Membrane-dependent activities of human 15-lox-2 and its murine counterpart implications for murine models of atherosclerosis

© 2016 by The American Society for Biochemistry and Molecular Biology, Inc. The enzyme encoded by the ALOX15B gene has been linked to the development of atherosclerotic plaques in humans and in a mouse model of hypercholesterolemia. In vitro, these enzymes, which share 78% sequence identity, generate distinct products from their substrate arachidonic acid: the human enzyme, a 15-S-hydroperoxy product; and the murine enzyme, an 8-S-product. We probed the activities of these enzymes with nanodiscs as membrane mimics to determine whether they can access substrate esterified in a bilayer and characterized their activities at the membrane interface. We observed that both enzymes transform phospholipid-esterified arachidonic acid to a 15-S-product. Moreover, when expressed in transfected HEK cells, both enzymes result in significant increases in the amounts of 15-hydroxyderivatives of eicosanoids detected. In addition, we show that 15-LOX-2 is distributed at the plasma membrane when the HEK293 cells are stimulated by the addition Ca 2+ ionophore and that cellular localization is dependent upon the presence of a putative membrane insertion loop. We also report that sequence differences between the human and mouse enzymes in this loop appear to confer distinct mechanisms of enzyme-membrane interaction for the homologues

Crystal structure of a lipoxygenase in complex with substrate: The arachidonic acid-binding site of 8R-lipoxygenase

Lipoxygenases (LOX) play critical roles in mammalian biology in the generation of potent lipid mediators of the inflammatory response; consequently, they are targets for the development of isoform-specific inhibitors. The regio- and stereo-specificity of the oxygenation of polyunsaturated fatty acids by the enzymes is understood in terms of the chemistry, but structural observation of the enzyme-substrate interactions is lacking. Although several LOX crystal structures are available, heretofore the rapid oxygenation of bound substrate has precluded capture of the enzyme-substrate complex, leaving a gap between chemical and structural insights. In this report, we describe the 2.0 Å resolution structure of 8R-LOX in complex with arachidonic acid obtained under anaerobic conditions. Subtle rearrangements, primarily in the side chains of three amino acids, allow binding of arachidonic acid in a catalytically competent conformation. Accompanying experimental work supports a model in which both substrate tethering and cavity depth contribute to positioning the appropriate carbon at the catalytic machinery

Crystal Structure of a Lipoxygenase in Complex with Substrate

Lipoxygenases (LOX) play critical roles in mammalian biology in the generation of potent lipid mediators of the inflammatory response; consequently, they are targets for the development of isoform-specific inhibitors. The regio- and stereo-specificity of the oxygenation of polyunsaturated fatty acids by the enzymes is understood in terms of the chemistry, but structural observation of the enzyme-substrate interactions is lacking. Although several LOX crystal structures are available, heretofore the rapid oxygenation of bound substrate has precluded capture of the enzyme-substrate complex, leaving a gap between chemical and structural insights. In this report, we describe the 2.0 Å resolution structure of 8R-LOX in complex with arachidonic acid obtained under anaerobic conditions. Subtle rearrangements, primarily in the side chains of three amino acids, allow binding of arachidonic acid in a catalytically competent conformation. Accompanying experimental work supports a model in which both substrate tethering and cavity depth contribute to positioning the appropriate carbon at the catalytic machinery

Heterogeneous Classifier Fusion for Ligand-Based Virtual Screening: Or, How Decision Making by Committee Can Be a Good Thing

The concept of data fusion - the combination of information from different sources describing the same object with the expectation to generate a more accurate representation - has found application in a very broad range of disciplines. In the context of ligand-based virtual screening (VS), data fusion has been applied to combine knowledge from either different active molecules or different fingerprints to improve similarity search performance. Machine-learning (ML) methods based on fusion of multiple homogeneous classifiers, in particular random forests, have also been widely applied in the ML literature. The heterogeneous version of classifier fusion - fusing the predictions from different model types - has been less explored. Here, we investigate heterogeneous classifier fusion for ligand-based VS using three different ML methods, RF, naı̈ve Bayes (NB), and logistic regression (LR), with four 2D fingerprints, atom pairs, topological torsions, RDKit fingerprint, and circular fingerprint. The methods are compared using a previously developed benchmarking platform for 2D fingerprints which is extended to ML methods in this article. The original data sets are filtered for difficulty, and a new set of challenging data sets from ChEMBL is added. Data sets were also generated for a second use case: starting from a small set of related actives instead of diverse actives. The final fused model consistently outperforms the other approaches across the broad variety of targets studied, indicating that heterogeneous classifier fusion is a very promising approach for ligand-based VS. The new data sets together with the adapted source code for ML methods are provided in the Supporting Information