Dynamic protein-protein interaction wiring of the human spliceosome

More than 200 proteins copurify with spliceosomes, the compositionally dynamic RNPs catalyzing pre-mRNA splicing. To better understand protein - protein interactions governing splicing, we systematically investigated interactions between human spliceosomal proteins. A comprehensive Y2H interaction matrix screen generated a protein interaction map comprising 632 interactions between 196 proteins. Among these, 242 interactions were found between spliceosomal core proteins and largely validated by coimmunoprecipitation. To reveal dynamic changes in protein interactions, we integrated spliceosomal complex purification information with our interaction data and performed link clustering. These data, together with interaction competition experiments, suggest that during step 1 of splicing, hPRP8 interactions with SF3b proteins are replaced by hSLU7, positioning this second step factor close to the active site, and that the DEAH-box helicases hPRP2 and hPRP16 cooperate through ordered interactions with GPKOW. Our data provide extensive information about the spliceosomal protein interaction network and its dynamics.     

Towards a predictive model of species interaction beta diversity

Species interactions are fundamental to community dynamics and ecosystem processes. Despite significant progress in describing species interactions, we lack the ability to predict changes in interactions across space and time. We outline a Bayesian approach to separate the probability of species co‐occurrence, interaction and detectability in influencing interaction betadiversity. We use a multi‐year hummingbird–plant time series, divided into training and testing data, to show that including models of detectability and occurrence improves forecasts of mutualistic interactions. We then extend our model to explore interaction betadiversity across two distinct seasons. Despite differences in the observed interactions among seasons, there was no significant change in hummingbird occurrence or interaction frequency between hummingbirds and plants. These results highlight the challenge of inferring the causes of interaction betadiversity when interaction detectability is low. Finally, we highlight potential applications of our model for integrating observations of local interactions with biogeographic and evolutionary histories of co‐occurring species. These advances will provide new insight into the mechanisms that drive variation in patterns of biodiversity.     

An assessment of the uses of homologous interactions

MOTIVATION: Protein-protein interactions have proved to be a valuable starting point for understanding the inner workings of the cell. Computational methodologies have been built which both predict interactions and use interaction datasets in order to predict other protein features. Such methods require gold standard positive (GSP) and negative (GSN) interaction sets. Here we examine and demonstrate the usefulness of homologous interactions in predicting good quality positive and negative interaction datasets. RESULTS: We generate GSP interaction sets as subsets from experimental data using only interaction and sequence information. We can therefore produce sets for several species (many of which at present have no identified GSPs). Comprehensive error rate testing demonstrates the power of the method. We also show how the use of our datasets significantly improves the predictive power of algorithms for interaction prediction and function prediction. Furthermore, we generate GSN interaction sets for yeast and examine the use of homology along with other protein properties such as localization, expression and function. Using a novel method to assess the accuracy of a negative interaction set, we find that the best single selector for negative interactions is a lack of co-function. However, an integrated method using all the characteristics shows significant improvement over any current method for identifying GSN interactions. The nature of homologous interactions is also examined and we demonstrate that interologs are found more commonly within species than across species. CONCLUSION: GSP sets built using our homologous verification method are demonstrably better than standard sets in terms of predictive ability. We can build such GSP sets for several species. When generating GSNs we show a combination of protein features and lack of homologous interactions gives the highest quality interaction sets. AVAILABILITY: GSP and GSN datasets for all the studied species can be downloaded from http://www.stats.ox.ac.uk/~deane/HPIV.     

Specificity of molecular interactions in transient protein-protein interaction interfaces

