Quantitative structure‐activity relationships for polychlorinated hydroxybiphenyl estrogen receptor binding affinity: An Assessment of conformer flexibility

Bradbury, Steven P.; Ankley, Gerald T.; Mekenyan, Ovanes G.

doi:10.1002/etc.5620151113

Cited by 37 publications

(32 citation statements)

References 32 publications

(89 reference statements)

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“…As in previous studies [1,4,5], conformers used in this analysis were restricted to those within 20 kcalamol of the DH o f for the conformer associated with the absolute energy minimum, under the assumption that receptor binding or solvation effects could compensate conformer interconversion [11,15,16]. As noted in our previous study [1], the conformers of AR ligands within 20 kcalamol of the lowest energy structure exhibited signi®cant variation in electronic structure, which highlights the necessity of including all energetically-reasonable conformers when de®ning common reactivity patterns.…”

Section: Ar Ligands: Binding Af®nity and Electronic Structuresupporting

confidence: 58%

New developments in a hazard identification algorithm for hormone receptor ligands

Mekenyan

Nikolova

Karabunarliev

et al. 1999

Quant. Struct.-Act. Relat.

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Recently we described the Common REactivity PAttern (COREPA) technique to screen data sets of diverse structures for their ability to serve as ligands for steroid hormone receptors [1]. The approach identi®es and quanti®es similar global and local stereoelectronic characteristics associated with active ligands through a comparison of energeticallyreasonable conformer distributions for selected descriptors. For each stereoelectronic descriptor selected, discrete conformer distributions from a training set of ligands are evaluated and parameter ranges common for conformers from all the chemicals in the training set are identi®ed. The use of discrete partitions of parameter ranges to de®ne common reactivity patterns can, however, in¯uence the outcome of the algorithm. To address this limitation, the original method has been extended by approximating continuous conformer distributions as probability distributions. The COREPA-Continuous (COREPA-C) algorithm assesses the common reactivity pattern of biologicallysimilar molecules in terms of a product of probability distributions, rather than a collection of common population ranges determined by examination of discrete partitions of a distribution. To illustrate the algorithm, common reactivity patterns based on interatomic distance and charge on heteroatoms were developed and evaluated using a set of 28 androgen receptor ligands. Notable attributes of the COREPA-C algorithm include¯exibility in establishing stereoelectronic descriptor criteria for identifying active and nonactive compounds and the ability to quantify threedimensional chemical similarity without the need to predetermine a toxicophore or align compounds(s) to a lead ligand.

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Section: Ar Ligands: Binding Af®nity and Electronic Structuresupporting

confidence: 58%

New developments in a hazard identification algorithm for hormone receptor ligands

Mekenyan

Nikolova

Karabunarliev

et al. 1999

Quant. Struct.-Act. Relat.

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“…The system of choice is the ligand-binding domain (LBD) of the estrogen receptor (ER). Previous computational approaches to computing binding free energies for this system involve empirical regression-based methods (12)(13)(14), docking efforts (15), and free-energy calculations from molecular dynamics simulations (3,6,16). A great number of structurally diverse compounds have been shown to interact with the ER LBD (14,(17)(18)(19)(20)(21)(22)(23)(24).…”

mentioning

confidence: 99%

Free energies of ligand binding for structurally diverse compounds

Oostenbrink¹,

Gunsteren²

2005

Proc. Natl. Acad. Sci. U.S.A.

