Predicting protein--protein interactions from primary structure

MOTIVATION: An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. The expectation is that this will provide a fuller appreciation of cellular processes and networks at the protein level, ultimately leading to a better understanding of disease mechanisms and suggesting new means for intervention. This paper addresses the question: can protein-protein interactions be predicted directly from primary structure and associated data? Using a diverse database of known protein interactions, a Support Vector Machine (SVM) learning system was trained to recognize and predict interactions based solely on primary structure and associated physicochemical properties. RESULTS: Inductive accuracy of the trained system, defined here as the percentage of correct protein interaction predictions for previously unseen test sets, averaged 80% for the ensemble of statistical experiments. Future proteomics studies may benefit from this research by proceeding directly from the automated identification of a cell's gene products to prediction of protein interaction pairs.     

Hot regions in protein--protein interactions: the organization and contribution of structurally conserved hot spot residues

Structurally conserved residues at protein-protein interfaces correlate with the experimental alanine-scanning hot spots. Here, we investigate the organization of these conserved, computational hot spots and their contribution to the stability of protein associations. We find that computational hot spots are not homogeneously distributed along the protein interfaces; rather they are clustered within locally tightly packed regions. Within the dense clusters, they form a network of interactions and consequently their contributions to the stability of the complex are cooperative; however the contributions of independent clusters are additive. This suggests that the binding free energy is not a simple summation of the single hot spot residue contributions. As expected, around the hot spot residues we observe moderately conserved residues, further highlighting the crucial role of the conserved interactions in the local densely packed environment. The conserved occurrence of these organizations suggests that they are advantageous for protein-protein associations. Interestingly, the total number of hydrogen bonds and salt bridges contributed by hot spots is as expected. Thus, H-bond forming residues may use a "hot spot for water exclusion" mechanism. Since conserved residues are located within highly packed regions, water molecules are easily removed upon binding, strengthening electrostatic contributions of charge-charge interactions. Hence, the picture that emerges is that protein-protein associations are optimized locally, with the clustered, networked, highly packed structurally conserved residues contributing dominantly and cooperatively to the stability of the complex. When addressing the crucial question of "what are the preferred ways of proteins to associate", these findings point toward a critical involvement of hot regions in protein-protein interactions.     

Cryptic growth within a binary microbial culture

Summary The ability of viable cells of the species Pseudomonas putida and Hyphomicrobium sp. to metabolize the particulate and soluble cellular organic constituents of both species was studied in a series of batch experiments. Both P. putida and Hyphomicrobium sp. were grown in individual batch reactors on either the ¹⁴C-labelled soluble or the particulate debris of sonicated cells of each species derived from steady-state chemostat cultures. Cell generation times (t _g)observed for P. putida cultivated on soluble organic material originating from either sonicated P. putida or Hyphomicrobium sp. cells, were t _g= 2.0 h and t _g= 6.3 h, respectively. Corresponding t _gvalues of Hyphomicrobium sp. on soluble organic material originating from sonicated P. putida and Hyphomicrobium so. were, respectively, 11.6 h and 4.3 h. While particulate debris originating from either species was solubilized by both P. putida and Hyphomicrobium sp., no increases in cell numbers were observed for either species. The data indicate that bacteria are capable of scavenging soluble material released upon cell lysis at near maximal rates; solubilization of debris also occurred but at much slower overall rates with no observable cell replication. The results reaffirm that cryptic growth and turnover of cellular biomass can be significant under situations of low substrate flux or starvation conditions. Offprint requests to: J. D. Bryers     

Path PPI: an integrated dataset of human pathways and protein-protein interactions

Integration of pathway and protein-protein interaction(PPI) data can provide more information that could lead to new biological insights. PPIs are usually represented by a simple binary model, whereas pathways are represented by more complicated models. We developed a series of rules for transforming protein interactions from pathway to binary model, and the protein interactions from seven pathway databases, including PID, Bio Carta, Reactome, Net Path, INOH, SPIKE and KEGG, were transformed based on these rules. These pathway-derived binary protein interactions were integrated with PPIs from other five PPI databases including HPRD, Int Act, Bio GRID, MINT and DIP, to develop integrated dataset(named Path PPI). More detailed interaction type and modification information on protein interactions can be preserved in Path PPI than other existing datasets. Comparison analysis results indicate that most of the interaction overlaps values(OAB) among these pathway databases were less than 5%, and these databases must be used conjunctively. The Path PPI data was provided at http://proteomeview. hupo.org.cn/Path PPI/Path PPI.html.     

