Global investigation of protein-protein interactions in yeast Saccharomyces cerevisiae using re-occurring short polypeptide sequences

Protein-protein interaction (PPI) maps provide insight into cellular biology and have received considerable attention in the post-genomic era. While large-scale experimental approaches have generated large collections of experimentally determined PPIs, technical limitations preclude certain PPIs from detection. Recently, we demonstrated that yeast PPIs can be computationally predicted using re-occurring short polypeptide sequences between known interacting protein pairs. However, the computational requirements and low specificity made this method unsuitable for large-scale investigations. Here, we report an improved approach, which exhibits a specificity of approximately 99.95% and executes 16,000 times faster. Importantly, we report the first all-to-all sequence-based computational screen of PPIs in yeast, Saccharomyces cerevisiae in which we identify 29,589 high confidence interactions of approximately 2 x 10(7) possible pairs. Of these, 14,438 PPIs have not been previously reported and may represent novel interactions. In particular, these results reveal a richer set of membrane protein interactions, not readily amenable to experimental investigations. From the novel PPIs, a novel putative protein complex comprised largely of membrane proteins was revealed. In addition, two novel gene functions were predicted and experimentally confirmed to affect the efficiency of non-homologous end-joining, providing further support for the usefulness of the identified PPIs in biological investigations.     

Resin-based investigation of acyl carrier protein interaction networks in Escherichia coli

Protein-protein interactions play an integral role in metabolic regulation. Elucidation of these networks is complicated by the changing identity of the proteins themselves. Here we demonstrate a resin-based technique that leverages the unique tools for acyl carrier protein (ACP) modification with non-hydrolyzable linkages. ACPs from Escherichia coli and Shewanella oneidensis MR-1 are bound to Affigel-15 with varying acyl groups attached and introduced to proteomic samples. Isolation of these binding partners is followed by MudPIT analysis to identify each interactome with the variable of ACP-tethered substrates. These techniques allow for investigation of protein interaction networks with the changing identity of a given protein target.     

数据来源对蛋白质相互作用网络度分布的影响

蛋白质-蛋白质相互作用(Protein-protein interaction,PPI)是生命体结构和生命活动的基础和特征,控制着生命活动的各个过程.PPI网络是研究蛋白质相互作用的有效手段.随着高通量实验技术的发展,越来越多的PPI数据得以使用,收录蛋白质相互作用的数据库数据每年都有变化.本文对DIP数据库从2003年到2008年的PPI网络数据分别计算度分布.为提高可信度,对注释蛋白质数据库交集进行抽样,分别探讨对不同年份的数据和注释数据库抽样对PPI网络度分布的影响.结果表明,从2003年到2008年的数据增长对PPI网络度分布没有明显影响,而且拟合度分布最优的函数并不是以往所认为的幂率分布(power-law),而是广延指数(stretched exponential)函数,数据库交集抽样同样得到广延指数(stretched exponential)函数分布最优且可信度的高低并不影响PPI网络的度分布.     

A novel approach for the identification of protein-protein interaction with integral membrane proteins

Protein-protein interaction plays a major role in all biological processes. The currently available genetic methods such as the two-hybrid system and the protein recruitment system are relatively limited in their ability to identify interactions with integral membrane proteins. Here we describe the development of a reverse Ras recruitment system (reverse RRS), in which the bait used encodes a membrane protein. The bait is expressed in its natural environment, the membrane, whereas the protein partner (the prey) is fused to a cytoplasmic Ras mutant. Protein-protein interaction between the proteins encoded by the prey and the bait results in Ras membrane translocation and activation of a viability pathway in yeast. We devised the expression of the bait and prey proteins under the control of dual distinct inducible promoters, thus enabling a rapid selection of transformants in which growth is attributed solely to specific protein-protein interaction. The reverse RRS approach greatly extends the usefulness of the protein recruitment systems and the use of integral membrane proteins as baits. The system serves as an attractive approach to explore novel protein-protein interactions with high specificity and selectivity, where other methods fail.     

