Global approaches to protein-protein interactions

Protein-protein docking and molecular dynamics studies of sericin and cocoonase of silkworm: an insight for cocoon softening

Cocoonase is known to digest the sericin protein that encapsulates the silkworm cocoon's fibroin protein. Silk fibroin and sericin are two types of proteins that make up silk, and accounts for around 20-30% of the overall cocoon weight. The aim of the study was to see the protein-protein interaction (PPI) and molecular dynamic study of sericin, cocoonase and protein-protein docked complex of silkworm by computational approaches. Here motif analysis, phylogenetic analysis, principal component analysis, root-mean-square deviation (RMSD), root mean square fluctuation, radius of gyration, structural and functional study of cocoonase and sericin as well as molecular docking study were carried out. The 33 amino acid residues of cocoonase shows interaction with 38 aa residues of sericin involving 4 disulphide bonds, 22 hydrogen bonds and 319 non-bonded contacts. The confirmational stability and flexibility of both the proteins as well as protein-protein complex were achieved at 70 ns of MD simulation study. RMSD-based data indicated that cocoonase is more stable than sericin and complex, and complex has a greater fluctuation with more compact (higher Rg) value than cocoonase and sericin, inferring higher conformational stability and flexibility of protein-protein complex than cocoonase and sericin. This study provides a new dimension for PPI study by computational approaches.Communicated by Ramaswamy H. Sarma.

Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning

Protein-protein interactions (PPIs) govern cellular pathways and processes, by significantly influencing the functional expression of proteins. Therefore, accurate identification of protein-protein interaction binding sites has become a key step in the functional analysis of proteins. However, since most computational methods are designed based on biological features, there are no available protein language models to directly encode amino acid sequences into distributed vector representations to model their characteristics for protein-protein binding events. Moreover, the number of experimentally detected protein interaction sites is much smaller than that of protein-protein interactions or protein sites in protein complexes, resulting in unbalanced data sets that leave room for improvement in their performance. To address these problems, we develop an ensemble deep learning model (EDLM)-based protein-protein interaction (PPI) site identification method (EDLMPPI). Evaluation results show that EDLMPPI outperforms state-of-the-art techniques including several PPI site prediction models on three widely-used benchmark datasets including Dset_448, Dset_72, and Dset_164, which demonstrated that EDLMPPI is superior to those PPI site prediction models by nearly 10% in terms of average precision. In addition, the biological and interpretable analyses provide new insights into protein binding site identification and characterization mechanisms from different perspectives. The EDLMPPI webserver is available at http://www.edlmppi.top:5002/ .

ctP2ISP: Protein-Protein Interaction Sites Prediction Using Convolution and Transformer With Data Augmentation

Protein-protein interactions are the basis of many cellular biological processes, such as cellular organization, signal transduction, and immune response. Identifying protein-protein interaction sites is essential for understanding the mechanisms of various biological processes, disease development, and drug design. However, it remains a challenging task to make accurate predictions, as the small amount of training data and severe imbalanced classification reduce the performance of computational methods. We design a deep learning method named ctP²ISP to improve the prediction of protein-protein interaction sites. ctP²ISP employs Convolution and Transformer to extract information and enhance information perception so that semantic features can be mined to identify protein-protein interaction sites. A weighting loss function with different sample weights is designed to suppress the preference of the model toward multi-category prediction. To efficiently reuse the information in the training set, a preprocessing of data augmentation with an improved sample-oriented sampling strategy is applied. The trained ctP²ISP was evaluated against current state-of-the-art methods on six public datasets. The results show that ctP²ISP outperforms all other competing methods on the balance metrics: F1, MCC, and AUPRC. In particular, our prediction on open tests related to viruses may also be consistent with biological insights. The source code and data can be obtained from https://github.com/lennylv/ctP2ISP.

Protein-protein interaction prediction with deep learning: A comprehensive review

Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein-protein interaction and protein-ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein-protein interaction and their sites, protein-ligand binding, and protein design.

