The interaction of the general anesthetic etomidate with the γ-aminobutyric acid type A receptor is influenced by a single amino acid

The γ-aminobutyric acid type A (GABAA) receptor is a transmitter-gated ion channel mediating the majority of fast inhibitory synaptic transmission within the brain. The receptor is a pentameric assembly of subunits drawn from multiple classes (α1–6, β1–3, γ1–3, δ1, and ɛ1). Positive allosteric modulation of GABAA receptor activity by general anesthetics represents one logical mechanism for central nervous system depression. The ability of the intravenous general anesthetic etomidate to modulate and activate GABAA receptors is uniquely dependent upon the β subunit subtype present within the receptor. Receptors containing β2- or β3-, but not β1 subunits, are highly sensitive to the agent. Here, chimeric β1/β2 subunits coexpressed in Xenopus laevis oocytes with human α6 and γ2 subunits identified a region distal to the extracellular N-terminal domain as a determinant of the selectivity of etomidate. The mutation of an amino acid (Asn-289) present within the channel domain of the β3 subunit to Ser (the homologous residue in β1), strongly suppressed the GABA-modulatory and GABA-mimetic effects of etomidate. The replacement of the β1 subunit Ser-290 by Asn produced the converse effect. When applied intracellularly to mouse L(tk−) cells stably expressing the α6β3γ2 subunit combination, etomidate was inert. Hence, the effects of a clinically utilized general anesthetic upon a physiologically relevant target protein are dramatically influenced by a single amino acid. Together with the lack of effect of intracellular etomidate, the data argue against a unitary, lipid-based theory of anesthesia.

Game theory and reciprocity in some extensive form experimental games

We examine decision making in two-person extensive form game trees using nine treatments that vary matching protocol, payoffs, and payoff information. Our objective is to establish replicable principles of cooperative versus noncooperative behavior that involve the use of signaling, reciprocity, and backward induction strategies, depending on the availability of dominated direct punishing strategies and the probability of repeated interaction with the same partner. Contrary to the predictions of game theory, we find substantial support for cooperation under complete information even in various single-play treatments.

Interaction between like-charged colloidal spheres in electrolyte solutions

How colloidal particles interact with each other is one of the key issues that determines our ability to interpret experimental results for phase transitions in colloidal dispersions and our ability to apply colloid science to various industrial processes. The long-accepted theories for answering this question have been challenged by results from recent experiments. Herein we show from Monte-Carlo simulations that there is a short-range attractive force between identical macroions in electrolyte solutions containing divalent counterions. Complementing some recent and related results by others, we present strong evidence of attraction between a pair of spherical macroions in the presence of added salt ions for the conditions where the interacting macroion pair is not affected by any other macroions that may be in the solution. This attractive force follows from the internal-energy contribution of counterion mediation. Contrary to conventional expectations, for charged macroions in an electrolyte solution, the entropic force is repulsive at most solution conditions because of localization of small ions in the vicinity of macroions. Both Derjaguin–Landau–Verwey–Overbeek theory and Sogami–Ise theory fail to describe the attractive interactions found in our simulations; the former predicts only repulsive interaction and the latter predicts a long-range attraction that is too weak and occurs at macroion separations that are too large. Our simulations provide fundamental “data” toward an improved theory for the potential of mean force as required for optimum design of new materials including those containing nanoparticles.

BIND—The Biomolecular Interaction Network Database

The Biomolecular Interaction Network Database (BIND; http://binddb.org) is a database designed to store full descriptions of interactions, molecular complexes and pathways. Development of the BIND 2.0 data model has led to the incorporation of virtually all components of molecular mechanisms including interactions between any two molecules composed of proteins, nucleic acids and small molecules. Chemical reactions, photochemical activation and conformational changes can also be described. Everything from small molecule biochemistry to signal transduction is abstracted in such a way that graph theory methods may be applied for data mining. The database can be used to study networks of interactions, to map pathways across taxonomic branches and to generate information for kinetic simulations. BIND anticipates the coming large influx of interaction information from high-throughput proteomics efforts including detailed information about post-translational modifications from mass spectrometry. Version 2.0 of the BIND data model is discussed as well as implementation, content and the open nature of the BIND project. The BIND data specification is available as ASN.1 and XML DTD.