In this study, we investigate what types of interactions are specific to their biological function, and what types of interactions are persistent regardless of their functional category in transient protein-protein heterocomplexes. This is the first approach to analyze protein-protein interfaces systematically at the molecular interaction level in the context of protein functions. We perform systematic analysis at the molecular interaction level using classification and feature subset selection technique prevalent in the field of pattern recognition. To represent the physicochemical properties of protein-protein interfaces, we design 18 molecular interaction types using canonical and noncanonical interactions. Then, we construct input vector using the frequency of each interaction type in protein-protein interface. We analyze the 131 interfaces of transient protein-protein heterocomplexes in PDB: 33 protease-inhibitors, 52 antibody-antigens, 46 signaling proteins including 4 cyclin dependent kinase and 26 G-protein. Using kNN classification and feature subset selection technique, we show that there are specific interaction types based on their functional category, and such interaction types are conserved through the common binding mechanism, rather than through the sequence or structure conservation. The extracted interaction types are C(alpha)-- H...O==C interaction, cation...anion interaction, amine...amine interaction, and amine...cation interaction. With these four interaction types, we achieve the classification success rate up to 83.2% with leave-one-out cross-validation at k = 15. Of these four interaction types, C(alpha)--H...O==C shows binding specificity for protease-inhibitor complexes, while cation-anion interaction is predominant in signaling complexes. The amine ... amine and amine...cation interaction give a minor contribution to the classification accuracy. When combined with these two interactions, they increase the accuracy by 3.8%. In the case of antibody-antigen complexes, the sign is somewhat ambiguous. From the evolutionary perspective, while protease-inhibitors and sig-naling proteins have optimized their interfaces to suit their biological functions, antibody-antigen interactions are the happenstance, implying that antibody-antigen complexes do not show distinctive interaction types. Persistent interaction types such as pi...pi, amide-carbonyl, and hydroxyl-carbonyl interaction, are also investigated. Analyzing the structural orientations of the pi...pi stacking interactions, we find that herringbone shape is a major configuration in transient protein-protein interfaces. This result is different from that of protein core, where parallel-displaced configurations are the major configuration. We also analyze overall trend of amide-carbonyl and hydroxyl-carbonyl interactions. It is noticeable that nearly 82% of the interfaces have at least one hydroxyl-carbonyl interactions.     

Disentangling the web of allosteric communication in a homotetramer: heterotropic activation in phosphofructokinase from Escherichia coli

Phosphofructokinase from Escherichia coli (EcPFK) is a homotetramer with four active sites and four allosteric sites. Understanding the allosteric activation of EcPFK by MgADP has been complicated by the complex web of possible interactions, including active site homotropic interactions, allosteric site homotropic interactions, and heterotropic interactions between active and allosteric sites. The current work has simplified this web of possible interactions to a series of single heterotropic interactions by forming and isolating hybrid tetramers. Each of the four unique heterotropic interactions have independently been isolated and compared to a control that has all four of the unique heterotropic interactions. If the interactions are labeled with the distances between interacting ligands, the 45-A interaction contributes 20% +/- 1%, the 33-A interaction contributes 34% +/- 1%, the 30-A interaction contributes 21% +/- 1%, and the 23-A interaction contributes 25% +/- 1% with respect to the total free energy of MgADP/fructose 6-phosphate (Fru-6-P) activation in the control. The free energies of the isolated interactions sum to 100% +/- 2% of the total. Therefore, the four unique interactions are all contributors to activation, are nonequivalent, and are additive.     

Seasonal patterns of root and shoot interactions in an alpine meadow on the Tibetan Plateau

Aims Numerous studies have showed that the balance between negative and positive plant–plant interactions shifted along environmental gradients. But little is known how the positive or negative plant–plant interactions varied with temporal fluctuating habitat conditions and plant ontogenetic phases.Methods In a 2-year experiment, the four perennial grasses (Kobresia humilis, Stipa aliena, Elymus nutans and Saussurea superba) were grown under four interaction treatments (no root or shoot interaction, only shoot interaction, only root interaction, root and shoot interaction). Intensity of above- and belowground interactions is proposed to vary with the fluctuation of seasonal climatic conditions and soil available nutrients. Here we report measurements of above- and belowground interactions during entire growing season. Correlation between plant interaction intensity and seasonal soil available N as well as habitat climate conditions was also performed.Important findings Our experiment found that root interactions had negative effect on plant growth for the four species during growing season. However, both negative and positive shoot interactions occurred among the four species. Despite there being shoot facilitative effect for E. nutans and S. superba, the full interaction was negative, suggested that root interaction take more important role on plant growth than that of shoot interaction. The interaction between root and shoot effect varied as a function of species identity and growth phases. The weak correlation of plant interaction intensity to habitat environmental factors suggested that plant ontogenetic characteristics may be primary factors causing temporal variation in plant interaction.     

