Exclusion of phospholipases (PLs)-producing bacteria in raw milk flushed with nitrogen gas (N(2))

Prolonged cold storage of raw milks favors the growth of psychrotrophs, which produce heat-resistant exoenzymes of considerable spoilage potential; the bacterial proteases and lipases affect raw milk quality; among them phospholipases (PLs) may target the milk fat globule. More importantly, bacterial PLs are key virulence factors for numerous species. Two studies examined the use of nitrogen (N(2)) gas and examined its effect on psychrotrophs, proteases and lipase producers when the milk was stored in closed vessels; however, the effect on PLs producers is unknown. Here we show that by considering an open system the PLs producers were sooner or later excluded in raw milk (whereas the PLs producers in the non-treated controls culminated at 10(8)CFU/ml), by effective gas treatments that bring oxygen (O(2)) levels in milk lower than 0.1ppm. No increase of the PLs producers among the anaerobes was noticed during the course of the experiments. In the experiments performed at 6.0 degrees C, the delay after which the PLs producers were no longer detectable seemed independent of the initial level of PLs producers in raw milk (lower than 10(3)CFU/ml). We anticipate that flushing pure N(2) gas in raw milk tanks, considered as open systems, along the cold chain of raw milk storage and transportation, may be an additional technique to control psychrotrophs, and may also constitute an interesting perspective for limiting their spoilage and pathogenic potential in food materials in general.     

PATIKA: an integrated visual environment for collaborative construction and analysis of cellular pathways

MOTIVATION: Availability of the sequences of entire genomes shifts the scientific curiosity towards the identification of function of the genomes in large scale as in genome studies. In the near future, data produced about cellular processes at molecular level will accumulate with an accelerating rate as a result of proteomics studies. In this regard, it is essential to develop tools for storing, integrating, accessing, and analyzing this data effectively. RESULTS: We define an ontology for a comprehensive representation of cellular events. The ontology presented here enables integration of fragmented or incomplete pathway information and supports manipulation and incorporation of the stored data, as well as multiple levels of abstraction. Based on this ontology, we present the architecture of an integrated environment named Patika (Pathway Analysis Tool for Integration and Knowledge Acquisition). Patika is composed of a server-side, scalable, object-oriented database and client-side editors to provide an integrated, multi-user environment for visualizing and manipulating network of cellular events. This tool features automated pathway layout, functional computation support, advanced querying and a user-friendly graphical interface. We expect that Patika will be a valuable tool for rapid knowledge acquisition, microarray generated large-scale data interpretation, disease gene identification, and drug development. AVAILABILITY: A prototype of Patika is available upon request from the authors.     

Computational basis of knowledge-based conformational probabilities derived from local- and long-range interactions in proteins

The probabilities of the various basins in Ramachandran maps are examined critically. The theoretical basis of probability calculations both from molecular computations and from protein libraries are discussed. The well-defined basins of the Ramachandran maps are treated as rotational isomeric states. Statistical independence and dependence of the states of different residues along the peptide chain are discussed. The Flory isolated pair hypothesis, near neighbor correlations, context effects, and long-range correlations are examined critically. A method of evaluating long-range correlations in helical and extended sequences is introduced in analogy with earlier polymer theory. Three different protein libraries are constructed where data is considered from residues in the (i) coiled regions, (ii) all regions, and (iii) only the helical and extended regions of proteins. Singlet and pairwise dependent probabilities calculated from these libraries are used to predict whether a given sequence is helical or extended. Predictions using pairwise dependence were not better than those using singlet probabilities. Modeling of long-range correlations improved the predictions significantly. Removal of the Chameleon sequences from the data set also improved the predictions, but to a lesser extent.     

