IDQD算法预测人类蛋白质相互作用

蛋白质是一切生命活动的物质基础,研究蛋白质的相互作用有助于理解生物过程的分子机制,阐明疾病的分子机理。本文依据蛋白质序列组分特征,应用基于多样性增量的二次判别分析方法,对人类的1 963对蛋白质相互作用进行了预测。自洽检验的各项预测指标均在79%以上,且交叉检验的总精度也大于60%,表明本算法可以用于蛋白质相互作用预测。     

Molecular interactions between Neisseria meningitidis and its human host

Neisseria meningitidis is a Gram‐negative bacterium that asymptomatically colonises the nasopharynx of humans. For an unknown reason, N. meningitidis can cross the nasopharyngeal barrier and invade the bloodstream where it becomes one of the most harmful extracellular bacterial pathogen. This infectious cycle involves the colonisation of two different environments. (a) In the nasopharynx, N. meningitidis grow on the top of mucus‐producing epithelial cells surrounded by a complex microbiota. To survive and grow in this challenging environment, the meningococcus expresses specific virulence factors such as polymorphic toxins and MDAΦ. (b) Meningococci have the ability to survive in the extra cellular fluids including blood and cerebrospinal fluid. The interaction of N. meningitidis with human endothelial cells leads to the formation of typical microcolonies that extend overtime and promote vascular injury, disseminated intravascular coagulation, and acute inflammation. In this review, we will focus on the interplay between N. meningitidis and these two different niches at the cellular and molecular level and discuss the use of inhibitors of piliation as a potent therapeutic approach.     

Prediction of protein-protein interactions between Ralstonia solanacearum and Arabidopsis thaliana

Ralstonia solanacearum is a devastating bacterial pathogen that has an unusually wide host range. R. solanacearum, together with Arabidopsis thaliana, has become a model system for studying the molecular basis of plant-pathogen interactions. Protein-protein interactions (PPIs) play a critical role in the infection process, and some PPIs can initiate a plant defense response. However, experimental investigations have rarely addressed such PPIs. Using two computational methods, the interolog and the domain-based methods, we predicted 3,074 potential PPIs between 119 R. solanacearum and 1,442 A. thaliana proteins. Interestingly, we found that the potential pathogen-targeted proteins are more important in the A. thaliana PPI network. To facilitate further studies, all predicted PPI data were compiled into a database server called PPIRA (http://protein.cau.edu.cn/ppira/). We hope that our work will provide new insights for future research addressing the pathogenesis of R. solanacearum.     

Prediction of physical protein-protein interactions

Many essential cellular processes such as signal transduction, transport, cellular motion and most regulatory mechanisms are mediated by protein-protein interactions. In recent years, new experimental techniques have been developed to discover the protein-protein interaction networks of several organisms. However, the accuracy and coverage of these techniques have proven to be limited, and computational approaches remain essential both to assist in the design and validation of experimental studies and for the prediction of interaction partners and detailed structures of protein complexes. Here, we provide a critical overview of existing structure-independent and structure-based computational methods. Although these techniques have significantly advanced in the past few years, we find that most of them are still in their infancy. We also provide an overview of experimental techniques for the detection of protein-protein interactions. Although the developments are promising, false positive and false negative results are common, and reliable detection is possible only by taking a consensus of different experimental approaches. The shortcomings of experimental techniques affect both the further development and the fair evaluation of computational prediction methods. For an adequate comparative evaluation of prediction and high-throughput experimental methods, an appropriately large benchmark set of biophysically characterized protein complexes would be needed, but is sorely lacking.     

Contribution of electron microscopy in the study of the interactions between pathogenic bacteria and their host cells

Our knowledge on the functional anatomy of bacteria is based on the electron microscopic (EM) studies performed during the last forty years. Most pathogenic properties however cannot be visualized in EM because they are not related to defined structures. In contrast, EM studies have provided important data on the behaviour of pathogenic bacteria in their host cells. They have shown that many bacterial species have developed different stratagems to survive and multiply in their host cell. Some are even able to use the host cell machinery to move and invade adjacent cells.     