Split-protein systems: beyond binary protein-protein interactions

It has been estimated that 650,000 protein-protein interactions exist in the human interactome (Stumpf et al., 2008), a subset of all possible macromolecular partnerships that dictate life. Thus there is a continued need for the development of sensitive and user-friendly methods for cataloguing biomacromolecules in complex environments and for detecting their interactions, modifications, and cellular location. Such methods also allow for establishing differences in the interactome between a normal and diseased cellular state and for quantifying the outcome of therapeutic intervention. A promising approach for deconvoluting the role of macromolecular partnerships is split-protein reassembly, also called protein fragment complementation. This approach relies on the appropriate fragmentation of protein reporters, such as the green fluorescent protein or firefly luciferase, which when attached to possible interacting partners can reassemble and regain function, thereby confirming the partnership. Split-protein methods have been effectively utilized for detecting protein-protein interactions in cell-free systems, Escherichia coli, yeast, mammalian cells, plants, and live animals. Herein, we present recent advances in engineering split-protein systems that allow for the rapid detection of ternary protein complexes, small molecule inhibitors, as well as a variety of macromolecules including nucleic acids, poly(ADP) ribose, and iron sulfur clusters. We also present advances that combine split-protein systems with chemical inducers of dimerization strategies that allow for regulating the activity of orthogonal split-proteases as well as aid in identifying enzyme inhibitors. Finally, we discuss autoinhibition strategies leading to turn-on sensors as well as future directions in split-protein methodology including possible therapeutic approaches.     

Exploring weak, transient protein--protein interactions in crowded in vivo environments by in-cell nuclear magnetic resonance spectroscopy

Biology relies on functional interplay of proteins in the crowded and heterogeneous environment inside cells, and functional protein interactions are often weak and transient. Thus, methods that preserve these interactions and provide information about them are needed. In-cell nuclear magnetic resonance (NMR) spectroscopy is an attractive method for studying a protein's behavior in cells because it may provide residue-level structural and dynamic information, yet several factors limit the feasibility of protein NMR spectroscopy in cells; among them, slow rotational diffusion has emerged as the most important. In this paper, we seek to elucidate the causes of the dramatically slow protein tumbling in cells and in so doing to gain insight into how the intracellular viscosity and weak, transient interactions modulate protein mobility. To address these questions, we characterized the rotational diffusion of three model globular proteins in Escherichia coli cells using two-dimensional heteronuclear NMR spectroscopy. These proteins have a similar molecular size and globular fold but very different surface properties, and indeed, they show very different rotational diffusion in the E. coli intracellular environment. Our data are consistent with an intracellular viscosity approximately 8 times that of water, too low to be a limiting factor for observation of small globular proteins by in-cell NMR spectroscopy. Thus, we conclude that transient interactions with cytoplasmic components significantly and differentially affect the mobility of proteins and therefore their NMR detectability. Moreover, we suggest that an intricate interplay of total protein charge and hydrophobic interactions plays a key role in regulating these weak intermolecular interactions in cells.     

Role of protein--protein interactions in the regulation of transcription by trp repressor investigated by fluorescence spectroscopy.