Protein-protein and protein-salt interactions in aqueous protein solutions containing concentrated electrolytes

Protein-protein and protein-salt interactions have been obtained for ovalbumin in solutions of ammonium sulfate and for lysozyme in solutions of ammonium sulfate, sodium chloride, potassium isothiocyanate, and potassium chloride. The two-body interactions between ovalbumin molecules in concentrated ammonium-sulfate solutions can be described by the DLVO potentials plus a potential that accounts for the decrease in free volume available to the protein due to the presence of the salt ions. The interaction between ovalbumin and ammonium sulfate is unfavorable, reflecting the kosmotropic nature of sulfate anions. Lysozyme-lysozyme interactions cannot be described by the above potentials because anion binding to lysozyme alters these interactions. Lysozyme-isothiocyanate complexes are strongly attractive due to electrostatic interactions resulting from bridging by the isothiocyanate ion. Lysozyme-lysozyme interactions in sulfate solutions are more repulsive than expected, possibly resulting from a larger excluded volume of a lysozyme-sulfate bound complex or perhaps, hydration forces between the lysozyme-sulfate complexes.     

A Mutation Deleting Sequences Encoding the Amino Terminus of Human Cytomegalovirus UL84 Impairs Interaction with UL44 and Capsid Localization

Protein-protein interactions are required for many biological functions. Previous work has demonstrated an interaction between the human cytomegalovirus DNA polymerase subunit UL44 and the viral replication factor UL84. In this study, glutathione S-transferase pulldown assays indicated that residues 1 to 68 of UL84 are both necessary and sufficient for efficient interaction of UL84 with UL44 in vitro. We created a mutant virus in which sequences encoding these residues were deleted. This mutant displayed decreased virus replication compared to wild-type virus. Immunoprecipitation assays showed that the mutation decreased but did not abrogate association of UL84 with UL44 in infected cell lysate, suggesting that the association in the infected cell can involve other protein-protein interactions. Further immunoprecipitation assays indicated that IRS1, TRS1, and nucleolin are candidates for such interactions in infected cells. Quantitative real-time PCR analysis of viral DNA indicated that the absence of the UL84 amino terminus does not notably affect viral DNA synthesis. Western blotting experiments and pulse labeling of infected cells with [(35)S]methionine demonstrated a rather modest downregulation of levels of multiple proteins and particularly decreased levels of the minor capsid protein UL85. Electron microscopy demonstrated that viral capsids assemble but are mislocalized in nuclei of cells infected with the mutant virus, with fewer cytoplasmic capsids detected. In sum, deletion of the sequences encoding the amino terminus of UL84 affects interaction with UL44 and virus replication unexpectedly, not viral DNA synthesis. Mislocalization of viral capsids in infected cell nuclei likely contributes to the observed decrease in virus replication.     

Roles for the two-hybrid system in exploration of the yeast protein interactome

Comprehensive analysis of protein-protein interactions is a challenging endeavor of functional proteomics and has been best explored in the budding yeast. The yeast protein interactome analysis was achieved first by using the yeast two-hybrid system in a proteome-wide scale and next by large-scale mass spectrometric analysis of affinity-purified protein complexes. While these interaction data have led to a number of novel findings and the emergence of a single huge network containing thousands of proteins, they suffer many false signals and fall short of grasping the entire interactome. Thus, continuous efforts are necessary in both bioinformatics and experimentation to fully exploit these data and to proceed another step forward to the goal. Computational tools to integrate existing biological knowledge buried in literature and various functional genomic data with the interactome data are required for biological interpretation of the huge protein interaction network. Novel experimental methods have to be developed to detect weak, transient interactions involving low abundance proteins as well as to obtain clues to the biological role for each interaction. Since the yeast two-hybrid system can be used for the mapping of the interaction domains and the isolation of interaction-defective mutants, it would serve as a technical basis for the latter purpose, thereby playing another important role in the next phase of protein interactome research.     

Studying protein-peptide interactions using benzophenone units: a case study of protein kinase B/Akt and its inhibitor PTR6154

Protein-protein interactions (PPIs) govern nearly all processes in living cells. Peptides play an important role in studying PPIs. Peptides carrying photoaffinity labels that covalently bind the interacting protein can be used to obtain more accurate information regarding PPIs. Benzophenone (BP) is a useful photoaffinity label that is widely used to study PPIs. We developed a one-pot two-step synthesis for the preparation of novel BP units. To map the binding site more thoroughly, linkers of various lengths were attached to the BP moiety. These units can be incorporated into peptide sequences using well-established solid phase peptide synthesis (SPPS) protocols. As a proof of concept, we studied the interaction between protein kinase B (PKB/Akt) and its synthetic peptide inhibitor, PTR6154. The methodology is general and can be implemented to study PPIs in a variety of biological systems.     

