programs.…”

Section: Relevance Of Small Molecule Crystal Structure Conformations mentioning

confidence: 99%

Small Molecule Conformational Preferences Derived from Crystal Structure Data. A Medicinal Chemistry Focused Analysis

Brameld

Kuhn

Reuter

et al. 2008

J. Chem. Inf. Model.

331

332

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Based on torsion angle distributions of frequently occurring substructures, conformation preferences of druglike molecules are presented, accompanied by a review of the relevant literature. First, the relevance of the Cambridge Structural Database (CSD) for drug design is demonstrated by comparing substructures present in compounds entering clinical trials with those found in the CSD and protein-bound ligands in the Protein Data Bank (PDB). Next, we briefly highlight preferred conformations of elementary acyclic systems, followed by a discussion of sulfonamide conformations. Due to their central role in medicinal chemistry, we discuss properties of aryl ring substituents in depth, including biaryl systems and systems of two aryl rings connected by two acyclic bonds. For a subset of torsion motifs, we also compare torsion angle histograms derived from CSD structures with those derived from ligands in the PDB. Furthermore, selected properties of some six-and seven-membered ring systems are discussed. The article closes with a section on attractive sulfuroxygen contacts.

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“…A conformational analyzer with molecular dynamics and sampling (CAMDAS) is one example of a program to derive compound conformations [31]. At present 3D-QSAR models could be generated by such programs as Catalyst (Accelrys, Inc., San Diego, USA), comparative molecular fi eld analysis (CoMFA) [32], and comparative molecular similarity index analysis (CoMSIA) [33]. These programs generate a hypothetical pharmacophore as a set of descriptors distributed within a 3D space.…”

Section: Ligand-based Prediction System For the Blockade Of Herg Currentmentioning

confidence: 99%

In Silico Prediction of the Chemical Block of Human Ether-a-Go-Go-Related Gene (hERG) K+ Current

et al. 2008

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A variety of compounds with different chemical properties directly interact with the cardiac repolarizing K + channel encoded by the human ether-a-go-go-related gene (hERG). This causes acquired forms of QT prolongation, which can result in lethal cardiac arrhythmias, including torsades de pointes one of the most serious adverse effects of various therapeutic agents. Prediction of this phenomenon will improve the safety of pharmacological therapy and also facilitate the process of drug development. Here we propose a strategy for the development of an in silico system to predict the potency of chemical compounds to block hERG. The system consists of two sequential processes. The first process is a ligand-based prediction to estimate half-maximal concentrations for the block of compounds inhibiting hERG current using the relationship between chemical features and activities of compounds. The second process is a protein-based prediction that comprises homology modeling of hERG, docking simulation of chemical-channel interaction, analysis of the shape of the channel pore cavity, and Brownian dynamics simulation to estimate hERG currents in the presence and absence of chemical blockers. Since each process is a combination of various calculations, the criterion for assessment at each calculation and the strategy to integrate these steps are significant for the construction of the system to predict a chemical's block of hERG current and also to predict the risk of inducing cardiac arrhythmias from the chemical information. The principles and criteria of elemental computations along this strategy are described.Key words: hERG, quantitative structure-activity relationship, molecular docking, Brownian dynamics simulation.Many ion channels and ion transporting systems contribute to the generation of the action potential of the heart. The ventricular action potential possesses a longlasting plateau that is terminated by the activation of repolarizing K + currents, I Kr and I Ks . The disturbance of these currents causes prolongation of the action potential duration and thus the QT interval of the electrocardiogram, and in many cases a worsening of transmural dispersion of repolarization [1][2][3]. These are the substrates for generation of life-threatening cardiac arrhythmias, including torsades de pointes. Many compounds inhibit I Kr , whose pore-forming subunit is the human ether-a-gogo-related gene (hERG) product. This can cause acquired forms of long QT syndrome [4][5][6]. The compounds exhibit a broad spectrum, including not only cardiac ion channel blockers, but also antipsychotic agents, antidepressant agents, antimicrobial agents, phosphodiesterase inhibitors, topoisomerase II inhibitors, and antagonists for G protein-coupled receptors, such as nonsedating antihistamine H 1 receptor blockers and β blockers [6]. The development of methods that could predict this phenomenon would improve the safety of pharmacological therapy and also facilitate the process of drug development.Here we propose an in silico str...

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A fast and efficient method to generate biologically relevant conformations

Cited by 164 publications

References 45 publications

Lessons in Molecular Recognition: The Effects of Ligand and Protein Flexibility on Molecular Docking Accuracy

Lessons in Molecular Recognition: The Effects of Ligand and Protein Flexibility on Molecular Docking Accuracy

Small Molecule Conformational Preferences Derived from Crystal Structure Data. A Medicinal Chemistry Focused Analysis

In Silico Prediction of the Chemical Block of Human Ether-a-Go-Go-Related Gene (hERG) K+ Current

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