In the present work, we have characterized the protein--protein interactions in the trp repressor (TR) from Escherichia coli using fluorescence spectroscopy. The steady-state and time-resolved fluorescence anisotropy of repressor labeled with 5-(dimethylamino)naphthalene-1-sulfonamide (DNS) was used to monitor subunit equilibria in the absence and presence of corepressor. In the absence of tryptophan, the repressor is in equilibrium between tetramers and dimers in the concentration range studied (approximately 0.04-40 microM in dimer). Binding of corepressor resulted in a marked destabilization of the tetramer. The beginning of a dimer-monomer dissociation transition was observed by monitoring the decrease in the intrinsic tryptophan emission energy upon dilution below 0.1 microM in dimer, indicating an upper limit for the dimer-dissociation constant near 1 nM. DNA titrations with a 26 base pair sequence containing the trp EDCBA operator performed in the absence and presence of the corepressor are consistent with a 1:1 dimer/operator stoichiometry in the presence of tryptophan, while the aporepressor binds with TR dimer/DNA stoichiometries greater than one and which depend upon both the concentration of protein and that of the operator. Using the multiple observable parameters available in fluorescence, we have thus carried out a thorough investigation of the coupled equilibria in this bacterial repressor. Our results are consistent with a physiologically relevant thermodynamic role for tetramerization in the regulatory function of the trp repressor. The present results which have brought to light novel protein--protein interactions in the trp repressor system indicate that fluorescence spectroscopic methods could prove quite useful in the study of the role of protein--protein interactions in eukaryotic systems as well.     

Significance of microbial interactions in control of microbial ecosystems

A microbial ecosystem represents a delicately balanced population of microorganisms each interacting with and influencing the other members of the population. An understanding of the nature and effects of these interactions is essential to improving the performance of these ecologies, which are important, in such diverse processes as biological waste treatment procedures, water pollution abatement, industrial fermentations, human or animal digestives processes and in soil. There are several types of mocrobial interactions, such as commensalism, inhibition, food competition, predation, parasitism, and synergism, which either singly or in combination may influence the functioning of the microbial ecology. To understand interactions, it is necessary to perform a detailed study of the physiology of the individual predominating microorganisms to establish their requirements with respect to such environmental factors as nutrients, temperature, pH, oxidation-reduction potential, removal of waste products, or toxic materials which may be involved in control processes and to determine how these factors affect their capabilities. The sum total of this information will indicate the possible interactions between the microorganisms and will form the basis for conducting experiments either in the laboratory or with mathematical models. Such experiments will lead to an understanding of microbial activities and to the formulation of control measures, often using an alteration of the environmental factors for regulation of the microbial ecologies. Extensive research remains to be done on the microbial interact inns in obtain the desired, precise control of these ecological processes.     

IMPARO: inferring microbial interactions through parameter optimisation

Type 2 diabetes mellitus (T2DM) is a worldwide disease that have an impact on individuals of all ages causing micro and macro vascular impairments due to hyperglycemic internal environment. For ultimate treatment to cure T2DM, association of diabetes with immune components provides a strong basis for immunotherapies and vaccines developments that could stimulate the immune cells to minimize the insulin resistance and initiate gluconeogenesis through an insulin independent route. Immunoinformatics based approach was used to design a polyvalent vaccine for T2DM that involved data accession, antigenicity analysis, T-cell epitopes prediction, conservation and proteasomal evaluation, functional annotation, interactomic and in silico binding affinity analysis. We found the binding affinity of antigenic peptides with major histocompatibility complex (MHC) Class-I molecules for immune activation to control T2DM. We found 13-epitopes of 9 amino acid residues for multiple alleles of MHC class-I bears significant binding affinity. The downstream signaling resulted by T-cell activation is directly regulated by the molecular weight, amino acid properties and affinity of these epitopes. Each epitope has important percentile rank with significant ANN IC50 values. These high score potential epitopes were linked using AAY, EAAAK linkers and HBHA adjuvant to generate T-cell polyvalent vaccine with a molecular weight of 35.6 kDa containing 322 amino acids residues. In silico analysis of polyvalent construct showed the significant binding affinity (− 15.34 Kcal/mol) with MHC Class-I. This interaction would help to understand our hypothesis, potential activation of T-cells and stimulatory factor of cytokines and GLUT1 receptors. Our system-level immunoinformatics approach is suitable for designing potential polyvalent therapeutic vaccine candidates for T2DM by reducing hyperglycemia and enhancing metabolic activities through the immune system.     