Protein-protein interaction networks: from interactions to networks

The goal of interaction proteomics that studies the protein-protein interactions of all expressed proteins is to understand biological processes that are strictly regulated by these interactions. The availability of entire genome sequences of many organisms and high-throughput analysis tools has led scientists to study the entire proteome (Pandey and Mann, 2000). There are various high-throughput methods for detecting protein interactions such as yeast two-hybrid approach and mass spectrometry to produce vast amounts of data that can be utilized to decipher protein functions in complicated biological networks. In this review, we discuss recent developments in analytical methods for large-scale protein interactions and the future direction of interaction proteomics.     

Protein-protein interaction site predictions with three-dimensional probability distributions of interacting atoms on protein surfaces

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors.     

A lock-and-key model for protein-protein interactions

MOTIVATION: Protein-protein interaction networks are one of the major post-genomic data sources available to molecular biologists. They provide a comprehensive view of the global interaction structure of an organism's proteome, as well as detailed information on specific interactions. Here we suggest a physical model of protein interactions that can be used to extract additional information at an intermediate level: It enables us to identify proteins which share biological interaction motifs, and also to identify potentially missing or spurious interactions. RESULTS: Our new graph model explains observed interactions between proteins by an underlying interaction of complementary binding domains (lock-and-key model). This leads to a novel graph-theoretical algorithm to identify bipartite subgraphs within protein-protein interaction networks where the underlying data are taken from yeast two-hybrid experimental results. By testing on synthetic data, we demonstrate that under certain modelling assumptions, the algorithm will return correct domain information about each protein in the network. Tests on data from various model organisms show that the local and global patterns predicted by the model are indeed found in experimental data. Using functional and protein structure annotations, we show that bipartite subnetworks can be identified that correspond to biologically relevant interaction motifs. Some of these are novel and we discuss an example involving SH3 domains from the Saccharomyces cerevisiae interactome. AVAILABILITY: The algorithm (in Matlab format) is available (see http://www.maths.strath.ac.uk/~aas96106/lock_key.html).     

Multilevel regulation of protein-protein interactions in biological circuitry

Protein-protein interactions are central to biology and, in this 'post-genomic era', prediction of these interactions has become the goal of many computational efforts. Close inspection of even relatively simple biological regulatory circuitry reveals multiple levels of control of the contributing protein interactions. The fundamental probability that an interaction will occur under a given set of conditions is difficult to predict because the relationship between structure and energy is not known. Layered on this basic difficulty are allosteric control mechanisms involving post-translational modification or small ligand binding. In addition, many biological processes involve multiple protein-protein interactions, some of which may be cooperative or even competitive. Finally, although the emphasis in predicting protein interactions is based on equilibrium thermodynamic principles, kinetics can be a major controlling feature in these systems. This complexity reinforces the necessity of performing detailed quantitative studies of the component interactions of complex biological regulatory systems. Results of such studies will help us to bridge the gap between our knowledge of structure and our understanding of functional biology.     

Identifying Protein-protein Interaction Sites Using Peptide Arrays

Protein-protein interactions mediate most of the processes in the living cell and control homeostasis of the organism. Impaired protein interactions may result in disease, making protein interactions important drug targets. It is thus highly important to understand these interactions at the molecular level. Protein interactions are studied using a variety of techniques ranging from cellular and biochemical assays to quantitative biophysical assays, and these may be performed either with full-length proteins, with protein domains or with peptides. Peptides serve as excellent tools to study protein interactions since peptides can be easily synthesized and allow the focusing on specific interaction sites. Peptide arrays enable the identification of the interaction sites between two proteins as well as screening for peptides that bind the target protein for therapeutic purposes. They also allow high throughput SAR studies. For identification of binding sites, a typical peptide array usually contains partly overlapping 10-20 residues peptides derived from the full sequences of one or more partner proteins of the desired target protein. Screening the array for binding the target protein reveals the binding peptides, corresponding to the binding sites in the partner proteins, in an easy and fast method using only small amount of protein.In this article we describe a protocol for screening peptide arrays for mapping the interaction sites between a target protein and its partners. The peptide array is designed based on the sequences of the partner proteins taking into account their secondary structures. The arrays used in this protocol were Celluspots arrays prepared by INTAVIS Bioanalytical Instruments. The array is blocked to prevent unspecific binding and then incubated with the studied protein. Detection using an antibody reveals the binding peptides corresponding to the specific interaction sites between the proteins.     

