Models of F·H Contacts Relevant to the Binding of Fluoroaromatic Inhibitors to Carbonic Anhydrase II

DerHovanessian, Ariss; Doyon, Jeffrey B.; Jain, Ahamindra; Rablen, Paul R.; Sapse, Anne‐Marie

doi:10.1021/ol9908326

Cited by 32 publications

(27 citation statements)

References 19 publications

(27 reference statements)

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“…The very low polarizability, high electronegativity, and three tightly bound nonbonding electron pairs of fluorine result in stronger repulsive and electrostatic interactions. These results are consistent with other ab initio calculations at a comparable level for the fluorobenzene⋅⋅⋅ benzene complex, in which the orientation with the fluorine atom pointing towards the plane of the benzene ring was not found to be energetically very favorable 23. Finally, the geometries for the approach of a hydrogen atom to the second aromatic ring are in very good agreement with other accounts of aromatic–aromatic “edge‐to‐face” interactions24, 25, 26, 16 with a minimum at 5.25 Å and a dimer MP2 interaction energy value of −9.54 kJ mol −1 .…”

Section: Structures Factor Xa Inhibition Constants (Ki) and Estimatsupporting

confidence: 92%

Evidence for CCl/CBr⋅⋅⋅π Interactions as an Important Contribution to Protein–Ligand Binding Affinity

et al. 2009

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Attraktives Chlor: Nichtkovalente Wechselwirkungen zwischen Chlor‐ oder Brom‐Atomen und aromatischen Ringen in Proteinen bieten einen neuen Ansatzpunkt für die Beeinflussung der molekularen Erkennung. Die Einführung dieser Substituenten an spezifischen Positionen zweier Faktor‐Xa‐Inhibitoren erhöht deren freie Bindungsenergie durch die Wechselwirkung mit einer Tyrosineinheit. Das allgemeine Vorkommen dieses Strukturmotivs wird anhand zahlreicher Kristallstukturen und quantenchemischer Rechnungen belegt (siehe Bild).

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Section: Structures Factor Xa Inhibition Constants (Ki) and Estimatsupporting

confidence: 92%

Evidence for CCl/CBr⋅⋅⋅π Interactions as an Important Contribution to Protein–Ligand Binding Affinity

et al. 2009

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“…. benzene complex, where the orientation with F pointing towards the plane of benzene was energetically not very favourable [43]. Finally, the geometries for hydrogen approaching the second aromatic ring are in very good agreement with other accounts of aromatic-aromatic "edge-to-face" interactions [117,[126][127][128] with a minimum at 5.25Å and a MP2 interaction energy of −9.54 kJ/mol.…”

Section: Exploring the Halogen π Interaction By Ab-initio Calcusupporting

confidence: 81%

“…These structures were also studied by quantum-chemical calculations methods, further underscoring the original hypothesis deduced from X-ray crystal structures of CAII-ligand complexes, that favourable fluorine-hydrogen interactions could indeed influence protein-ligand binding affinity [43].…”

Section: Influence Of Fluorine On Ch π and π ···π Interactionsmentioning

confidence: 65%

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Halogen…π Interactions as Important Contributors to Binding Affinity in Medicinal Chemistry

Matter

Nazaré

Güssregen

2012

The Importance of Pi‐Interactions in Crystal Engineering

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IntroductionMolecular recognition in biological systems relies on a sophisticated interplay of several nonbonded interactions between ligands and their binding sites in addition to other important factors like binding kinetics and desolvation. Insight into those interactions became possible due to the tremendous increase of 3D structural information of protein targets for medical therapy, as exemplified by the growth of the PDB database (RCSB [1,2]) with now ∼70 000 entries (December 2010). Detailed database analysis provides many structurally characterised examples for ligands favourably interacting with their target protein and thus allows studying the relevant interaction motifs contributing to the experimentally observed binding affinity. In addition, guidelines on favourable motifs can be deduced based on interaction geometries and associated affinity data [3].In order to identify lead structures for optimisation, biophysical and in silico lead finding techniques, like virtual screening [4][5][6][7] or structure-based design, are integral components in the industrial drug-discovery setting today and complementing the routinely applied highthroughput screening (HTS) [8,9]. Capitalising on 3D structural knowledge, computational approaches are cost effective and powerful, and can potentially reduce the number of in-vitro assays after in-silico selection to a few hundreds or thousands.

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“…Notably, close contacts are observed between aromatic CH, for example in Phe, and FC residues 9. 10 In particular, the CF 3 group has a pronounced lipophilicity, as reflected by its Hansch–Leo substituent parameter π of 0.88 (CH 3 : π =0.56, CH 2 CH 3 : π =1.02) 11–13. In other cases, the effect of F substitution on protein–ligand binding is a more indirect one.…”

Section: Introductionmentioning

confidence: 99%

Progress curve analysis of qRT‐PCR reactions using the logistic growth equation

Liu

Udhe-Stone

Goudar

2011

Biotechnology Progress

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We present an alternate approach for analyzing data from real-time reverse transcription polymerase chain reaction (qRT-PCR) experiments by fitting individual fluorescence vs. cycle number (F vs. C) curves to the logistic growth equation. The best fit parameters determined by nonlinear least squares were used to compute the second derivative of the logistic equation and the cycle threshold, C(t), was determined from the maximum value of the second derivative. This C(t) value was subsequently used to determine ΔΔC(t) and the amplification efficiency, E(n), thereby completing the analysis on a qRT-PCR data set. The robustness of the logistic approach was verified by testing ~600 F vs. C curves using both new and previously published data sets. In most cases, comparisons were made between the logistic estimates and those from the standard curve and comparative C(t) methods. Deviations between the logistic and standard curve method ranged between 3-10% for C(t) estimates, 2-10% for ΔΔC(t) estimates, and 1-11% for E(n) estimates. The correlations between C(t) estimates from the logistic and standard curve methods were very high, often >0.95. When compared with five other established methods of qRT-PCR data analysis to predict initial concentrations of two genes encompassing a total of 500 F vs. C curves, the logistic estimates were of comparable accuracy. This reliable performance of the logistic approach comes without the need to construct standard curves which can be a laborious undertaking. Also, no a priori assumptions for E(n) are necessary while some other methods assume equal E(n) values for the reference and target genes, an assumption that is not universally valid. In addition, by accurately describing the data in the plateau region of the F vs. C curve, the logistic method overcomes the limitations of the sigmoidal curve fitting method. The streamlined nature of the logistic approach makes it ideal for complete automation on a variety of computing environments thereby completely eliminating user bias. The simplicity, robustness, and ease of computer implementation of the logistic approach should make it an attractive alternative for rapidly analyzing qRT-PCR data.

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Models of F·H Contacts Relevant to the Binding of Fluoroaromatic Inhibitors to Carbonic Anhydrase II

Cited by 32 publications

References 19 publications

Evidence for CCl/CBr⋅⋅⋅π Interactions as an Important Contribution to Protein–Ligand Binding Affinity

Evidence for CCl/CBr⋅⋅⋅π Interactions as an Important Contribution to Protein–Ligand Binding Affinity

Halogen…π Interactions as Important Contributors to Binding Affinity in Medicinal Chemistry

Progress curve analysis of qRT‐PCR reactions using the logistic growth equation

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