Volatile affairs in microbial interactions

Microorganisms are important factors in shaping our environment. One key characteristic that has been neglected for a long time is the ability of microorganisms to release chemically diverse volatile compounds. At present, it is clear that the blend of volatiles released by microorganisms can be very complex and often includes many unknown compounds for which the chemical structures remain to be elucidated. The biggest challenge now is to unravel the biological and ecological functions of these microbial volatiles. There is increasing evidence that microbial volatiles can act as infochemicals in interactions among microbes and between microbes and their eukaryotic hosts. Here, we review and discuss recent advances in understanding the natural roles of volatiles in microbe–microbe interactions. Specific emphasis will be given to the antimicrobial activities of microbial volatiles and their effects on bacterial quorum sensing, motility, gene expression and antibiotic resistance.     

Tracking microbial interactions with NanoSIMS

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Specificity of marine microbial surface interactions

The macromolecular surface components involved in intraspecific cell surface interactions of the green microalga Chlorella vulgaris and closely associated bacteria were investigated. The specific surface attachment between this alga and its associated bacteria is mediated by lectin-like macromolecules associated with the surfaces of these cells. The binding activity of these surface polymers was inhibited by specific simple sugars; this suggests the involvement of specific receptor-ligand binding sites on the interactive surfaces. Epifluorescent microscopic evaluation of bacteria-alga interactions in the presence and absence of the macromolecules that mediate these interactions showed that the glycoproteins active in these processes were specific to the microbial sources from which they were obtained. The demonstration and definition of the specificity of these interactions in mixed microbial populations may play an important role in our understanding of the dynamics of marine microbial populations in the sea.     

微生物相互作用中的空间自组织

微生物在全球生态系统中占据着重要地位, 其中一个重要的研究领域是微生物与环境(包括无机环境与生物环境)之间的相互作用。在生态相互作用过程中, 微生物常常通过自组织形成特定的空间模式。微生物的空间模式在种群稳定性、群落动态变化以及维持合作行为方面具有重要作用。本文中, 我们梳理了当下对微生物空间自组织及其所形成的空间模式的研究内容, 首先介绍什么是空间自组织, 再根据生态相互作用类型对自组织的空间模式进行描述, 其中重点讨论合作与竞争中的空间模式, 接着关注微生物空间自组织的过程, 最后我们指出空间自组织对整个群体的结构和功能稳定具有重要意义。研究微生物种群间相互作用中的空间模式, 有助于探索维持合作行为的新机制, 进而为微生物共生系统的构建提供新的理解。     

A psychophysical investigation of binary bitter-compound interactions

The aim of this study was to determine if taste interactions occur when bitter stimuli are mixed. Eight bitter stimuli were employed: denatonium benzoate (DB), quinine-HCl (QHCl), sucrose octaacetate (SOA), urea, L-tryptophan (L-trp), L-phenylalanine (L-phe), ranitidine-HCl, and Tetralone. The first experiment constructed individual psychophysical curves for each subject (n = 19) for each compound to account for individual differences in sensitivities when presenting bitter compounds in experiment 2. Correlation analysis revealed two groupings of bitter compounds at low intensity (1, L-trp, L-phe, and ranitidine; 2, SOA and QHCl), but the correlations within each group decreased as the perceived intensity increased. In experiment 2, intensity ratings and two-alternative forced-choice discrimination tasks showed that bitter compounds generally combine additively in mixture and do not show interactions with a few specific exceptions. The methods employed detected synergy among sweeteners, but could not detect synergy among these eight bitter compounds. In general, the perceived bitterness of these binary bitter-compound mixtures was an additive function of the total bitter-inducing stimuli in the mouth.