Allele-specific suppression as a tool to study protein-protein interactions in bacteria

Suppression analysis is well suited to study the interactions of gene products. It offers the advantage of simplicity for any organism for which a convenient genetic system has been developed, which holds for a wide spectrum of bacteria and an ever-increasing number of unicellular as well as complex eukaryotes. No other method provides as much information about the functional relationships of biological macromolecules. The intrinsic value of suppression analysis is enhanced by advances in genomics and in biophysical techniques for investigating the properties of nucleic acids and proteins, such as X-ray crystallography, liquid and solid-state nuclear magnetic resonance, electron spin labeling, and isothermal calorimetry. These approaches confirm and complement whatever is revealed by genetics. Despite these sterling qualities, suppression analysis has its dangers, less in execution than in conceptualization of experiments and interpretation of data. A consistent nomenclature is essential for a uniform and widespread understanding of the results. Familiarity with the genetic background and idiosyncracies of the organism studied is critical in avoiding extraneous phenomena that can affect the outcome. Finally, it is imperative not to underestimate potentially bizarre and improbable consequences that can transpire when rigorous genetic selection is maintained for an appreciable length of time. The article begins with a somewhat pedagogical discussion of genetic terminology. It then moves on to the necessary precautions to observe while planning and conducting suppression analysis. The remainder of the article considers different manifestations of suppression: bypass suppression; gradients of suppression; suppression by relaxed specificity; allele-specific "suppression at a distance"; and true conformational suppression. The treatment is not exhaustive, but representative examples have been gleaned from the recent bacterial literature.     

Construction and application of a yeast surface-displayed human cDNA library to identify post-translational modification-dependent protein-protein interactions

Although post-translational modifications such as phosphorylation mediate fundamental biological processes within the cell, relatively few methods exist that allow proteome-wide identification of proteins that interact with these modifications. We constructed a yeast surface-displayed human cDNA library and utilized it to identify protein fragments with affinity for phosphorylated peptides derived from the major tyrosine autophosphorylation sites of the epidermal growth factor receptor or focal adhesion kinase. We identified cDNAs encoding the Src homology 2 domains from adapter protein APS, phosphoinositide 3-kinase regulatory subunit 3, SH2B, and tensin, demonstrating the effectiveness of this approach. Our results suggest that large libraries of functional human protein fragments can be efficiently displayed on the yeast surface. In addition to the analysis of post-translational modifications, yeast surface-displayed human cDNA libraries have many potential applications, including identifying targets and defining potential cross-reactive proteins for small molecules or drugs.     

Prediction of protein motions from amino acid sequence and its application to protein-protein interaction

Background  

Structural flexibility is an important characteristic of proteins because it is often associated with their function. The movement of a polypeptide segment in a protein can be broken down into two types of motions: internal and external ones. The former is deformation of the segment itself, but the latter involves only rotational and translational motions as a rigid body. Normal Model Analysis (NMA) can derive these two motions, but its application remains limited because it necessitates the gathering of complete structural information.     

Prediction of protein-protein interactions by docking methods

Recently, developments have been made in predicting the structure of docked complexes when the coordinates of the components are known. The process generally consists of a stage during which the components are combined rigidly and then a refinement stage. Several rapid new algorithms have been introduced in the rigid docking problem and promising refinement techniques have been developed, based on modified molecular mechanics force fields and empirical measures of desolvation, combined with minimisations that switch on the short-range interactions gradually. There has also been progress in developing a benchmark set of targets for docking and a blind trial, similar to the trials of protein structure prediction, has taken place.     

Prediction of protein-protein interactions: the CAPRI experiment, its evaluation and implications

Given the increasing interest in protein-protein interactions, the prediction of these interactions from sequence and structural information has become a booming activity. CAPRI, the community-wide experiment for assessing blind predictions of protein-protein interactions, is playing an important role in fostering progress in docking procedures. At the same time, novel methods are being derived for predicting regions of a protein that are likely to interact and for characterizing putative intermolecular contacts from sequence and structural data. Together with docking procedures, these methods provide an integrated computational approach that should be a valuable complement to genome-scale experimental studies of protein-protein interactions.     