Complexity of multitrophic interactions in a grassland ecosystem depends on plant species diversity

1.?We studied the theoretical prediction that a loss of plant species richness has a strong impact on community interactions among all trophic levels and tested whether decreased plant species diversity results in a less complex structure and reduced interactions in ecological networks. 2.?Using plant species-specific biomass and arthropod abundance data from experimental grassland plots (Jena Experiment), we constructed multitrophic functional group interaction webs to compare communities based on 4 and 16 plant species. 427 insect and spider species were classified into 13 functional groups. These functional groups represent the nodes of ecological networks. Direct and indirect interactions among them were assessed using partial Mantel tests. Interaction web complexity was quantified using three measures of network structure: connectance, interaction diversity and interaction strength. 3.?Compared with high plant diversity plots, interaction webs based on low plant diversity plots showed reduced complexity in terms of total connectance, interaction diversity and mean interaction strength. Plant diversity effects obviously cascade up the food web and modify interactions across all trophic levels. The strongest effects occurred in interactions between adjacent trophic levels (i.e. predominantly trophic interactions), while significant interactions among plant and carnivore functional groups, as well as horizontal interactions (i.e. interactions between functional groups of the same trophic level), showed rather inconsistent responses and were generally rarer. 4.?Reduced interaction diversity has the potential to decrease and destabilize ecosystem processes. Therefore, we conclude that the loss of basal producer species leads to more simple structured, less and more loosely connected species assemblages, which in turn are very likely to decrease ecosystem functioning, community robustness and tolerance to disturbance. Our results suggest that the functioning of the entire ecological community is critically linked to the diversity of its component plants species.     

Geometrical and electro-static determinants of protein-protein interactions

Protein-protein interactions (PPI) are pivotal to the numerous processes in the cell. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the of forces interactions. The interaction interface of obligatory protein-protein complexes differs from that of the transient interactions. We have created a large database of protein-protein interactions containing over100 thousand interfaces. The structural redundancy was eliminated to obtain a non-redundant database of over 2,265 interaction interfaces. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the offorces interactions. The residue interaction propensity and all of the rest of the parametric scores converged to a statistical indistinguishable common sub-range and followed the similar distribution trends for all three classes of sequence-based classifications PPInS. This indicates that the principles of molecular recognition are dependent on the preciseness of the fit in the interaction interfaces. Thus, it reinforces the importance of geometrical and electrostatic complementarity as the main determinants for PPIs.     

Interaction generality, a measurement to assess the reliability of a protein-protein interaction

Here we introduce the ‘interaction generality’ measure, a new method for computationally assessing the reliability of protein–protein interactions obtained in biological experiments. This measure is basically the number of proteins involved in a given interaction and also adopts the idea that interactions observed in a complicated interaction network are likely to be true positives. Using a group of yeast protein–protein interactions identified in various biological experiments, we show that interactions with low generalities are more likely to be reproducible in other independent assays. We constructed more reliable networks by eliminating interactions whose generalities were above a particular threshold. The rate of interactions with common cellular roles increased from 63% in the unadjusted estimates to 79% in the refined networks. As a result, the rate of cross-talk between proteins with different cellular roles decreased, enabling very clear predictions of the functions of some unknown proteins. The results suggest that the interaction generality measure will make interaction data more useful in all organisms and may yield insights into the biological roles of the proteins studied.     

Construction of reliable protein-protein interaction networks with a new interaction generality measure

MOTIVATION: Recent screening techniques have made large amounts of protein-protein interaction data available, from which biologically important information such as the function of uncharacterized proteins, the existence of novel protein complexes, and novel signal-transduction pathways can be discovered. However, experimental data on protein interactions contain many false positives, making these discoveries difficult. Therefore computational methods of assessing the reliability of each candidate protein-protein interaction are urgently needed. RESULTS: We developed a new 'interaction generality' measure (IG2) to assess the reliability of protein-protein interactions using only the topological properties of their interaction-network structure. Using yeast protein-protein interaction data, we showed that reliable protein-protein interactions had significantly lower IG2 values than less-reliable interactions, suggesting that IG2 values can be used to evaluate and filter interaction data to enable the construction of reliable protein-protein interaction networks.     

Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents

Aim Biotic interactions – within guilds or across trophic levels – have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of ‘species interaction distribution models’ (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co‐occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non‐stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio‐temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species’ effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co‐occurrence datasets across large‐scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio‐temporal data on biotic interactions in multispecies communities.     

