In silico identification of an aryl hydrocarbon receptor antagonist with biological activity in vitro and in vivo

The aryl hydrocarbon receptor (AHR) is critically involved in several physiologic processes, including cancer progression and multiple immune system activities. We, and others, have hypothesized that AHR modulators represent an important new class of targeted therapeutics. Here, ligand shape–based virtual modeling techniques were used to identify novel AHR ligands on the basis of previously identified chemotypes. Four structurally unique compounds were identified. One lead compound, 2-((2-(5-bromofuran-2-yl)-4-oxo-4H-chromen-3-yl)oxy)acetamide (CB7993113), was further tested for its ability to block three AHR-dependent biologic activities: triple-negative breast cancer cell invasion or migration in vitro and AHR ligand–induced bone marrow toxicity in vivo. CB7993113 directly bound both murine and human AHR and inhibited polycyclic aromatic hydrocarbon (PAH)– and TCDD-induced reporter activity by 75% and 90% respectively. A novel homology model, comprehensive agonist and inhibitor titration experiments, and AHR localization studies were consistent with competitive antagonism and blockade of nuclear translocation as the primary mechanism of action. CB7993113 (IC(50) 3.3 × 10(−7) M) effectively reduced invasion of human breast cancer cells in three-dimensional cultures and blocked tumor cell migration in two-dimensional cultures without significantly affecting cell viability or proliferation. Finally, CB7993113 effectively inhibited the bone marrow ablative effects of 7,12-dimethylbenz[a]anthracene in vivo, demonstrating drug absorption and tissue distribution leading to pharmacological efficacy. These experiments suggest that AHR antagonists such as CB7993113 may represent a new class of targeted therapeutics for immunomodulation and/or cancer therapy

In Silico Identification of an Aryl Hydrocarbon Receptor Antagonist with Biological Activity In Vitro and In Vivo

ABSTRACT The aryl hydrocarbon receptor (AHR) is critically involved in several physiologic processes, including cancer progression and multiple immune system activities. We, and others, have hypothesized that AHR modulators represent an important new class of targeted therapeutics. Here, ligand shape-based virtual modeling techniques were used to identify novel AHR ligands on the basis of previously identified chemotypes. Four structurally unique compounds were identified. One lead compound, 2-((2-(5-bromofuran-2-yl)-4-oxo-4H-chromen-3-yl)oxy) acetamide (CB7993113), was further tested for its ability to block three AHR-dependent biologic activities: triple-negative breast cancer cell invasion or migration in vitro and AHR ligand-induced bone marrow toxicity in vivo. CB7993113 directly bound both murine and human AHR and inhibited polycyclic aromatic hydrocarbon (PAH)-and TCDD-induced reporter activity by 75% and 90% respectively. A novel homology model, comprehensive agonist and inhibitor titration experiments, and AHR localization studies were consistent with competitive antagonism and blockade of nuclear translocation as the primary mechanism of action. CB7993113 (IC 50 3.3 Â 10 27 M) effectively reduced invasion of human breast cancer cells in three-dimensional cultures and blocked tumor cell migration in two-dimensional cultures without significantly affecting cell viability or proliferation. Finally, CB7993113 effectively inhibited the bone marrow ablative effects of 7,12-dimethylbenz[a]anthracene in vivo, demonstrating drug absorption and tissue distribution leading to pharmacological efficacy. These experiments suggest that AHR antagonists such as CB7993113 may represent a new class of targeted therapeutics for immunomodulation and/or cancer therapy

Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions

Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies

Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions

International audienceCommunity-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies

Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions

Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies

Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions

Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies