G protein-coupled receptors (GPCRs) participate in diverse physiological processes, ranging from sensory responses such as vision, taste and smell to those regulating behavior, the immune and the cardiac system among others. The $\sim 800$ human GPCRs sense diverse signaling molecules such as hormones and neurotransmitters to allosterically activate the associated G proteins, which in turn regulate diverse intracellular signaling pathways. In this manner, GPCRs regulate virtually every aspect of human physiology. Not surprisingly, GPCRs are the targets of over one-third of all prescribed human drugs.

Alternative splicing can expand the diversity of proteomes. Homologous mutually exclusive exons (MXEs) originate from the same ancestral exon and result in polypeptides with similar structural properties but altered sequence. Why would some genes switch homologous exons and what are their biological impact? Here, we analyse the extent of sequence, structural and functional variability in MXEs and report the first large scale, structure-based analysis of the biological impact of MXE events from different genomes. MXE-specific residues tend to map to single domains, are highly enriched in surface exposed residues and cluster at or near protein functional sites. Thus, MXE events are likely to maintain the protein fold, but alter specificity and selectivity of protein function. This comprehensive resource of MXE events and their annotations is available at: http://gene3d.biochem.ucl.ac.uk/mxemod/. These findings highlight how small, but significant changes at critical positions on a protein surface are exploited in evolution to alter function.

MOTIVATION: Spial (Specificity in alignments) is a tool for the comparative analysis of two alignments of evolutionarily related sequences that differ in their function, such as two receptor subtypes. It highlights functionally important residues that are either specific to one of the two alignments or conserved across both alignments. It permits visualization of this information in three complementary ways: by colour-coding alignment positions, by sequence logos and optionally by colour-coding the residues of a protein structure provided by the user. This can aid in the detection of residues that are involved in the subtype-specific interaction with a ligand, other proteins or nucleic acids. Spial may also be used to detect residues that may be post-translationally modified in one of the two sets of sequences. AVAILABILITY: http://www.mrc-lmb.cam.ac.uk/genomes/spial/; supplementary information is available at http://www.mrc-lmb.cam.ac.uk/genomes/spial/help.html.

MOTIVATION: Several methods have recently been developed to analyse large-scale sets of physical interactions between proteins in terms of physical contacts between the constituent domains, often with a view to predicting new pairwise interactions. Our aim is to combine genomic interaction data, in which domain-domain contacts are not explicitly reported, with the domain-level structure of individual proteins, in order to learn about the structure of interacting protein pairs. Our approach is driven by the need to assess the evidence for physical contacts between domains in a statistically rigorous way. RESULTS: We develop a statistical approach that assigns p-values to pairs of domain superfamilies, measuring the strength of evidence within a set of protein interactions that domains from these superfamilies form contacts. A set of p-values is calculated for SCOP superfamily pairs, based on a pooled data set of interactions from yeast. These p-values can be used to predict which domains come into contact in an interacting protein pair. This predictive scheme is tested against protein complexes in the Protein Quaternary Structure (PQS) database, and is used to predict domain-domain contacts within 705 interacting protein pairs taken from our pooled data set.

UNLABELLED: Bacterial lipoproteins and lipid modification are gaining importance owing to their essential nature, roles in pathogenesis and interesting commercial applications. We have created an exclusive knowledge base for bacterial lipoproteins by processing information from 510 entries to provide a list of 199 distinct lipoproteins with relevant links to molecular details. Features include functional classification, predictive algorithm for query sequences, primary sequence analysis and lists of predicted lipoproteins from 43 completed bacterial genomes along with interactive information exchange facility. AVAILABILITY: The website called Database Of bacterial LipOProteins (DOLOP) is available at http://www.mrc-lmb.cam.ac.uk/genomes/dolop along with additional information on the biosynthetic pathway, supplementary material and other related figures.