MOESM1 of Discriminating agonist and antagonist ligands of the nuclear receptors using 3D-pharmacophores
Creators: name: Lagarde, Nathalie
name: Delahaye, Solenne
name: Jean-François Zagury
name: Montes, Matthieu
Publisher: name: Figshare
Year: 2016
Subjects: subject: Biophysics
subject: Biochemistry
subject: Medicine
subject: Cell Biology
subject: Molecular Biology
subject: Pharmacology
subject: Biotechnology
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences
subject: FOS: Chemical sciences
subject: Immunology
subject: FOS: Clinical medicine
subject: FOS: Clinical medicine
subject: 69999 Biological Sciences not elsewhere classified
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: Cancer

description: Additional file 1: Figure S1. Correlation between the number of pharmacophores necessary to cover a given dataset and the number of ligands in the dataset. Figure S2. A–N Representation of the pharmacophore features composition of the “SBLB agonist selective pharmacophores” and “SBLB antagonist selective pharmacophores” combinations for each NRLiSt BDB dataset (left graph). The number of ligands found with each pharmacophore and the total number of unique ligands found by combining the pharmacophores are also illustrated (right graph).Figure S3. A–C Comparison of the distribution of pharmacophore features between SBLB agonist selective pharmacophores and SBLB antagonist selective pharmacophores for each NR of the NRLiSt BDB using the Wilcoxon-test. The red line represents the significance threshold (p-value = 0.05). Figure S4. Correlation between the selectivity of a combination of pharmacophore towards their dedicated NRs ligands (average recovery rate against all the others NRLiSt BDB datasets) and the number of ligands in the dataset. Figure S5. Correlation between the selectivity of a combination of pharmacophore towards their dedicated NRs ligands (average recovery rate against all the others NRLiSt BDB datasets) and the number of pharmacophores included in the combination. Figure S6. Correlation between the selectivity of a combination of pharmacophore towards their dedicated NRs ligands (average recovery rate against all the others NRLiSt BDB datasets) and the active ligands over decoys ratio. Figure S7. Structure of the LXR_alpha and LXR_beta agonist that could only be represented by a pharmacophore formed of 2 non-independent features. Figure S8. Structure of the ER_alpha antagonist ligand and ER_alpha agonist ligand that could not be separated using 3D pharmacophore models. Figure S9. Structure of the GR antagonist ligands and GR agonist ligands that could not be separated using 3D pharmacophore models. Figure S10. Structure of the PR agonist ligands and PR antagonist ligands that could not be separated using 3D pharmacophore models. Figure S11. Structure of the PR antagonist ligand and PR agonist ligand that could not be separated using 3D pharmacophore models. Figure S12. Structure of the RAR_alpha agonist ligands and RAR_alpha antagonist ligands that could not be separated using 3D pharmacophore models. Figure S13. Structure of the RAR_gamma agonist ligand and RAR_gamma antagonist ligands that could not be separated using 3D pharmacophore models. Figure S14. Overlapping features between the SB_pharmacophore5 ER_alpha agonist selective pharmacophore and LB ER_alpha agonist selective pharmacophores (reference point tethers are represented by the orange circle on the alignment, right graph). Figure S15. Correlation between the number of pharmacophore features and the number of ligands retrieved by each pharmacophore. Table S1–Table S54.. For each NR agonist and antagonist datasets. Pharmacophore features composition of the SBLB agonist or antagonist selective pharmacophores (AR: aromatic ring, H:hydrophobic, HBA: Hydrogen Bond Acceptor, HBD: Hydrogen Bond Donor, PI: Positive ionizable area, NI: negative ionisable area, opt.: feature set as optional) and number of active ligands found with each pharmacophore (“Nbr of agonist or antagonist ligands found”), number of unique active ligands found by combining the pharmacophores (“Accumulated nbr of unique agonist or antagonist ligands found”) and number of decoys found (“Nbr of agonist or antagonist ligands found”). Table S55. Overlapping features between ER_alpha the SB agonist selective pharmacophores and the LB agonist selective pharmacophores included in the SBLB agonist combination of pharmacophores (Overlap: number of LB pharmacophore features that overlap SB pharmacophore features, RMS: Root Mean Square of matched features pairs, Score: LigandScout alignment score). Table S56. Overlapping features between ER_alpha the SB antagonist selective pharmacophores and the LB antagonist selective pharmacophores included in the SBLB antagonist combination of pharmacophores (Overlap: number of LB pharmacophore features that overlap SB pharmacophore features, RMS: Root Mean Square of matched features pairs, Score: LigandScout alignment score).