Quantifying Water-Mediated Protein–Ligand Interactions in a Glutamate Receptor: A DFT Study

It is becoming increasingly clear that careful treatment of water molecules in ligand–protein interactions is required in many cases if the correct binding pose is to be identified in molecular docking. Water can form complex bridging networks and can play a critical role in dictating the binding mode of ligands. A particularly striking example of this can be found in the ionotropic glutamate receptors. Despite possessing similar chemical moieties, crystal structures of glutamate and α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid (AMPA) in complex with the ligand-binding core of the GluA2 ionotropic glutamate receptor revealed, contrary to all expectation, two distinct modes of binding. The difference appears to be related to the position of water molecules within the binding pocket. However, it is unclear exactly what governs the preference for water molecules to occupy a particular site in any one binding mode. In this work we use density functional theory (DFT) calculations to investigate the interaction energies and polarization effects of the various components of the binding pocket. Our results show (i) the energetics of a key water molecule are more favorable for the site found in the glutamate-bound mode compared to the alternative site observed in the AMPA-bound mode, (ii) polarization effects are important for glutamate but less so for AMPA, (iii) ligand–system interaction energies alone can predict the correct binding mode for glutamate, but for AMPA alternative modes of binding have similar interaction energies, and (iv) the internal energy is a significant factor for AMPA but not for glutamate. We discuss the results within the broader context of rational drug-design

Software for the frontiers of quantum chemistry:An overview of developments in the Q-Chem 5 package

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange–correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear–electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an “open teamware” model and an increasingly modular design

Differential response of fish assemblages to coral reef-based seaweed farming.

As the global demand for seaweed-derived products drives the expansion of seaweed farming onto shallow coral ecosystems, the effects of farms on fish assemblages remain largely unexplored. Shallow coral reefs provide food and shelter for highly diverse fish assemblages but are increasingly modified by anthropogenic activities. We hypothesized that the introduction of seaweed farms into degraded shallow coral reefs had potential to generate ecological benefits for fish by adding structural complexity and a possible food source. We conducted 210 transects at 14 locations, with sampling stratified across seaweed farms and sites adjacent to and distant from farms. At a seascape scale, locations were classified by their level of exposure to human disturbance. We compared sites where (1) marine protected areas (MPAs) were established, (2) neither MPAs nor blast fishing was present (hence "unprotected"), and (3) blast fishing occurred. We observed 80,186 fish representing 148 species from 38 families. The negative effects of seaweed farms on fish assemblages appeared stronger in the absence of blast fishing and were strongest when MPAs were present, likely reflecting the positive influence of the MPAs on fish within them. Species differentiating fish assemblages with respect to seaweed farming and disturbance were typically small but also included two key target species. The propensity for seaweed farms to increase fish diversity, abundance, and biomass is limited and may reduce MPA benefits. We suggest that careful consideration be given to the placement of seaweed farms relative to MPAs

The 23 Siganid species for six countries used in the analysis and their diets as listed on Fishbase, where P is a possible but unconfirmed sighting.

<p>The 23 Siganid species for six countries used in the analysis and their diets as listed on Fishbase, where P is a possible but unconfirmed sighting.</p

Impacts of human disturbance on the abundance, biomass and diversity of reef-associated fish in the Danajon Bank.

<p>SF indicate sites where seaweed farming occurs, ADJ and FAR are adjacent and far sites, respectively. Values represent site-specific averages.</p

Countries from the three regions (Southeast Asia, Africa, and the Western Pacific) included in the global analysis of seaweed production.

<p>Countries from the three regions (Southeast Asia, Africa, and the Western Pacific) included in the global analysis of seaweed production.</p

Map of the study area, showing the location of sampling sites.

<p>(1) Pandao, (2) Pandanon, (3) Jandayan Sur. (4) Jandayan Norte, (5) Handumon, (6) Tahong Tahong, (7) Guindacpan, (8) Tambo, (9)Banbanon, (10) Busili-an, (11)Pinamgo, (12) Cataban, (13) Saag, (14) Bansaan</p

A Global Analysis of the Relationship between Farmed Seaweed Production and Herbivorous Fish Catch - Fig 4

<p>Temporal trends in seaweed production (solid line), siganid catch (dashed line) and reef fish catch (dotted line) as a percentage of maximum value (PMV) for the focal countries in each of the three regions: Southeast Asia (a) Indonesia, (b) Malaysia, (c) the Philippines; Africa (d) Tanzania and (e) Zanzibar; and the Western Pacific (f) Fiji.</p

Pairwise tests for Biomass in grams for three levels of disturbance by level of farming present (FARM) where; SF indicate sites where seaweed farming occurs, ADJ and FAR are adjacent and far sites, respectively.

<p>Pairwise tests for Biomass in grams for three levels of disturbance by level of farming present (FARM) where; SF indicate sites where seaweed farming occurs, ADJ and FAR are adjacent and far sites, respectively.</p