Defining the molecular details and consequences of the association of water-soluble proteins with membranes is fundamental to understanding protein-lipid interactions and membrane functioning. Phospholipase A 2 (PLA 2 ) enzymes, which catalyze the hydrolysis of phospholipid substrates that compose the membrane bilayers, provide the ideal system for studying protein-lipid interactions. Our study focuses on understanding the catalytic cycle of two different human PLA 2 s: the cytosolic Group IVA cPLA 2 and calcium-independent Group VIA iPLA 2 . Computer-aided techniques guided by deuterium exchange mass spectrometry data, were used to create structural complexes of each enzyme with a single phospholipid substrate molecule, whereas the substrate extraction process was studied using steered molecular dynamics simulations. Molecular dynamic simulations of the enzyme-substrate-membrane systems revealed important information about the mechanisms by which these enzymes associate with the membrane and then extract and bind their phospholipid substrate. Our data support the hypothesis that the membrane acts as an allosteric ligand that binds at the allosteric site of the enzyme's interfacial surface, shifting its conformation from a closed (inactive) state in water to an open (active) state at the membrane interface.GIVA cPLA 2 | GVIA iPLA 2 | PAPC | MD simulations | DXMS

We demonstrate that lipidomics coupled with molecular dynamics reveal unique phospholipase A2 specificity toward membrane phospholipid substrates. We discovered unexpected headgroup and acyl-chain specificity for three major human phospholipases A2. The differences between each enzyme’s specificity, coupled with molecular dynamics-based structural and binding studies, revealed unique binding sites and interfacial surface binding moieties for each enzyme that explain the observed specificity at a hitherto inaccessible structural level. Surprisingly, we discovered that a unique hydrophobic binding site for the cleaved fatty acid dominates each enzyme’s specificity rather than its catalytic residues and polar headgroup binding site. Molecular dynamics simulations revealed the optimal phospholipid binding mode leading to a detailed understanding of the preference of cytosolic phospholipase A2 for cleavage of proinflammatory arachidonic acid, calcium-independent phospholipase A2, which is involved in membrane remodeling for cleavage of linoleic acid and for antibacterial secreted phospholipase A2 favoring linoleic acid, saturated fatty acids, and phosphatidylglycerol.

. 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 machine 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 employed 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 highlights hot topics for further development.

Phospholipase A 2 (PLA 2) enzymes are the upstream regulators of the eicosanoid pathway liberating free arachidonic acid from the sn−2 position of membrane phospholipids. Increased levels of intracellular arachidonic acid serve as a substrate for the eicosanoid biosynthetic pathway enzymes including cyclooxygenases, lipoxygenases and cytochrome P450s that lead to inflammation. The Group IVA cytosolic (cPLA 2), Group VIA calcium-independent (iPLA 2), and Group V secreted (sPLA 2) are three well-characterized human enzymes that have been implicated in eicosanoid formation. In this review, we will introduce and summarize the regulation of catalytic activity and cellular localization, structural characteristics, interfacial activation and kinetics, substrate specificity, inhibitor binding and interactions, and the downstream implications for eicosanoid biosynthesis of these three important PLA 2 enzymes.

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.

The phospholipase A superfamily of enzymes plays a significant role in the development and progression of numerous inflammatory diseases. Through their catalytic action on membrane phospholipids, phospholipases are the upstream regulators of the eicosanoid pathway releasing free fatty acids for cyclooxygenases, lipoxygenases, and cytochrome P450 enzymes which produce various well-known inflammatory mediators including leukotrienes, thromboxanes and prostaglandins. Elucidating the association of phospholipases A with the membrane, the extraction and binding of phospholipid substrates, and their interactions with small-molecule inhibitors is crucial for the development of new anti-inflammatory therapeutics. Studying phospholipases has been challenging because they act on the surface of cellular membranes and micelles. Multidisciplinary approaches including hydrogen/deuterium exchange mass spectrometry, molecular dynamics simulations, and other computer-aided drug design techniques have been successfully employed by our laboratory to study interactions of phospholipases with membranes, phospholipid substrates and inhibitors. This review summarizes the application of these techniques to study four human recombinant phospholipases A.

The objectives of this study include the design of a series of novel fullerene-based inhibitors for HIV-1 protease (HIV-1 PR), by employing two strategies that can also be applied to the design of inhibitors for any other target. Additionally, the interactions which contribute to the observed exceptionally high binding free energies were analyzed. In particular, we investigated: (1) hydrogen bonding (H-bond) interactions between specific fullerene derivatives and the protease, (2) the regions of HIV-1 PR that play a significant role in binding, (3) protease changes upon binding and (4) various contributions to the binding free energy, in order to identify the most significant of them. This study has been performed by employing a docking technique, two 3D-QSAR models, molecular dynamics (MD) simulations and the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method. Our computed binding free energies are in satisfactory agreement with the experimental results. The suitability of specific fullerene derivatives as drug candidates was further enhanced, after ADMET (absorption, distribution, metabolism, excretion and toxicity) properties have been estimated to be promising. The outcomes of this study revealed important protein-ligand interaction patterns that may lead towards the development of novel, potent HIV-1 PR inhibitors.

Group VI Ca2+-independent phospholipase A2 (iPLA2) is a water-soluble enzyme that is active when associated with phospholipid membranes. Despite its clear pharmaceutical relevance, no X-ray or NMR structural information is currently available for the iPLA2 or its membrane complex. In this paper, we combine homology modeling with coarse-grained (CG) and all-atom (AA) molecular dynamics (MD) simulations to build structural models of iPLA2 in association with a phospholipid bilayer. CG-MD simulations of the membrane insertion process were employed to provide a starting point for an atomistic description. Six AA-MD simulations were then conducted for 60 ns, starting from different initial CG structures, to refine the membrane complex. The resulting structures are shown to be consistent with each other and with deuterium exchange mass spectrometry (DXMS) experiments, suggesting that our approach is suitable for the modeling of iPLA2 at the membrane surface. The models show that an anchoring region (residues 710–724) forms an amphipathic helix that is stabilized by the membrane. In future studies, the proposed iPLA2 models should provide a structural basis for understanding the mechanisms of lipid extraction and drug-inhibition. In addition, the dual-resolution approach discussed here should provide the means for the future exploration of the impact of lipid diversity and sequence mutations on the activity of iPLA2 and related enzymes.