The unique metabolic profile of cancer (aerobic glycolysis) might confer apoptosis resistance and be therapeutically targeted. Compared to normal cells, several human cancers have high mitochondrial membrane potential (DeltaPsim) and low expression of the K+ channel Kv1.5, both contributing to apoptosis resistance. Dichloroacetate (DCA) inhibits mitochondrial pyruvate dehydrogenase kinase (PDK), shifts metabolism from glycolysis to glucose oxidation, decreases DeltaPsim, increases mitochondrial H2O2, and activates Kv channels in all cancer, but not normal, cells; DCA upregulates Kv1.5 by an NFAT1-dependent mechanism. DCA induces apoptosis, decreases proliferation, and inhibits tumor growth, without apparent toxicity. Molecular inhibition of PDK2 by siRNA mimics DCA. The mitochondria-NFAT-Kv axis and PDK are important therapeutic targets in cancer; the orally available DCA is a promising selective anticancer agent.

Arteries and veins are anatomically, functionally and molecularly distinct. The current model of arterial-venous identity proposes that binding of vascular endothelial growth factor to its heterodimeric receptor--Flk1 and neuropilin 1 (NP-1; also called Nrp1)--activates the Notch signalling pathway in the endothelium, causing induction of ephrin B2 expression and suppression of ephrin receptor B4 expression to establish arterial identity. Little is known about vein identity except that it involves ephrin receptor B4 expression, because Notch signalling is not activated in veins; an unresolved question is how vein identity is regulated. Here, we show that COUP-TFII (also known as Nr2f2), a member of the orphan nuclear receptor superfamily, is specifically expressed in venous but not arterial endothelium. Ablation of COUP-TFII in endothelial cells enables veins to acquire arterial characteristics, including the expression of arterial markers NP-1 and Notch signalling molecules, and the generation of haematopoietic cell clusters. Furthermore, ectopic expression of COUP-TFII in endothelial cells results in the fusion of veins and arteries in transgenic mouse embryos. Thus, COUP-TFII has a critical role in repressing Notch signalling to maintain vein identity, which suggests that vein identity is under genetic control and is not derived by a default pathway.

Predicting the rate of nonfacilitated permeation of solutes across lipid bilayers is important to drug design, toxicology, and signaling. These rates can be estimated using molecular dynamics simulations combined with the inhomogeneous solubility-diffusion model, which requires calculation of the potential of mean force and position-dependent diffusivity of the solute along the transmembrane axis. In this paper, we assess the efficiency and accuracy of several methods for the calculation of the permeability of a model DMPC bilayer to urea, benzoic acid, and codeine. We compare umbrella sampling, replica exchange umbrella sampling, adaptive biasing forces, and multiple-walker adaptive biasing forces for the calculation of the transmembrane PMF. No definitive advantage for any of these methods in their ability to predict the permeability coefficient Pm was found, provided that a sufficiently long equilibration is performed. For diffusivities, a Bayesian inference method was compared to a Generalized Langevin method, both being sensitive to chosen parameters and the slow relaxation of membrane defects. Agreement within 1.5 log units of the computed Pm with experiment is found for all permeants and methods. Remaining discrepancies can likely be attributed to limitations of the force field as well as slowly relaxing collective movements within the lipid environment. Numerical calculations based on model profiles show that Pm can be reliably estimated from only a few data points, leading to recommendations for calculating Pm from simulations.

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Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.