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Stacking effects are among the most important effects in DNA. We have recently studied their influence in fragments of DNA through the analysis of NMR magnetic shieldings, firstly in vacuo . As a continuation of this line of research we show here the influence of solvent effects on the shieldings through the application of both explicit and implicit models. We found that the explicit solvent model is more appropriate for consideration due to the results matching better in general with experiments, as well as providing clear knowledge of the electronic origin of the value of the shieldings. Our study is grounded on a recently developed theoretical model of our own, by which we are able to learn about the magnetic effects of given fragments of DNA molecules on selected base pairs. We use the shieldings of the atoms of a central base pair (guanine-cytosine) of a selected fragment of DNA molecules as descriptors of physical effects, like π-stacking and solvent effects. They can be taken separately and altogether. The effect of π-stacking is introduced through the addition of some pairs above and below of the central base pair, and now, the solvent effect is considered including a network of water molecules that consist of two solvation layers, which were fixed in the calculations performed in all fragments. We show that the solvent effects enhance the stacking effects on the magnetic shieldings of atoms that belong to the external N-H bonds. The net effect is of deshielding on both atoms. There is also a deshielding effect on the carbon atoms that belong to CO bonds, for which the oxygen atom has an explicit hydrogen bond (HB) with a solvent water molecule. Solvent effects are found to be no higher than a few percent of the total value of the shieldings (between 1% and 5%) for most atoms, although there are few for which such an effect can be higher. There is one nitrogen atom, the acceptor of the HB between guanine and cytosine, that is more highly shielded (around 15 ppm or 10%) when the explicit solvent is considered. In a similar manner, the most external nitrogen atom of cytosine and the hydrogen atom that is bonded to it are highly deshielded (around 10 ppm for nitrogen and around 3 ppm for hydrogen).

The use of high-throughput docking (HTD) in the drug discovery pipeline is today widely established. In spite of methodological improvements in docking accuracy (pose prediction), scoring power, ranking power, and screening power in HTD remain challenging. In fact, pose prediction is of critical importance in view of the pose-dependent scoring process, since incorrect poses will necessarily decrease the ranking power of scoring functions. The combination of results from different docking programs (consensus scoring) has been shown to improve the performance of HTD. Moreover, it has been also shown that a pose consensus approach might also result in database enrichment. We present a new methodology named Pose/Ranking Consensus (PRC) that combines both pose and ranking consensus approaches, to overcome the limitations of each stand-alone strategy. This approach has been developed using four docking programs (ICM, rDock, Auto Dock 4, and PLANTS; the first one is commercial, the other three are free). We undertook a thorough analysis for the best way of combining pose and rank strategies, and applied the PRC to a wide range of 34 targets sampling different protein families and binding site properties. Our approach exhibits an improved systematic performance in terms of enrichment factor and hit rate with respect to either pose consensus or consensus ranking alone strategies at a lower computational cost, while always ensuring the recovery of a suitable number of ligands. An analysis using four free docking programs (replacing ICM by Auto Dock Vina) displayed comparable results., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Class B G protein-coupled receptors (GPCRs) are involved in a variety of human pathophysiological states. These groups of membrane receptors are less studied than class A GPCRs due to the lack of structural information, delayed small molecule drug discovery, and scarce fluorescence detection tools available. The class B corticotropin-releasing hormone type 1 receptor (CRHR1) is a key player in the stress response whose dysregulation is critically involved in stress-related disorders: psychiatric conditions (i.e. depression, anxiety, and addictions), neuroendocrinological alterations, and neurodegenerative diseases. Here, we present a strategy to label GPCRs with a small fluorescent antagonist that permits the observation of the receptor in live cells through stochastic optical reconstruction microscopy (STORM) with 23 nm resolution. The marker, an aza-BODIPY derivative, was designed based on computational docking studies, then synthesized, and finally tested in biological cells. Experiments on hippocampal neurons demonstrate antagonist effects in similar concentrations as the well-established antagonist CP-376395. A quantitative analysis of two color STORM images enabled the determination of the binding affinity of the new marker in the cellular environment.

With >800 members in humans, the G protein-coupled receptor (GPCR) super-family is the target for more than 30% of the marketed drugs. The recent boom in GPCR crystallography has enabled the solution of ∼30 different GPCR structures, which boosted the identification and optimization of novel modulators and new chemical entities through structure-based strategies. However, the number of available structures represents a small part of the human GPCR druggable target space, and its complete coverage in the near future seems unlikely. Homology modelling represents a reliable tool to fill this gap, and hence to vastly expand the horizons of structure-based drug discovery and design. In this Feature Article, we show from a wealth of retrospective and prospective studies that in spite of the pitfalls of and standing challenges in homology modelling, structural models have been critical for the blossoming and success of GPCR structure-based lead discovery and optimization endeavours; in addition, they have also been instrumental in characterizing receptor-ligand interaction, guiding the design of site-directed mutagenesis and SAR studies, and assessing off-target effects. Considering though their current limitations, we also discuss the most pressing issues to develop more accurate homology modelling strategies, with a special focus on the integration of computational tools with biochemical, biophysical and QSAR data, highlighting methodological aspects and recent progress. The teachings of the three GPCR Dock community-wide assessments and the fresh developments in GPCR classes B, C and F are commented. This is a fast growing and highly promising field of research, and in the coming years, the use of high-quality models should enable the discovery of a growing number of potent, selective and efficient GPCR drug leads with high therapeutic potential through receptor structure-based strategies.