Predator diversity and environmental change modify the strengths of trophic and nontrophic interactions

Understanding the dependence of species interaction strengths on environmental factors and species diversity is crucial to predict community dynamics and persistence in a rapidly changing world. Nontrophic (e.g. predator interference) and trophic components together determine species interaction strengths, but the effects of environmental factors on these two components remain largely unknown. This impedes our ability to fully understand the links between environmental drivers and species interactions. Here, we used a dynamical modelling framework based on measured predator functional responses to investigate the effects of predator diversity, prey density, and temperature on trophic and nontrophic interaction strengths within a freshwater food web. We found that (i) species interaction strengths cannot be predicted from trophic interactions alone, (ii) nontrophic interaction strengths vary strongly among predator assemblages, (iii) temperature has opposite effects on trophic and nontrophic interaction strengths, and (iv) trophic interaction strengths decrease with prey density, whereas the dependence of nontrophic interaction strengths on prey density is concave up. Interestingly, the qualitative impacts of temperature and prey density on the strengths of trophic and nontrophic interactions were independent of predator identity, suggesting a general pattern. Our results indicate that taking multiple environmental factors and the nonlinearity of density‐dependent species interactions into account is an important step towards a better understanding of the effects of environmental variations on complex ecological communities. The functional response approach used in this study opens new avenues for (i) the quantification of the relative importance of the trophic and nontrophic components in species interactions and (ii) a better understanding how environmental factors affect these interactions and the dynamics of ecological communities.     

Integrative approach for computationally inferring protein domain interactions

MOTIVATION: The current need for high-throughput protein interaction detection has resulted in interaction data being generated en masse through such experimental methods as yeast-two-hybrids and protein chips. Such data can be erroneous and they often do not provide adequate functional information for the detected interactions. Therefore, it is useful to develop an in silico approach to further validate and annotate the detected protein interactions. RESULTS: Given that protein-protein interactions involve physical interactions between protein domains, domain-domain interaction information can be useful for validating, annotating, and even predicting protein interactions. However, large-scale, experimentally determined domain-domain interaction data do not exist. Here, we describe an integrative approach to computationally derive putative domain interactions from multiple data sources, including protein interactions, protein complexes, and Rosetta Stone sequences. We further prove the usefulness of such an integrative approach by applying the derived domain interactions to predict and validate protein-protein interactions. AVAILABILITY: A database of putative protein domain interactions derived using the method described in this paper is available at http://interdom.lit.org.sg.     

Interaction rewiring and the rapid turnover of plant–pollinator networks

Whether species interactions are static or change over time has wide‐reaching ecological and evolutionary consequences. However, species interaction networks are typically constructed from temporally aggregated interaction data, thereby implicitly assuming that interactions are fixed. This approach has advanced our understanding of communities, but it obscures the timescale at which interactions form (or dissolve) and the drivers and consequences of such dynamics. We address this knowledge gap by quantifying the within‐season turnover of plant–pollinator interactions from weekly censuses across 3 years in a subalpine ecosystem. Week‐to‐week turnover of interactions (1) was high, (2) followed a consistent seasonal progression in all years of study and (3) was dominated by interaction rewiring (the reassembly of interactions among species). Simulation models revealed that species’ phenologies and relative abundances constrained both total interaction turnover and rewiring. Our findings reveal the diversity of species interactions that may be missed when the temporal dynamics of networks are ignored.     

Structural analysis of cation-pi interactions in DNA binding proteins

Cation-pi interactions play an important role in the stability of protein structures. In this work, we have analyzed the influence of cation-pi interactions in DNA binding proteins. We observed cation-pi interactions in 45 out of 62 DNA binding proteins and there is no significant correlation between the number of amino acid residues and number of cation-pi interactions. These interactions are mainly formed by long-range contacts, and the role of short and medium-range contacts is minimal. The preference of Arg is higher than Lys to form cation-pi interactions. The pair-wise cation-pi interaction energy between aromatic and positively charged residues shows that Arg-Tyr energy is the strongest among the possible six pairs. The structural analysis of cation-pi interaction forming residues shows that Lys, Trp, and Tyr prefer to be in the binding site of protein-DNA complexes. Further, the accessible surface areas of cation-pi interaction forming cationic residues are significantly less than that of other residues. The preference of cation-pi interaction forming residues in different secondary structures shows that Lys prefers to be in strand and Phe prefers to be in turn regions. The results obtained in the present study will be useful in understanding the contribution of cation-pi interactions to the stability and specificity of protein-DNA complexes.     

