Use of Catalyst in a 3D-QSAR Study of the Interactions between Flavor Compounds and β-Lactoglobulin

Tromelin, Anne; Guichard, Élisabeth

doi:10.1021/jf0207981

Cited by 25 publications

(19 citation statements)

References 25 publications

(33 reference statements)

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“…This test randomizes the activity data associated with the training set compounds, and the randomized training sets are used to generate pharmacophore hypotheses using the same features and parameters to develop the original pharmacophore hypothesis. If the randomized data set results in the generation of a pharmacophore with better cost values, RMSD, and correlation, the original hypothesis is generated by chance . The confidence level was set to 95%, where 19 random spread sheets (random hypotheses) were generated.…”

Section: Methodsmentioning

confidence: 99%

The Discovery of a Novel and Selective Inhibitor of PTP1B Over TCPTP: 3D QSAR Pharmacophore Modeling, Virtual Screening, Synthesis, and Biological Evaluation

Jin

Wang

et al. 2014

Chem Biol Drug Des

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Given the special role of insulin and leptin signaling in various biological responses, protein-tyrosine phosphatase-1B (PTP1B) was regarded as a novel therapeutic target for treating type 2 diabetes and obesity. However, owing to the highly conserved (sequence identity of about 74%) in active pocket, targeting PTP1B for drug discovery is a great challenge. In this study, we employed the software package Discovery Studio to develop 3D QSAR pharmacophore models for PTP1B and TCPTP inhibitors. It was further validated by three methods (cost analysis, test set prediction, and Fisher's test) to show that the models can be used to predict the biological activities of compounds without costly and time-consuming synthesis. The criteria for virtual screening were also validated by testing the selective PTP1B inhibitors. Virtual screening experiments and subsequent in vitro evaluation of promising hits revealed a novel and selective inhibitor of PTP1B over TCPTP. After that, a most likely binding mode was proposed. Thus, the findings reported here may provide a new strategy in discovering selective PTP1B inhibitors.

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Section: Methodsmentioning

confidence: 99%

The Discovery of a Novel and Selective Inhibitor of PTP1B Over TCPTP: 3D QSAR Pharmacophore Modeling, Virtual Screening, Synthesis, and Biological Evaluation

Jin

Wang

et al. 2014

Chem Biol Drug Des

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“…The binding onto the protein surface of aroma compounds that have or adopt a compact structure occurs in a site located between strand β G, α helix, and strand β I of β-lactoglobulin (Tavel et al 2008a). The existence of at least two binding sites was also suggested using a 3D QSAR study with CATALYST software on binding constants between aroma compounds and β-lactoglobulin: one binding site for ketones, esters, lactones, and alcohols, and the other one for terpenes, some cyclic compounds, and aromatic compounds (Tromelin and Guichard 2003). The use of CATALYST for modeling interaction also underlined the fact that hydrophobicity was not the only important feature, but also that the topology of hydrocarbon chain and hydrogen bonding should be essential in the binding involved between aroma and protein.…”

Section: Influence Of Proteinsmentioning

confidence: 99%

Lipid-Derived Flavor Compounds

Jeleń¹

2011

Food Flavors

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“…12 Three sets of aroma compounds were used in this investigation and named group P26 (Table 1, compounds 1-26) 13 , group S24 (Table 1, compounds 27-50) 14 and group R35 (Table 1, compounds 51-85). 15 The binding constant values (K b ) were previously determined in our laboratory by affinity chromatography [13][14][15] and were used independently for each group (Table 1).…”

Section: Molecular Modelling Study Qsarmentioning

confidence: 99%

“…12,28 A hypothesis generation run was performed on group P26 (esters). Four features, one HBA lipid, one hydrophobic and two hydrophobic aliphatic, made up the best significant hypothesis, which underestimated the affinity values of two aromatic compounds (ethyl benzoate 4 and methyl benzoate 11).…”

Section: Catalyst 3d-qsar Studymentioning

confidence: 99%

Interaction between flavour compounds andβ-lactoglobulin: approach by NMR and 2D/3D-QSAR studies of ligands

Tromelin¹,

Guichard²

2005

Flavour Fragr. J.

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Interactions between flavour compounds and β-lactoglobulin (BLG) have been the subject of several studies, but there are no unanimous binding site explanations. In our laboratory, interactions between BLG, and two flavour compounds, β-ionone and γ-decalactone, were studied by 2D-NMR spectroscopy. It appears that several amino acids affected by binding of γ-decalactone are buried in the central cavity, whereas binding of β-ionone affects amino acids located in a groove near the outer surface of the protein. 2D/3D-QSAR studies were performed using QSAR + + + + + module of Cerius 2 and Catalyst. The QSAR equation provided by Cerius involves three molecular descriptors: AlogP98 and two topological connectivity indices (CHI-0 and CHI-1). This model takes into account hydrophobicity and molecular shape more than molecular volume. A relatively flat (e.g. a circle) and elongated (non-branched) shape appears to be able to increase the affinity for BLG. In this way, affinity appears to be strongly related to London dispersive forces. It adequately satisfies internal and external validation and allows a significant, but not an accurate, prediction of binding of aroma compounds to BLG. The commercially available software Catalyst focuses the modelling on the molecular behaviour of a ligand interacting with a receptor from the point of view of the receptor, but using only information from the ligand. In this way, it appears to be appropriate to identify binding sub-sites on BLG. The 3D-QSAR models generation runs succeeded in providing significant models which precisely estimated the affinities of sub-sets of compounds. A hydrogen bond acceptor and at least two hydrophobic features constitute the best models. Some model allows explaining the abnormal values of affinity constants of any compounds as α α α α α-terpineol and highlights the importance of hydrogen bonding. Thus, Catalyst confirms the existence of at least two binding sites on the BLG.

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Use of Catalyst in a 3D-QSAR Study of the Interactions between Flavor Compounds and β-Lactoglobulin

Cited by 25 publications

References 25 publications

The Discovery of a Novel and Selective Inhibitor of PTP1B Over TCPTP: 3D QSAR Pharmacophore Modeling, Virtual Screening, Synthesis, and Biological Evaluation

The Discovery of a Novel and Selective Inhibitor of PTP1B Over TCPTP: 3D QSAR Pharmacophore Modeling, Virtual Screening, Synthesis, and Biological Evaluation

Lipid-Derived Flavor Compounds

Interaction between flavour compounds andβ-lactoglobulin: approach by NMR and 2D/3D-QSAR studies of ligands

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