Identifying subset errors in multiple sequence alignments.

Multiple sequence alignment (MSA) accuracy is important, but there is no widely accepted method of judging the accuracy that different alignment algorithms give. We present a simple approach to detecting two types of error, namely block shifts and the misplacement of residues within a gap. Given a MSA, subsets of very similar sequences are generated through the use of a redundancy filter, typically using a 70-90% sequence identity cut-off. Subsets thus produced are typically small and degenerate, and errors can be easily detected even by manual examination. The errors, albeit minor, are inevitably associated with gaps in the alignment, and so the procedure is particularly relevant to homology modelling of protein loop regions. The usefulness of the approach is illustrated in the context of the universal but little known [K/R]KLH motif that occurs in intracellular loop 1 of G protein coupled receptors (GPCR); other issues relevant to GPCR modelling are also discussed.

Modeling active GPCR conformations.

The most significant advance in modeling GPCR active states has been the ß(2)-adrenergic receptor-Gs complex as this essentially transforms active-state modeling into homology modeling. Various different molecular dynamics-based approaches for modeling active states are presented, and a number of key applications discussed. These simulations have given insights into the activation pathway, conformational changes, dimerization, hydration, the ionic lock, ligand binding, protonation, and sodium binding. Crystallography and simulations have shown that the presence of agonist alone is unlikely to be sufficient to form the active state and that restraints applied to the G protein-binding region are required. The role of various microswitches in activation is discussed, including the controversial rotamer toggle switch. The importance of explicitly simulating experimental molecular probes to understand activation is highlighted, along with the need to ensure that such molecules are well parameterized. Approaches to loop modeling are discussed. We argue that the role of successful virtual screening against active models should not be overestimated as the main conformational changes on activation occur in the intracellular region.

G-protein-coupled receptor dynamics: dimerization and activation models compared with experiment.

Our previously derived models of the active state of the ß2-adrenergic receptor are compared with recently published X-ray crystallographic structures of activated GPCRs (G-protein-coupled receptors). These molecular dynamics-based models using experimental data derived from biophysical experiments on activation were used to restrain the receptor to an active state that gave high enrichment for agonists in virtual screening. The ß2-adrenergic receptor active model and X-ray structures are in good agreement over both the transmembrane region and the orthosteric binding site, although in some regions the active model is more similar to the active rhodopsin X-ray structures. The general features of the microswitches were well reproduced, but with minor differences, partly because of the unexpected X-ray results for the rotamer toggle switch. In addition, most of the interacting residues between the receptor and the G-protein were identified. This analysis of the modelling has also given important additional insight into GPCR dimerization: re-analysis of results on photoaffinity analogues of rhodopsin provided additional evidence that TM4 (transmembrane helix 4) resides at the dimer interface and that ligands such as bivalent ligands may pass between the mobile helices. A comparison, and discussion, is also carried out between the use of implicit and explicit solvent for active-state modelling.

Modeling GPCR active state conformations: the ß(2)-adrenergic receptor.

The recent publication of several G protein-coupled receptor (GPCR) structures has increased the information available for homology modeling inactive class A GPCRs. Moreover, the opsin crystal structure shows some active features. We have therefore combined information from these two sources to generate an extensively validated model of the active conformation of the ß(2)-adrenergic receptor. Experimental information on fully active GPCRs from zinc binding studies, site-directed spin labeling, and other spectroscopic techniques has been used in molecular dynamics simulations. The observed conformational changes reside mainly in transmembrane helix 6 (TM6), with additional small but significant changes in TM5 and TM7. The active model has been validated by manual docking and is in agreement with a large amount of experimental work, including site-directed mutagenesis information. Virtual screening experiments show that the models are selective for ß-adrenergic agonists over other GPCR ligands, for (R)- over (S)-ß-hydroxy agonists and for ß(2)-selective agonists over ß(1)-selective agonists. The virtual screens reproduce interactions similar to those generated by manual docking. The C-terminal peptide from a model of the stimulatory G protein, readily docks into the active model in a similar manner to which the C-terminal peptide from transducin, docks into opsin, as shown in a recent opsin crystal structure. This GPCR-G protein model has been used to explain site-directed mutagenesis data on activation. The agreement with experiment suggests a robust model of an active state of the ß(2)-adrenergic receptor has been produced. The methodology used here should be transferable to modeling the active state of other GPCRs.

Bioinformatics and molecular modelling approaches to GPCR oligomerization.

The elusive nature of the structure and function of the G-protein coupled receptor (GPCR) dimer or oligomer has led to a variety of computational studies, most of which have been directed primarily towards understanding structure. Here we review some of the recent studies based on sequence analysis and docking experiments and the recent developments in GPCR structure that have underpinned dimerization studies. In addition, we review recent nanosecond molecular dynamics simulations and coarse-grained methods for investigating the dynamic consequences of dimerization. The strengths and weaknesses of these complementary methods are discussed. The consensus of a variety of studies is that several transmembrane helices are involved in the dimerization/oligomerization interface(s); computation has been particularly effective in elucidating the experiments that seem to indicate a key role for transmembrane helix 4.