Using chimeric hypoviruses to fine-tune the interaction between a pathogenic fungus and its plant host

Infectious cDNA clones of mild (CHV1-Euro7) and severe (CHV1-EP713) hypovirus strains responsible for virulence attenuation (hypovirulence) of the chestnut blight fungus Cryphonectria parasitica were used to construct viable chimeric viruses. Differences in virus-mediated alterations of fungal colony morphology, growth rate, and canker morphology were mapped to a region of open reading frame B extending from nucleotides 2,363 to 9, 904. By swapping domains within this region, it was possible to generate chimeric hypovirus-infected C. parasitica isolates that exhibited a spectrum of defined colony and canker morphologies. Several severe strain traits were observed to be dominant. It was also possible to uncouple the severe strain traits of small canker size and suppression of asexual sporulation. For example, fungal isolates infected with a chimera containing nucleotides 2363 through 5310 from CHV1-Euro7 in a CHV1-713 background formed small cankers that were similar in size to that caused by CHV1-EP713-infected isolates but with the capacity for producing asexual spores at levels approaching that observed for fungal isolates infected with the mild strain. These results demonstrate that hypoviruses can be engineered to fine-tune the interaction between a pathogenic fungus and its plant host. The identification of specific hypovirus domains that differentially contribute to canker morphology and sporulation levels also provides considerable utility for continuing efforts to enhance biological control potential by balancing hypovirulence and ecological fitness.     

Prediction of protein-protein interactions using protein signature profiling

Protein domains are conserved and functionally independent structures that play an important role in interactions among related proteins. Domain-domain interactions have been recently used to predict protein-protein interactions (PPI). In general, the interaction probability of a pair of domains is scored using a trained scoring function. Satisfying a threshold, the protein pairs carrying those domains are regarded as "interacting". In this study, the signature contents of proteins were utilized to predict PPI pairs in Saccharomyces cerevisiae, Caenorhabditis elegans, and Homo sapiens. Similarity between protein signature patterns was scored and PPI predictions were drawn based on the binary similarity scoring function. Results show that the true positive rate of prediction by the proposed approach is approximately 32% higher than that using the maximum likelihood estimation method when compared with a test set, resulting in 22% increase in the area under the receiver operating characteristic (ROC) curve. When proteins containing one or two signatures were removed, the sensitivity of the predicted PPI pairs increased significantly. The predicted PPI pairs are on average 11 times more likely to interact than the random selection at a confidence level of 0.95, and on average 4 times better than those predicted by either phylogenetic profiling or gene expression profiling.     

Candida albicans proteinases and host/pathogen interactions

Candida infections are common, debilitating and often recurring fungal diseases and a problem of significant clinical importance. Candida albicans, the most virulent of the Candida spp., can cause severe mucosal and life-threatening systemic infections in immunocompromised hosts. Attributes that contribute to C. albicans virulence include adhesion, hyphal formation, phenotypic switching and extracellular hydrolytic enzyme production. The extracellular hydrolytic enzymes, especially the secreted aspartyl proteinases (Saps), are one of few gene products that have been shown to directly contribute to C. albicans pathogenicity. Because C. albicans is able to colonize and infect almost every tissue in the human host, it may be crucial for the fungus to possess a number of similar but independently regulated and functionally distinct secreted proteinases to provide sufficient flexibility in order to survive and promote infection at different niche sites. The aim of this review is to explore the functional roles of the C. albicans proteinases and how they may contribute to the host/pathogen interaction in vivo.     

Co-evolutionary interactions between host resistance and pathogen effector genes in flax rust disease

Plant-pathogen co-evolutionary selection processes are continuous, complex and occur across many spatial and temporal scales. Comprehensive studies of the flax-flax rust pathosystem have led to the postulation of the gene-for-gene model, a genetic paradigm describing recognition events between host disease resistance proteins and pathogen effector proteins. The identification of directly interacting fungal effector proteins and plant disease resistance proteins in this pathosystem has facilitated the study of both the physical nature of these interactions and the evolutionary forces that have resulted in a molecular arms race between these organisms. The flax-flax rust pathosystem has also been detailed on the scale of interacting populations, and the integration of molecular- and population-scale datasets represents a unique opportunity to further our understanding of many poorly understood facets of host-pathogen dynamics. In this article, we discuss recent developments and insights in the flax-flax rust pathosystem and their implications for both long-term co-evolutionary dynamics in natural settings, as well as short-term co-evolutionary dynamics in agro-ecosystems.     