A Structural View of Negative Regulation of the Toll-like Receptor-Mediated Inflammatory Pathway

Even though the Toll-like receptor (TLR) pathway is integral to inflammatory defense mechanisms, its excessive signaling may be devastating. Cells have acquired a cascade of strategies to regulate TLR signaling by targeting protein-protein interactions, or ubiquitin chains, but the details of the inhibition mechanisms are still unclear. Here, we provide the structural basis for the regulation of TLR signaling by constructing architectures of protein-protein interactions. Structural data suggest that 1) Toll/IL-1R (TIR) domain-containing regulators (BCAP, SIGIRR, and ST2) interfere with TIR domain signalosome formation; 2) major deubiquitinases such as A20, CYLD, and DUBA prevent association of TRAF6 and TRAF3 with their partners, in addition to removing K63-linked ubiquitin chains that serve as a docking platform for downstream effectors; 3) alternative downstream pathways of TLRs also restrict signaling by competing to bind common partners through shared binding sites. We also performed in silico mutagenesis analysis to characterize the effects of oncogenic mutations on the negative regulators and to observe the cellular outcome (whether there is/is not inflammation). Missense mutations that fall on interfaces and nonsense/frameshift mutations that result in truncated negative regulators disrupt the interactions with the targets, thereby enabling constitutive activation of the nuclear factor-kappa B, and contributing to chronic inflammation, autoimmune diseases, and oncogenesis.     

Molecular recognition of H3/H4 histone tails by the tudor domains of JMJD2A: a comparative molecular dynamics simulations study

Background

Histone demethylase, JMJD2A, specifically recognizes and binds to methylated lysine residues at histone H3 and H4 tails (especially trimethylated H3K4 (H3K4me3), trimethylated H3K9 (H3K9me3) and di,trimethylated H4K20 (H4K20me2, H4K20me3)) via its tandem tudor domains. Crystal structures of JMJD2A-tudor binding to H3K4me3 and H4K20me3 peptides are available whereas the others are not. Complete picture of the recognition of the four histone peptides by the tandem tudor domains yet remains to be clarified.

Methodology/Principal Findings

We report a detailed molecular dynamics simulation and binding energy analysis of the recognition of JMJD2A-tudor with four different histone tails. 25 ns fully unrestrained molecular dynamics simulations are carried out for each of the bound and free structures. We investigate the important hydrogen bonds and electrostatic interactions between the tudor domains and the peptide molecules and identify the critical residues that stabilize the complexes. Our binding free energy calculations show that H4K20me2 and H3K9me3 peptides have the highest and lowest affinity to JMJD2A-tudor, respectively. We also show that H4K20me2 peptide adopts the same binding mode with H4K20me3 peptide, and H3K9me3 peptide adopts the same binding mode with H3K4me3 peptide. Decomposition of the enthalpic and the entropic contributions to the binding free energies indicate that the recognition of the histone peptides is mainly driven by favourable van der Waals interactions. Residue decomposition of the binding free energies with backbone and side chain contributions as well as their energetic constituents identify the hotspots in the binding interface of the structures.

Conclusion

Energetic investigations of the four complexes suggest that many of the residues involved in the interactions are common. However, we found two receptor residues that were related to selective binding of the H3 and H4 ligands. Modifications or mutations on one of these residues can selectively alter the recognition of the H3 tails or the H4 tails.     

Integrating Structure to Protein-Protein Interaction Networks That Drive Metastasis to Brain and Lung in Breast Cancer

Blocking specific protein interactions can lead to human diseases. Accordingly, protein interactions and the structural knowledge on interacting surfaces of proteins (interfaces) have an important role in predicting the genotype-phenotype relationship. We have built the phenotype specific sub-networks of protein-protein interactions (PPIs) involving the relevant genes responsible for lung and brain metastasis from primary tumor in breast cancer. First, we selected the PPIs most relevant to metastasis causing genes (seed genes), by using the “guilt-by-association” principle. Then, we modeled structures of the interactions whose complex forms are not available in Protein Databank (PDB). Finally, we mapped mutations to interface structures (real and modeled), in order to spot the interactions that might be manipulated by these mutations. Functional analyses performed on these sub-networks revealed the potential relationship between immune system-infectious diseases and lung metastasis progression, but this connection was not observed significantly in the brain metastasis. Besides, structural analyses showed that some PPI interfaces in both metastasis sub-networks are originating from microbial proteins, which in turn were mostly related with cell adhesion. Cell adhesion is a key mechanism in metastasis, therefore these PPIs may be involved in similar molecular pathways that are shared by infectious disease and metastasis. Finally, by mapping the mutations and amino acid variations on the interface regions of the proteins in the metastasis sub-networks we found evidence for some mutations to be involved in the mechanisms differentiating the type of the metastasis.     

Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces

The vast majority of the chores in the living cell involve protein-protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein-protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations.     

Investigation of the mechanism of nicotine-induced relaxation on the sheep sphincter of Oddi

Possible mechanisms for nicotine-induced relaxation were investigated in the isolated sheep's sphincter of Oddi. Sheep's sphincter of Oddi rings were mounted in tissue bath with modified Krebs-Henseleit solution and aerated with 95% oxygen and 5% carbon dioxide. Tension was measured with isometric force transducers, and muscle relaxation was expressed as percent decrease of precontraction induced by carbachol. Nicotine (1 x 10(-5) to 3 x 10(-3) mol/L) produced concentration-dependent relaxation on sphincter of Oddi precontracted by carbachol (10(-6) mol/L). Nicotine-induced relaxation was 72.8 +/- 4.2% of precontraction with carbachol (10(-6) mol/L) (mean pD2 value, 3.76 +/- 0.05 mol/L). Nicotine-induced relaxation was not affected by N(w)-nitro L-arginine methyl ester (L-NAME) (3 x 10(-5) mol/L), methylene blue (10(-5) mol/L), indomethacin (10(-5) mol/L), hexamethonium (10(-5) mol/L), glibenclamide (10(-5) mol/L), 4-aminopyridine (10(-3) mol/L), tetraethylammonium (3 x 10(-4) mol/L), clotrimazole (10(-6) mol/L), 5-nitro-2-(3-phenylpropylamino) benzoic acid (NPPB) (10(-6) mol/L), and anthracene-9-carboxylate (9-AC) (10(-6) mol/L), but potentiated by bupivacain (10(-5) mol/L). A calcium-antagonizing effect of nicotine was not observed. The results suggest that nicotine-induced relaxation of the sheep's sphincter of Oddi is not mediated by the release of prostaglandins, nitric oxide (NO), or a related substance; by the activation of potassium channels or chloride channels; or by the stimulation of nicotinic cholinoceptors. Potentiation of the nicotine-induced relaxation by bupivacain indicates that blockade of sodium channels may play a role in this relaxation.     

Architectures and functional coverage of protein-protein interfaces

The diverse range of cellular functions is performed by a limited number of protein folds existing in nature. One may similarly expect that cellular functional diversity would be covered by a limited number of protein-protein interface architectures. Here, we present 8205 interface clusters, each representing a unique interface architecture. This data set of protein-protein interfaces is analyzed and compared with older data sets. We observe that the number of both biological and crystal interfaces increases significantly compared to the number of Protein Data Bank entries. Furthermore, we find that the number of distinct interface architectures grows at a much faster rate than the number of folds and is yet to level off. We further analyze the growth trend of the functional coverage by constructing functional interaction networks from interfaces. The functional coverage is also found to steadily increase. Interestingly, we also observe that despite the diversity of interface architectures, some are more favorable and frequently used, and of particular interest, are the ones that are also preferred in single chains.     

Prediction of protein-protein interactions by combining structure and sequence conservation in protein interfaces

MOTIVATION: Elucidation of the full network of protein-protein interactions is crucial for understanding of the principles of biological systems and processes. Thus, there is a need for in silico methods for predicting interactions. We present a novel algorithm for automated prediction of protein-protein interactions that employs a unique bottom-up approach combining structure and sequence conservation in protein interfaces. RESULTS: Running the algorithm on a template dataset of 67 interfaces and a sequentially non-redundant dataset of 6170 protein structures, 62 616 potential interactions are predicted. These interactions are compared with the ones in two publicly available interaction databases (Database of Interacting Proteins and Biomolecular Interaction Network Database) and also the Protein Data Bank. A significant number of predictions are verified in these databases. The unverified ones may correspond to (1) interactions that are not covered in these databases but known in literature, (2) unknown interactions that actually occur in nature and (3) interactions that do not occur naturally but may possibly be realized synthetically in laboratory conditions. Some unverified interactions, supported significantly with studies found in the literature, are discussed. AVAILABILITY: http://gordion.hpc.eng.ku.edu.tr/prism CONTACT: agursoy@ku.edu.tr; okeskin@ku.edu.tr.