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Motions related to protein-protein binding events can be surveyed from the perspective of the Database of Macromolecular Movements. There are a number of alternative conceptual models that describe these events, particularly induced fit and pre-existing equilibrium. There is evidence for both alternatives from recent studies of conformational change. However, there is increasing support for the pre-existing equilibrium model, whereby proteins are found to simultaneously exist in populations of diverse conformations.

We present version 2 of the SPINE system for structural proteomics. SPINE is available over the web at http://nesg.org. It serves as the central hub for the Northeast Structural Genomics Consortium, allowing collaborative structural proteomics to be carried out in a distributed fashion. The core of SPINE is a laboratory information management system (LIMS) for key bits of information related to the progress of the consortium in cloning, expressing and purifying proteins and then solving their structures by NMR or X-ray crystallography. Originally, SPINE focused on tracking constructs, but, in its current form, it is able to track target sample tubes and store detailed sample histories. The core database comprises a set of standard relational tables and a data dictionary that form an initial ontology for proteomic properties and provide a framework for large-scale data mining. Moreover, SPINE sits at the center of a federation of interoperable information resources. These can be divided into (i) local resources closely coupled with SPINE that enable it to handle less standardized information (e.g. integrated mailing and publication lists), (ii) other information resources in the NESG consortium that are inter-linked with SPINE (e.g. crystallization LIMS local to particular laboratories) and (iii) international archival resources that SPINE links to and passes on information to (e.g. TargetDB at the PDB).

Protein-protein interactions play crucial roles in biological processes. Experimental methods have been developed to survey the proteome for interacting partners and some computational approaches have been developed to extend the impact of these experimental methods. Computational methods are routinely applied to newly discovered genes to infer protein function and plausible protein-protein interactions. Here, we develop and extend a quantitative method that identifies interacting proteins based upon the correlated behavior of the evolutionary histories of protein ligands and their receptors. We have studied six families of ligand-receptor pairs including: the syntaxin/Unc-18 family, the GPCR/G-alpha's, the TGF-beta/TGF-beta receptor system, the immunity/colicin domain collection from bacteria, the chemokine/chemokine receptors, and the VEGF/VEGF receptor family. For correlation scores above a defined threshold, we were able to find an average of 79% of all known binding partners. We then applied this method to find plausible binding partners for proteins with uncharacterized binding specificities in the syntaxin/Unc-18 protein and TGF-beta/TGF-beta receptor families. Analysis of the results shows that co-evolutionary analysis of interacting protein families can reduce the search space for identifying binding partners by not only finding binding partners for uncharacterized proteins but also recognizing potentially new binding partners for previously characterized proteins. We believe that correlated evolutionary histories provide a route to exploit the wealth of whole genome sequences and recent systematic proteomic results to extend the impact of these studies and focus experimental efforts to categorize physiologically or pathologically relevant protein-protein interactions.

The divergent evolution of proteins in cellular signaling pathways requires ligands and their receptors to co-evolve, creating new pathways when a new receptor is activated by a new ligand. However, information about the evolution of binding specificity in ligand-receptor systems is difficult to glean from sequences alone. We have used phosphoglycerate kinase (PGK), an enzyme that forms its active site between its two domains, to develop a standard for measuring the co-evolution of interacting proteins. The N-terminal and C-terminal domains of PGK form the active site at their interface and are covalently linked. Therefore, they must have co-evolved to preserve enzyme function. By building two phylogenetic trees from multiple sequence alignments of each of the two domains of PGK, we have calculated a correlation coefficient for the two trees that quantifies the co-evolution of the two domains. The correlation coefficient for the trees of the two domains of PGK is 0. 79, which establishes an upper bound for the co-evolution of a protein domain with its binding partner. The analysis is extended to ligands and their receptors, using the chemokines as a model. We show that the correlation between the chemokine ligand and receptor trees' distances is 0.57. The chemokine family of protein ligands and their G-protein coupled receptors have co-evolved so that each subgroup of chemokine ligands has a matching subgroup of chemokine receptors. The matching subfamilies of ligands and their receptors create a framework within which the ligands of orphan chemokine receptors can be more easily determined. This approach can be applied to a variety of ligand and receptor systems.