Mapping the protein-protein interface between a toxin and its cognate antitoxin from the bacterial pathogen Streptococcus pyogenes

Protein--protein interactions are ubiquitous and essential for most biological processes. Although new proteomic technologies have generated large catalogs of interacting proteins, considerably less is known about these interactions at the molecular level, information that would aid in predicting protein interactions, designing therapeutics to alter these interactions, and understanding the effects of disease-producing mutations. Here we describe mapping the interacting surfaces of the bacterial toxin SPN (Streptococcus pyogenes NAD(+) hydrolase) in complex with its antitoxin IFS (immunity factor for SPN) by using hydrogen-deuterium amide exchange and electrospray ionization mass spectrometry. This approach affords data in a relatively short time for small amounts of protein, typically 5-7 pmol per analysis. The results show a good correspondence with a recently determined crystal structure of the IFS--SPN complex but additionally provide strong evidence for a folding transition of the IFS protein that accompanies its binding to SPN. The outcome shows that mass-based chemical footprinting of protein interaction surfaces can provide information about protein dynamics that is not easily obtained by other methods and can potentially be applied to large, multiprotein complexes that are out of range for most solution-based methods of biophysical analysis.     

Virus particle quantitation and its application to virus–host cell interactions

Virus quantitation is discussed stressing the discrepancy between the biological titer and the physical virus particle count. When both these techniques are used in conjunction, one can more realistically catalog viral properties. The merits of this technique are exemplified in a study of the growth kinetics and properties of vesicular stomatitis virus in tissue culture.     

自然植物种群中病原菌与寄主植物相互作用的遗传学

在农业种群中,作物和菌病相互作用的遗传学已为人们所广泛研究,但对自然植物种群中病原菌与其寄主相互作用体系的某些重要现象却缺乏足够的重视。本文主要论述寄主与病原菌相互作用的遗传学本质;寄主抗性基因和病原菌毒性基因的获得代价及对各自适应力的影响;简单介绍了寄主-病原菌作用体系在种群水平上的遗传学内容及共进化的意义。     

Prediction of protein-protein interactions using random decision forest framework

MOTIVATION: Protein interactions are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Domains are the building blocks of proteins; therefore, proteins are assumed to interact as a result of their interacting domains. Many domain-based models for protein interaction prediction have been developed, and preliminary results have demonstrated their feasibility. Most of the existing domain-based methods, however, consider only single-domain pairs (one domain from one protein) and assume independence between domain-domain interactions. RESULTS: In this paper, we introduce a domain-based random forest of decision trees to infer protein interactions. Our proposed method is capable of exploring all possible domain interactions and making predictions based on all the protein domains. Experimental results on Saccharomyces cerevisiae dataset demonstrate that our approach can predict protein-protein interactions with higher sensitivity (79.78%) and specificity (64.38%) compared with that of the maximum likelihood approach. Furthermore, our model can be used to infer interactions not only for single-domain pairs but also for multiple domain pairs.     

Prediction of protein-protein interactions based on surface patch comparison

A method to predict if two proteins interact, based on their three-dimensional structures, is presented. It consists of five steps: (i) the surface of each protein, represented by the solvent accessible atoms, is divided into small patches; (ii) the shape of each patch is described by the atom distributions along its principal axes; (iii) the shape complementarity between two patches is estimated by comparing, through contingency table analysis, their atom distributions along their principal axes; (iv) given protein A, with nA surface patches, and protein B, with nB surface patches, nA x nB shape complementarity values are obtained; and (v) the distribution of the latter allows one to discriminate pairs of interacting and of noninteracting proteins. Only a few seconds are necessary to predict if two proteins interact, with accuracy close to 80%, sensitivity over 70% and specificity close to 50%.