Potential application of TurboID-based proximity labeling in studying the protein interaction network in plant response to abiotic stress

Abiotic stresses are major environmental conditions that reduce plant growth, productivity and quality. Protein-protein interaction (PPI) approaches can be used to screen stress-responsive proteins and reveal the mechanisms of protein response to various abiotic stresses. Biotin-based proximity labeling (PL) is a recently developed technique to label proximal proteins of a target protein. TurboID, a biotin ligase produced by directed evolution, has the advantages of non-toxicity, time-saving and high catalytic efficiency compared to other classic protein-labeling enzymes. TurboID-based PL has been successfully applied in animal, microorganism and plant systems, particularly to screen transient or weak protein interactions, and detect spatially or temporally restricted local proteomes in living cells. This review concludes classic PPI approaches in plant response to abiotic stresses and their limitations for identifying complex network of regulatory proteins of plant abiotic stresses, and introduces the working mechanism of TurboID-based PL, as well as its feasibility and advantages in plant abiotic stress research. We hope the information summarized in this article can serve as technical references for further understanding the regulation of plant adaptation to abiotic stress at the protein level.

Bacterial Protein Interaction Networks: Connectivity is Ruled by Gene Conservation, Essentiality and Function

BACKGROUND

Protein-protein interaction (PPI) networks are the backbone of all processes in living cells. In this work, we relate conservation, essentiality and functional repertoire of a gene to the connectivity k (i.e. the number of interactions, links) of the corresponding protein in the PPI network.

METHODS

On a set of 42 bacterial genomes of different sizes, and with reasonably separated evolutionary trajectories, we investigate three issues: i) whether the distribution of connectivities changes between PPI subnetworks of essential and nonessential genes; ii) how gene conservation, measured both by the evolutionary retention index (ERI) and by evolutionary pressures, is related to the connectivity of the corresponding protein; iii) how PPI connectivities are modulated by evolutionary and functional relationships, as represented by the Clusters of Orthologous Genes (COGs).

RESULTS

We show that conservation, essentiality and functional specialisation of genes constrain the connectivity of the corresponding proteins in bacterial PPI networks. In particular, we isolated a core of highly connected proteins (connectivities k≥40), which is ubiquitous among the species considered here, though mostly visible in the degree distributions of bacteria with small genomes (less than 1000 genes).

CONCLUSION

The genes that support this highly connected core are conserved, essential and, in most cases, belong to the COG cluster J, related to ribosomal functions and the processing of genetic information.

Analysis of Interacting Proteins of Aluminum Toxicity Response Factor ALS3 and CAD in Citrus

Aluminum (Al) treatment significantly decreased the dry weight (DW) of stem, shoot and whole plant of both Citrus sinensis and C. grandis, but did not change that of root. Al significantly decreased leaf DW of C. grandis, increased the ratio of root to shoot and the lignin content in roots of both species. The higher content of Al in leaves and stems and lignin in roots of C. grandis than that of C. sinensis might be due to the over-expression of Al sensitive 3 (ALS3) and cinnamyl alcohol deaminase (CAD) in roots of C. grandis, respectively. By using yeast-two-hybridazation (Y2H) and bimolecular fluorescence complementation (BiFC) techniques, we obtained the results that glutathione S-transferase (GST), vacuolar-type proton ATPase (V-ATPase), aquaporin PIP2 (PIP2), ubiquitin carboxyl-terminal hydrolase 13 (UCT13), putative dicyanin blue copper protein (DCBC) and uncharacterized protein 2 (UP2) were interacted with ALS3 and GST, V-ATPase, Al sensitive 3 (ALS3), cytochrome P450 (CP450), PIP2, uncharacterized protein 1 (UP1) and UP2 were interacted with CAD. Annotation analysis revealed that these proteins were involved in detoxification, cellular transport, post-transcriptional modification and oxidation-reduction homeostasis or lignin biosynthesis in plants. Real-time quantitative PCR (RT-qPCR) analysis further revealed that the higher gene expression levels of most of these interacting proteins in C. grandis roots than that in C. sinensis ones were consistent with the higher contents of lignin in C. grandis roots and Al absorbed by C. grandis. In conclusion, our study identified some key interacting components of Al responsive proteins ALS3 and CAD, which could further help us to understand the molecular mechanism of Al tolerance in citrus plants and provide new information to the selection and breeding of tolerant cultivars, which are cultivated in acidic areas.