Domain-mediated protein interaction prediction: From genome to network

Protein-protein interactions (PPIs), involved in many biological processes such as cellular signaling, are ultimately encoded in the genome. Solving the problem of predicting protein interactions from the genome sequence will lead to increased understanding of complex networks, evolution and human disease. We can learn the relationship between genomes and networks by focusing on an easily approachable subset of high-resolution protein interactions that are mediated by peptide recognition modules (PRMs) such as PDZ, WW and SH3 domains. This review focuses on computational prediction and analysis of PRM-mediated networks and discusses sequence- and structure-based interaction predictors, techniques and datasets for identifying physiologically relevant PPIs, and interpreting high-resolution interaction networks in the context of evolution and human disease.     

Charting plant interactomes: possibilities and challenges

Protein-protein interactions are essential for nearly all cellular processes. Therefore, an important goal of post-genomic research for defining gene function and understanding the function of macromolecular complexes involves creating 'interactome' maps from empirical or inferred datasets. Systematic efforts to conduct high-throughput surveys of protein-protein interactions in plants are needed to chart the complex and dynamic interaction networks that occur throughout plant development. However, no single approach can build a complete map of the interactome. Here, we review the utility and potential of various experimental approaches for creating large-scale protein-protein interaction maps in plants. Bioinformatics approaches for curating and assessing the confidence of these datasets through inter-species comparisons will be crucial in achieving a complete understanding of protein interaction networks in plants.     

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.     

Predicting active site residue annotations in the Pfam database

Background

The recent increase in the use of high-throughput two-hybrid analysis has generated large quantities of data on protein interactions. Specifically, the availability of information about experimental protein-protein interactions and other protein features on the Internet enables human protein-protein interactions to be computationally predicted from co-evolution events (interolog). This study also considers other protein interaction features, including sub-cellular localization, tissue-specificity, the cell-cycle stage and domain-domain combination. Computational methods need to be developed to integrate these heterogeneous biological data to facilitate the maximum accuracy of the human protein interaction prediction.

Results

This study proposes a relative conservation score by finding maximal quasi-cliques in protein interaction networks, and considering other interaction features to formulate a scoring method. The scoring method can be adopted to discover which protein pairs are the most likely to interact among multiple protein pairs. The predicted human protein-protein interactions associated with confidence scores are derived from six eukaryotic organisms – rat, mouse, fly, worm, thale cress and baker's yeast.

Conclusion

Evaluation results of the proposed method using functional keyword and Gene Ontology (GO) annotations indicate that some confidence is justified in the accuracy of the predicted interactions. Comparisons among existing methods also reveal that the proposed method predicts human protein-protein interactions more accurately than other interolog-based methods.     

Evaluation of different domain-based methods in protein interaction prediction

Protein-protein interactions (PPIs) play an important role in many biological functions. PPIs typically involve binding between domains, the basic units of protein folding, evolution and function. Identifying domain-domain interactions (DDIs) would aid understanding PPI networks. Recently, many computational methods aimed to infer DDIs from databases of interacting proteins and subsequently used the inferred DDIs to predict new PPIs. We attempt to describe systematically current domain-based approaches including the association method, maximum likelihood estimation and parsimonious explanation method. The performance of these methods at inferring DDIs and predicting PPIs was evaluated comparatively. We observe that each method generates artefacts in certain situations and discuss biases in the available benchmark sets.     

Protein complex prediction using an integrative bioinformatics approach

Since protein complexes play a crucial role in biological cells, one of the major goals in bioinformatics is the elucidation of protein complexes. A general approach is to build a prediction rule based on multiple data sources, e.g. gene expression data and protein interaction data, to assess the likelihood of two proteins having complex association. We critically revisit the step of predictor construction, i.e. the determination of a proper training set, an optimal classifier, and, most importantly, an optimal feature set. We use an exhaustive set of features, which includes the 2hop-feature as introduced by Wong et al. for predicting synthetic sick or lethal interactions. Post-processing of the likelihoods of protein interaction is then required to extract protein complexes. We propose a new protocol for combining these likelihood estimates. The protocol interprets the probabilities of complex association as output by the prediction rule as distances and employs hierarchical clustering to find groups of interacting proteins. In contrast to the computationally expensive search-and-score approach of Sharan et al., this protocol is very fast and can be applied to fully connected graphs. The protocol identifies trusted protein complexes with high confidence. We show that the 2hop-feature is relevant for predicting protein complexes. Furthermore, several interesting hypotheses about new protein complexes have been generated. For example, our approach linked the protein FYV4 to the mitochondrial ribosomal subunit. Interestingly, it is known that this protein is located in the mitochondrion, but its biological role is unknown. Vid22 and YGR071C were also linked, which corresponds to the new TAP data of Krogan et al.