111

112

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The one-step perturbation approach is an efficient means to calculate many relative free energies from a common reference compound. Combining lessons learned in previous studies, an application of the method is presented that allows for the calculation of relative binding free energies for structurally rather diverse compounds from only a few simulations. Based on the well known statistical-mechanical perturbation formula, the results do not require any empirical parameters, or training sets, only limited knowledge of the binding characteristics of the ligands suffices to design appropriate reference compounds. Depending on the choice of reference compound, relative free energies of binding rigid ligands to the ligand-binding domain of the estrogen receptor can be obtained that show good agreement with the experimental values. The approach presented here can easily be applied to many rigid ligands, and it should be relatively easy to extend the method to account for ligand flexibility. The free-energy calculations can be straightforwardly parallelized, allowing for an efficient means to understand and predict relative binding free energies. free-energy calculation ͉ molecular dynamics simulation ͉ estrogen receptor ͉ one-step perturbation T he calculation of relative binding free energies for several ligands to a common receptor is of relevance for drug design purposes, and for obtaining a better understanding of the molecular interactions of proteins with small compounds. Despite the increased availability of computational power, calculating free energies from molecular dynamics simulations is still time-consuming, often requiring several extensive simulations to obtain a single relative free energy. The approach in which several free energies can be obtained from a single simulation of a, not necessarily physically meaningful, reference state (1) has been applied successfully in recent years (2-6). The idea behind the method is to simulate a judiciously chosen reference compound R, generating an ensemble of structures that contains conformations representative for several physically relevant compounds. The free-energy difference between any real ligand A and the reference compound can be calculated from the perturbation formula (7)where the angle brackets indicate the ensemble average over the structures generated in a simulation of R. H A and H R are the Hamiltonians for the real compound (A) and the reference compound (R), respectively. Because this expression only involves the difference between the two Hamiltonians, only interactions that differ between compounds A and R need to be reevaluated over the ensemble. k B is the Boltzmann constant, and T is the temperature. As was demonstrated before, one can split up the process of changing the reference compound into a real ligand into insertion of a real ligand and the removal of the reference compound (6). Assuming that the removal of the reference compound is independent of the real ligand that was added, one can estimate the free-energy difference aswith G...

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“…Instead of focussing on minimum-energy conformations of the molecules, the dynamic QSAR approach was employed [25][26][27][28] in combination with a recently introduced genetic algorithm for generating sets of conformationally most different structures [29]. After generation and AM1 optimization of initial conformers for each molecule, the selection of active conformers is based on selected 3D-structure-specific electronic or geometric parameters (e.g.…”

Section: Dynamic Qsar Methods and Molecular Descriptorsmentioning

confidence: 99%

QSAR Modeling of Antimycobacterial Activity and Activity Against Other Bacteria of 3-Formyl Rifamycin SV Derivatives

Dimov

Nedyalkova

Haladjova

et al. 2001

Quant. Struct.-Act. Relat.

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Rifamycins form a class of antibiotics with a specific potency as drug against tuberculosis via inhibition of the DNA-dependent RNA polymerase. In the present study, literature data on the antibacterial potency against Mycobacterium tuberculosis and other bacteria of 53 derivatives of 3-formyl rifamycin SV are subjected to a QSAR analysis using AM1-based quantum chemical descriptors and the recently introduced dynamic approach that allows explicit consideration of the conformational space of properly generated 3D structures. Data pre-treatment includes normalization of the minimum inhibition concentration (MIC) values of ordinary bacteria using the well-known antituberculosis drug rifampicine as reference compound (RIA), and averaging over the different strains as motivated by a mathematical analysis of the two-step inhibition process. For both this generalized potency against ordinary bacteria and the antimycobacterial activity, QSAR modelling yields 3-variable regression equations with acceptable statistics for screening purposes (r 2 around 0.60), which however differ partly in the stepwise-selected molecular descriptors and the respective conformers. While both types of activity are increased by increasing HOMO energy reflecting an increased electron donor capability and tendency to undergo hydroquinone-semiquinone-quinone oxidation, the models differ in the best 2nd and 3rd local quantum chemical descriptors, pointing to partially conflicting electronic structure requirements for optimal antimycobacterial activity and generalized activity against other bacteria. The discussion includes a detailed mechanistic analysis of the underlying bioreactivity aspects.

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Quantitative structure‐activity relationships for polychlorinated hydroxybiphenyl estrogen receptor binding affinity: An Assessment of conformer flexibility

Cited by 37 publications

References 32 publications

New developments in a hazard identification algorithm for hormone receptor ligands

New developments in a hazard identification algorithm for hormone receptor ligands

Free energies of ligand binding for structurally diverse compounds

QSAR Modeling of Antimycobacterial Activity and Activity Against Other Bacteria of 3-Formyl Rifamycin SV Derivatives

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