More than half of malignant gliomas reportedly have alterations in the p53 tumor suppressor gene. Because p53 plays a key role in the cellular response to DNA-damaging agents, we investigated the role of p53 gene therapy before ionizing radiation in cultured human glioma cells containing normal or mutated p53. Three established human glioma cell lines expressing the wild-type (U87 MG, p53wt) or mutant (A172 and U373 MG, p53mut) p53 gene were transduced by recombinant adenoviral vectors bearing human p53 (Adp53) and Escherichia coli beta-galactosidase genes (AdLacZ, control virus) before radiation (0-20 Gy). Changes in p53, p21, and Bax expression were studied by Western immunoblotting, whereas cell cycle alterations and apoptosis were investigated by flow cytometry and nuclear staining. Survival was assessed by clonogenic assays. Within 48 hours of Adp53 exposure, all three cell lines demonstrated p53 expression at a viral multiplicity of infection of 100. p21, which is a p53-inducible downstream effector gene, was overexpressed, and cells were arrested in the G1 phase. Bax expression, which is thought to play a role in p53-induced apoptosis, did not change with either radiation or Adp53. Apoptosis and survival after p53 gene therapy varied. U87 MG (p53wt) cells showed minimal apoptosis after Adp53, irradiation, or combined treatments. U373 MG (p53mut) cells underwent massive apoptosis and died within 48 hours of Adp53 treatment, independent of irradiation. Surprisingly, A172 (p53mut) cells demonstrated minimal apoptosis after Adp53 exposure; however, unlike U373 MG cells, apoptosis increased with radiation dose. Survival of all three cell lines was reduced dramatically after >10 Gy. Although Adp53 transduction significantly reduced the survival of U373 MG cells and inhibited A172 growth, it had no effect on the U87 MG cell line. Transduction with AdLacZ did not affect apoptosis or cell cycle progression and only minimally affected survival in all cell lines. We conclude that responses to p53 gene therapy are variable among gliomas and most likely depend upon both cellular p53 status and as yet ill-defined downstream pathways involving activation of cell cycle regulatory and apoptotic genes.

With mounting availability of genomic and phenotypic databases, data integration and mining become increasingly challenging. While efforts have been put forward to analyze prokaryotic phenotypes, current computational technologies either lack high throughput capacity for genomic scale analysis, or are limited in their capability to integrate and mine data across different scales of biology. Consequently, simultaneous analysis of associations among genomes, phenotypes, and gene functions is prohibited. Here, we developed a high throughput computational approach, and demonstrated for the first time the feasibility of integrating large quantities of prokaryotic phenotypes along with genomic datasets for mining across multiple scales of biology (protein domains, pathways, molecular functions, and cellular processes). Applying this method over 59 fully sequenced prokaryotic species, we identified genetic basis and molecular mechanisms underlying the phenotypes in bacteria. We identified 3,711 significant correlations between 1,499 distinct Pfam and 63 phenotypes, with 2,650 correlations and 1,061 anti-correlations. Manual evaluation of a random sample of these significant correlations showed a minimal precision of 30% (95% confidence interval: 20%–42%; n = 50). We stratified the most significant 478 predictions and subjected 100 to manual evaluation, of which 60 were corroborated in the literature. We furthermore unveiled 10 significant correlations between phenotypes and KEGG pathways, eight of which were corroborated in the evaluation, and 309 significant correlations between phenotypes and 166 GO concepts evaluated using a random sample (minimal precision = 72%; 95% confidence interval: 60%–80%; n = 50). Additionally, we conducted a novel large-scale phenomic visualization analysis to provide insight into the modular nature of common molecular mechanisms spanning multiple biological scales and reused by related phenotypes (metaphenotypes). We propose that this method elucidates which classes of molecular mechanisms are associated with phenotypes or metaphenotypes and holds promise in facilitating a computable systems biology approach to genomic and biomedical research., Synopsis A key challenge of the post-genomic era is to conceive large-scale studies of genomes and observable characteristics of organisms (phenotypes) and to interpret the data thus produced. The goal of this “phenomic” study is to improve our understanding of complex biological systems in terms of their molecular underpinnings. In this paper, Liu and colleagues present comprehensive computational and novel visualization methods for discovering biological knowledge spanning multiple scales of biology. The authors were able to predict and visualize new knowledge between clusters of microbiological phenotypes and their molecular mechanisms. To their knowledge, this is the first time this has been done. More specifically, the method integrates microbiological data with genomic-scale data from protein family databases, gene ontology, and biological pathways. Conducted over 59 fully sequenced bacteria, and including significantly more phenotypes than previous studies of its kind, this study enables a “systems biology” view across different classifications of genes and processes. This represents advancement over previous techniques, which are either limited in biological scale or analytical breadth. Visualization of the networks generated by this technique shows the common biological modules shared by related phenotypes. The results of this experiment demonstrate that the fusion of clinical data with genomic information is able to elucidate, in high throughput, a massive number of biological processes underlying phenotypes.

Background The ability to rapidly characterize an unknown microorganism is critical in both responding to infectious disease and biodefense. To do this, we need some way of anticipating an organism's phenotype based on the molecules encoded by its genome. However, the link between molecular composition (i.e. genotype) and phenotype for microbes is not obvious. While there have been several studies that address this challenge, none have yet proposed a large-scale method integrating curated biological information. Here we utilize a systematic approach to discover genotype-phenotype associations that combines phenotypic information from a biomedical informatics database, GIDEON, with the molecular information contained in National Center for Biotechnology Information's Clusters of Orthologous Groups database (NCBI COGs). Results Integrating the information in the two databases, we are able to correlate the presence or absence of a given protein in a microbe with its phenotype as measured by certain morphological characteristics or survival in a particular growth media. With a 0.8 correlation score threshold, 66% of the associations found were confirmed by the literature and at a 0.9 correlation threshold, 86% were positively verified. Conclusion Our results suggest possible phenotypic manifestations for proteins biochemically associated with sugar metabolism and electron transport. Moreover, we believe our approach can be extended to linking pathogenic phenotypes with functionally related proteins.

