Substrate-Specific Inhibition Constants for Phospholipase A2 Acting on Unique Phospholipid Substrates in Mixed Micelles and Membranes Using Lipidomics.

Assaying lipolytic enzymes is extremely challenging because they act on water-insoluble lipid substrates, which are normally components of micelles, vesicles, and cellular membranes. We extended a new lipidomics-based liquid chromatographic-mass spectrometric assay for phospholipases A2 to perform inhibition analysis using a variety of commercially available synthetic and natural phospholipids as substrates. Potent and selective inhibitors of three recombinant human enzymes, including cytosolic, calcium-independent, and secreted phospholipases A2 were used to establish and validate this assay. This is a novel use of dose-response curves with a mixture of phospholipid substrates, not previously feasible using traditional radioactive assays. The new application of lipidomics to developing assays for lipolytic enzymes revolutionizes in vitro testing for the discovery of potent and selective inhibitors using mixtures of membranelike substrates

2-Oxoesters: A Novel Class of Potent and Selective Inhibitors of Cytosolic Group IVA Phospholipase A2.

Cytosolic phospholipase A2 (GIVA cPLA2) is the only PLA2 that exhibits a marked preference for hydrolysis of arachidonic acid containing phospholipid substrates releasing free arachidonic acid and lysophospholipids and giving rise to the generation of diverse lipid mediators involved in inflammatory conditions. Thus, the development of potent and selective GIVA cPLA2 inhibitors is of great importance. We have developed a novel class of such inhibitors based on the 2-oxoester functionality. This functionality in combination with a long aliphatic chain or a chain carrying an appropriate aromatic system, such as the biphenyl system, and a free carboxyl group leads to highly potent and selective GIVA cPLA2 inhibitors (X I(50) values 0.00007-0.00008) and docking studies aid in understanding this selectivity. A methyl 2-oxoester, with a short chain carrying a naphthalene ring, was found to preferentially inhibit the other major intracellular PLA2, the calcium-independent PLA2. In RAW264.7 macrophages, treatment with the most potent 2-oxoester GIVA cPLA2 inhibitor resulted in over 50% decrease in KLA-elicited prostaglandin D2 production. The novel, highly potent and selective GIVA cPLA2 inhibitors provide excellent tools for the study of the role of the enzyme and could contribute to the development of novel therapeutic agents for the treatment of inflammatory diseases

Advances in De Novo Drug Design : From Conventional to Machine Learning Methods

De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including ma-chine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been em-ployed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and high-lights hot topics for further development.Peer reviewe

Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches

Identification of Endocrine Disrupting Chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause Estrogen Receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous Quantitative Structure-Activity Relationships (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα binding affinity (MTL R2=0.71, STL R2=0.73). For ERβ binding affinity, MTL models were significantly more predictive (R2=0.53, p<0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation