Family resemblances? Ligand binding and activation of family A and B G-protein-coupled receptors

In April 2007, the Biochemical Society held a meeting to compare and contrast ligand binding and activation of Family A and B GPCRs (G-protein-coupled receptors). Being the largest class, Family A GPCRs usually receive the most attention, although a previous Biochemical Society meeting has focused on Family B GPCRs. The aim of the present meeting was to bring researchers of both families together in order to identify commonalities between the two. The present article introduces the proceedings of the meeting, briefly commenting on the focus of each of the following articles. ©The Authors.

Microscale mesoarrays created by dip-pen nanolithography for screening of protein-protein interactions

Using microarrays to probe protein-protein interactions is becoming increasingly attractive due to their compatibility with highly sensitive detection techniques, selectivity of interaction, robustness and capacity for examining multiple proteins simultaneously. The major drawback to using this approach is the relatively large volumes and high concentrations necessary. Reducing the protein array spot size should allow for smaller volumes and lower concentrations to be used as well as opening the way for combination with more sensitive detection technologies. Dip-Pen Nanolithography (DPN) is a recently developed technique for structure creation on the nano to microscale with the capacity to create biological architectures. Here we describe the creation of miniaturised microarrays, 'mesoarrays', using DPN with protein spots 400× smaller by area compared to conventional microarrays. The mesoarrays were then used to probe the ERK2-KSR binding event of the Ras/Raf/MEK/ERK signalling pathway at a physical scale below that previously reported. Whilst the overall assay efficiency was determined to be low, the mesoarrays could detect KSR binding to ERK2 repeatedly and with low non-specific binding. This study serves as a first step towards an approach that can be used for analysis of proteins at a concentration level comparable to that found in the cellular environment.

The heteromeric 5-HT3A/B receptor:the effect of 5-HT3B subunits on receptor structure and ligand recognition

The 5-HT3 receptors are members of the cys-loop family of ligand-gated ion channels. Two functional subtypes are known, the homomeric 5HT3A and the heteromeric 5HT3A/B receptors, which exhibit distinct biophysical characteristics but are difficult to differentiate pharmacologically. Atomic force microscopy has been used to determine the stoichiometry and architecture of the heteromeric 5HT3A/B receptor. Each subunit was engineered to express a unique C-terminal epitope tag, together with six sequential histidine residues to facilitate nickel affinity purification. The 5-HT3 receptors, ectopically expressed in HEK293 cells, were solubilised, purified and decorated with antibodies to the subunit specific epitope tags. Imaging of individual receptors by atomic force microscopy revealed a pentameric arrangement of subunits in the order BBABA, reading anti-clockwise when viewed from the extracellular face. Homology models for the heteromeric receptor were then constructed using both the electron microscopic structure of the nicotinic acetylcholine receptor, from Torpedo marmorata, and the X-ray crystallographic structure of the soluble acetylcholine binding protein, from Lymnaea stagnalis, as templates. These homology models were used, together with equivalent models constructed for the homomeric receptor, to interpret mutagenesis experiments designed to explore the minimal recognition differences of both the natural agonist, 5-HT, and the competitive antagonist, granisetron, for the two human receptor subtypes. The results of this work revealed that the 5-HT3B subunit residues within the ligand binding site, for both the agonist and antagonist, are accommodating to conservative mutations. They are consistent with the view that the 5-HT3A subunit provides the principal and the 5-HT38 subunit the complementary recognition interactions at the binding interface.

Protein-protein interactions in ovalbumin solutions studied by small angle scattering: effect of ionic strength, and the chemical nature of cations

The influence of ionic strength and of the chemical nature of cations on the protein-protein interactions in ovalbumin solution was studied using small-angle X-ray and neutron scattering (SAXS/SANS). The globular protein ovalbumin is found in dimeric form in solutions as suggested by SANS/SAXS experiments. Due to the negative charge of the proteins at neutral pH, the protein-protein interactions without any salt addition are dominated by electrostatic repulsion. A structure factor related to screened Coulombic interactions together with an ellipsoid form factor was used to fit the scattering intensity. A monovalent salt (NaCl) and a trivalent salt (YCl3) were used to study the effect of the chemical nature of cations on the interaction in protein solutions. Upon addition of NaCl, with ionic strength below that of physiological conditions (150 mM), the effective interactions are still dominated by the surface charge of the proteins and the scattering data can be understood using the same model. When yttrium chloride was used, a reentrant condensation behavior, i.e., aggregation and subsequent redissolution of proteins with increasing salt concentration, was observed. SAXS measurements reveal a transition from effective repulsion to attraction with increasing salt concentration. The solutions in the reentrant regime become unstable after long times (several days). The results are discussed and compared with those from bovine serum albumin (BSA) in solutions.

Novel peptide antagonists of adrenomedullin and calcitonin gene-related peptide receptors:identification, pharmacological characterization, and interactions with position 74 in receptor activity-modifying protein 1/3

Human adrenomedullin (AM) is a 52-amino acid peptide belonging to the calcitonin peptide family, which also includes calcitonin gene-related peptide (CGRP) and AM2. The two AM receptors, AM(1) and AM(2), are calcitonin receptor-like receptor (CL)/receptor activity-modifying protein (RAMP) (RAMP2 and RAMP3, respectively) heterodimers. CGRP receptors comprise CL/RAMP1. The only human AM receptor antagonist (AM(22-52)) is a truncated form of AM; it has low affinity and is only weakly selective for AM(1) over AM(2) receptors. To develop novel AM receptor antagonists, we explored the importance of different regions of AM in interactions with AM(1), AM(2), and CGRP receptors. AM(22-52) was the framework for generating further AM fragments (AM(26-52) and AM(30-52)), novel AM/alphaCGRP chimeras (C1-C5 and C9), and AM/AM(2) chimeras (C6-C8). cAMP assays were used to screen the antagonists at all receptors to determine their affinity and selectivity. Circular dichroism spectroscopy was used to investigate the secondary structures of AM and its related peptides. The data indicate that the structures of AM, AM2, and alphaCGRP differ from one another. Our chimeric approach enabled the identification of two nonselective high-affinity antagonists of AM(1), AM(2), and CGRP receptors (C2 and C6), one high-affinity antagonist of AM(2) receptors (C7), and a weak antagonist selective for the CGRP receptor (C5). By use of receptor mutagenesis, we also determined that the C-terminal nine amino acids of AM seem to be responsible for its interaction with Glu74 of RAMP3. We provide new information on the structure-activity relationship of AM, alphaCGRP, and AM2 and how AM interacts with CGRP and AM(2) receptors.

Ligand diffusion on protein surface observed in molecular dynamics simulation

The process of binding of small ligands to dihydrofolate reductase protein has been investigated using all-atom molecular dynamics simulations. The existence of a mechanism that facilitates the search of the binding site by the ligand is demonstrated. The mechanism consists of ligand diffusing on the protein’s surface. It has been discussed in the literature before, but has not been explicitly confirmed for realistic molecular systems. The strength of this nonspecific binding is roughly estimated and found to be essential for the binding kinetics.

Extracting protein-protein interactions from MEDLINE using the hidden vector state model

A major challenge in text mining for biomedicine is automatically extracting protein-protein interactions from the vast amount of biomedical literature. We have constructed an information extraction system based on the Hidden Vector State (HVS) model for protein-protein interactions. The HVS model can be trained using only lightly annotated data whilst simultaneously retaining sufficient ability to capture the hierarchical structure. When applied in extracting protein-protein interactions, we found that it performed better than other established statistical methods and achieved 61.5% in F-score with balanced recall and precision values. Moreover, the statistical nature of the pure data-driven HVS model makes it intrinsically robust and it can be easily adapted to other domains.

Ontology-based protein-protein interactions extraction from literature using the hidden vector state model

This paper proposes a novel framework of incorporating protein-protein interactions (PPI) ontology knowledge into PPI extraction from biomedical literature in order to address the emerging challenges of deep natural language understanding. It is built upon the existing work on relation extraction using the Hidden Vector State (HVS) model. The HVS model belongs to the category of statistical learning methods. It can be trained directly from un-annotated data in a constrained way whilst at the same time being able to capture the underlying named entity relationships. However, it is difficult to incorporate background knowledge or non-local information into the HVS model. This paper proposes to represent the HVS model as a conditionally trained undirected graphical model in which non-local features derived from PPI ontology through inference would be easily incorporated. The seamless fusion of ontology inference with statistical learning produces a new paradigm to information extraction.