PRASA: An integrated web server that analyzes protein interaction types

This work presents the Protein Association Analyzer (PRASA) (http://zoro.ee.ncku.edu.tw/prasa/) that predicts protein interactions as well as interaction types. Protein interactions are essential to most biological functions. The existence of diverse interaction types, such as physically contacted or functionally related interactions, makes protein interactions complex. Different interaction types are distinct and should not be confused. However, most existing tools focus on a specific interaction type or mix different interaction types. This work collected 7234058 associations with experimentally verified interaction types from five databases and compiled individual probabilistic models for different interaction types. The PRASA result page shows predicted associations and their related references by interaction type. Experimental results demonstrate the performance difference when distinguishing between different interaction types. The PRASA provides a centralized and organized platform for easy browsing, downloading and comparing of interaction types, which helps reveal insights into the complex roles that proteins play in organisms.     

Analysis of inphase interaction pattern in EEG

Inphase interactions among EEG signals recorded using eight electrodes were investigated. The inphase interaction parameters are presented in two ways: (1) matrix form in which the number of inphase interactions are tabulated; and (2) histogram in which the number of inphase interactions are plotted pair-wise between two sites as a function of phase delays in milliseconds. The highest number of interactions occurs between 0 and 8 ms in normal brains. The values of interaction parameters are enhanced by various activities. For example, inphase interaction parameters increase in the motor area in the right hemisphere if the EEG is recorded during repeated left fist clenching. Inphase interactions are drastically altered by the presence of a tumor. We studied the inphase interactions of the EEG of a patient having an occipital tumor. The interaction parameters are greatly diminished in this area, indicating a severe impairment of neuronal communications between both hemispheres in the occipital region. The confidence limits of the changes in inphase interaction parameters during fist clenching are tested statistically using the Student's t test. The test shows that the interaction parameters increase, in general, with 1–5% confidence limits in respective cortex areas as a result of fist clenching.     

PRINCESS, a protein interaction confidence evaluation system with multiple data sources

Advances in proteomics technologies have enabled novel protein interactions to be detected at high speed, but they come at the expense of relatively low quality. Therefore, a crucial step in utilizing the high throughput protein interaction data is evaluating their confidence and then separating the subsets of reliable interactions from the background noise for further analyses. Using Bayesian network approaches, we combine multiple heterogeneous biological evidences, including model organism protein-protein interaction, interaction domain, functional annotation, gene expression, genome context, and network topology structure, to assign reliability to the human protein-protein interactions identified by high throughput experiments. This method shows high sensitivity and specificity to predict true interactions from the human high throughput protein-protein interaction data sets. This method has been developed into an on-line confidence scoring system specifically for the human high throughput protein-protein interactions. Users may submit their protein-protein interaction data on line, and the detailed information about the supporting evidence for query interactions together with the confidence scores will be returned. The Web interface of PRINCESS (protein interaction confidence evaluation system with multiple data sources) is available at the website of China Human Proteome Organisation.     

染色质互作相关转录因子的挖掘及功能分析

染色质互作是真核生物基因组组装的基础,并且在调控真核基因细胞特异性表达中发挥重要作用.染色质互作的发生与特定的蛋白质有关,目前已经发现CTCF (CCCTC binding facor,转录阻抑物)和黏连蛋白与染色质互作相关,然而并不清楚是否还有其他蛋白质参与染色质互作.我们将整合高通量染色体构象捕获(Hi-C)和染色质免疫沉淀-测序(ChIP-seq)数据,在GM12878和K562细胞系中挖掘与染色质互作相关的转录因子,并对发现的转录因子做功能分析.我们在频繁发生互作的染色质位点中发现RUNX3、SPI1等转录因子也可能参与染色质互作.另外,通过FP-growth的数据挖掘方法还发现多个转录因子可能协同作用参与染色质互作.研究结果将为染色质互作相关实验的开展提供先验知识.     

A benchmarked protein microarray-based platform for the identification of novel low-affinity extracellular protein interactions

Low-affinity extracellular protein interactions are critical for cellular recognition processes, but existing methods to detect them are limited in scale, making genome-wide interaction screens technically challenging. To address this, we report here the miniaturization of the AVEXIS (avidity-based extracellular interaction screen) assay by using protein microarray technology. To achieve this, we have developed protein tags and sample preparation methods that enable the parallel purification of hundreds of recombinant proteins expressed in mammalian cells. We benchmarked the protein microarray-based assay against a set of known quantified receptor-ligand pairs and show that it is sensitive enough to detect even very weak interactions that are typical of this class of interactions. The increase in scale enables interaction screening against a dilution series of immobilized proteins on the microarray enabling the observation of saturation binding behaviors to show interaction specificity and also the estimation of interaction affinities directly from the primary screen. These methodological improvements now permit screening for novel extracellular receptor-ligand interactions on a genome-wide scale.