We review recent computational advances in the study of membrane proteins, focusing on those that have at least one transmembrane helix. Transmembrane protein regions are, in many respects, easier to investigate computationally than experimentally, due to the uniformity of their structure and interactions (e.g. consisting predominately of nearly parallel helices packed together) on one hand and presenting the challenges of solubility on the other. We present the progress made on identifying and classifying membrane proteins into families, predicting their structure from amino-acid sequence patterns (using many different methods), and analyzing their interactions and packing. The total result of this work allows us for the first time to begin to think about the membrane protein interactome, the set of all interactions between distinct transmembrane helices in the lipid bilayer.

Structural genomics projects represent major undertakings that will change our understanding of proteins. They generate unique datasets that, for the first time, present a standardized view of proteins in terms of their physical and chemical properties. By analyzing these datasets here, we are able to discover correlations between a protein's characteristics and its progress through each stage of the structural genomics pipeline, from cloning, expression, purification, and ultimately to structural determination. First, we use tree-based analyses (decision trees and random forest algorithms) to discover the most significant protein features that influence a protein's amenability to high-throughput experimentation. Based on this, we identify potential bottlenecks in various stages of the structural genomics process through specialized “pipeline schematics”. We find that the properties of a protein that are most significant are: (i) whether it is conserved across many organisms; (ii) the percentage composition of charged residues; (iii) the occurrence of hydrophobic patches; (iv) the number of binding partners it has; and (v) its length. Conversely, a number of other properties that might have been thought to be important, such as nuclear localization signals, are not significant. Thus, using our tree-based analyses, we are able to identify combinations of features that best differentiate the small group of proteins for which a structure has been determined from all the currently selected targets. This information may prove useful in optimizing high-throughput experimentation. Further information is available from http://mining.nesg.org/ .

Homologues of barley Mlo encode the only family of seven-transmembrane (TM) proteins in plants. Their topology, subcellular localization, and sequence diversification are reminiscent of those of G-protein coupled receptors (GPCRs) from animals and fungi. We present a computational analysis of MLO family members based on 31 full-size and 3 partial sequences, which originate from several monocot species, the dicot Arabidopsis thaliana, and the moss Ceratodon purpureus. This enabled us to date the origin of the Mlo gene family back at least to the early stages of land plant evolution. The genomic organization of the corresponding genes supports a monophyletic origin of the Mlo gene family. Phylo-genetic analysis revealed five clades, of which three contain both monocot and dicot members, while two indicate class-specific diversification. Analysis of the ratio of nonsynonymous-to-synonymous changes in coding sequences provided evidence for functional constraint on the evolution of the DNA sequences and purifying selection, which appears to be reduced in the first extracellular loop of 12 closely related orthologues. The 31 full-size sequences were examined for potential domain-specific intramolecular coevolution. This revealed evidence for concerted evolution of all three cytoplasmic domains with each other and the C-terminal cytoplasmic tail, suggesting interplay of all intracellular domains for MLO function.

Object. The goal of this study was to evaluate gene delivery to a benign brain tumor. Methods. A recombinant adenovirus vector bearing the Escherichia coli β-galactosidase reporter gene was selectively injected into the vascular supply of a spontaneously occurring canine olfactory groove meningioma. The tumor and a small amount of peritumoral brain tissue were removed 5 days after viral injection and stained with X-Gal to assess gene delivery. The authors noted significant β-galactosidase gene expression by the tumor, but not by surrounding brain tissue. No obvious viral-related cytotoxicity was noted. Conclusions. The authors found that meningiomas can be successfully transduced by adenovirus vectors by using endovascular techniques.

The aim of this research was to determine whether early maturation of the dorsal hippocampal cholinergic system mediates behavior exhibited by preweanling rats in the presence or absence of an unfamiliar adult male rat, a threatening stimulus. The behavioral responses that were examined included behavioral inhibition or freezing which emerges at 2 weeks of age and ultrasonic vocalizations. Prior to behavioral testing, oxotremorine, an M2 muscarinic receptor agonist that reduces cholinergic release from presynaptic terminals, was infused into the dorsal hippocampal dentate gyrus. Results demonstrated that 14-day-old rats with bilateral hippocampal infusions of a 1 microgram dose of oxotremorine exhibited significant deficits in freezing when exposed to the adult male rat. Importantly, oxotremorine had no significant effects on ultrasound emission and ambulatory activity when rat pups were tested in social isolation. Thus, effects of oxotremorine in the hippocampal dentate gyrus do not produce global changes in behavior. Results suggest that cholinergic release into the dorsal hippocampus facilitates the display of behavioral inhibition at the end of the second postnatal week. Behavioral deficits in freezing may reflect an oxotremorine-induced disruption of hippocampal cholinergic function underlying the processing of biologically relevant olfactory stimuli as well as mechanisms associated with attention.