Two-dimensional blue native/SDS-PAGE analysis of whole cell lysate protein complexes of rice in response to salt stress

To understand the biology of a plant in response to stress, insight into protein-protein interactions, which almost define cell behavior, is thought to be crucial. Here, we provide a comparative complexomics analysis of leaf whole cell lysate of two rice genotypes with contrasting responses to salt using two-dimensional blue native/SDS-PAGE (2D-BN/SDS-PAGE). We aimed to identify changes in subunit composition and stoichiometry of protein complexes elicited by salt. Using mild detergent for protein complex solubilization, we were able to identify 9 protein assemblies as hetero-oligomeric and 30 as homo-oligomeric complexes. A total of 20 proteins were identified as monomers in the 2D-BN/SDS-PAGE gels. In addition to identifying known protein complexes that confirm the technical validity of our analysis, we were also able to discover novel protein-protein interactions. Interestingly, an interaction was detected for glycolytic enzymes enolase (ENO1) and triosephosphate isomerase (TPI) and also for a chlorophyll a-b binding protein and RuBisCo small subunit. To show changes in subunit composition and stoichiometry of protein assemblies during salt stress, the differential abundance of interacting proteins was compared between salt-treated and control plants. A detailed exploration of some of the protein complexes provided novel insight into the function, composition, stoichiometry and dynamics of known and previously uncharacterized protein complexes in response to salt stress.

Rapid immunoprecipitation mass spectrometry of endogenous proteins (RIME) for analysis of chromatin complexes

Rapid immunoprecipitation mass spectrometry of endogenous protein (RIME) is a method that allows the study of protein complexes, in particular chromatin and transcription factor complexes, in a rapid and robust manner by mass spectrometry (MS). The method can be used in parallel with chromatin immunoprecipitation-sequencing (ChIP-seq) experiments to provide information on both the cistrome and interactome for a given protein. The method uses formaldehyde fixation to stabilize protein complexes. By using antibodies against the endogenous target, the cross-linked complex is immunoprecipitated, rigorously washed, and then digested into peptides while avoiding antibody contamination (on-bead digestion). By using this method, MS identification of the target protein and several dozen interacting proteins is possible using a 100-min LC-MS/MS run. The protocol does not require substantial proteomics expertise, and it typically takes 2-3 d from the collection of material to results.

Towards a Hierarchical Strategy to Explore Multi-Scale IP/MS Data for Protein Complexes

Protein interaction in cells can be described at different levels. At a low interaction level, proteins function together in small, stable complexes and at a higher level, in sets of interacting complexes. All interaction levels are crucial for the living organism, and one of the challenges in proteomics is to measure the proteins at their different interaction levels. One common method for such measurements is immunoprecipitation followed by mass spectrometry (IP/MS), which has the potential to probe the different protein interaction forms. However, IP/MS data are complex because proteins, in their diverse interaction forms, manifest themselves in different ways in the data. Numerous bioinformatic tools for finding protein complexes in IP/MS data are currently available, but most tools do not provide information about the interaction level of the discovered complexes, and no tool is geared specifically to unraveling and visualizing these different levels. We present a new bioinformatic tool to explore IP/MS datasets for protein complexes at different interaction levels and show its performance on several real–life datasets. Our tool creates clusters that represent protein complexes, but unlike previous methods, it arranges them in a tree–shaped structure, reporting why specific proteins are predicted to build a complex and where it can be divided into smaller complexes. In every data analysis method, parameters have to be chosen. Our method can suggest values for its parameters and comes with adapted visualization tools that display the effect of the parameters on the result. The tools provide fast graphical feedback and allow the user to interact with the data by changing the parameters and examining the result. The tools also allow for exploring the different organizational levels of the protein complexes in a given dataset. Our method is available as GNU-R source code and includes examples at www.bdagroup.nl.

Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network

To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes' Ontology Fingerprints--a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms' corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general.

Proteomic strategies for the discovery of novel diagnostic and therapeutic targets for infectious diseases

Viruses have developed numerous and elegant strategies to manipulate the host cell machinery to establish a productive infectious cycle. The interaction of viral proteins with host proteins plays an important role in infection and pathogenesis, often bypassing traditional host defenses such as the interferon response and apoptosis. Host–viral protein interactions can be studied using a variety of proteomic approaches ranging from genetic and biochemical to large‐scale high‐throughput technologies. Protein interactions between host and viral proteins are greatly influenced by host signal transduction pathways. In this review, we will focus on comparing proteomic information obtained through differing technologies and how their integration can be used to determine the functional aspect of the host response to infection. We will briefly review and evaluate techniques employed to elucidate viral–host interactions with a primary focus on Protein Microarrays (PMA) and Mass Spectrometry (MS) as potential tools in the discovery of novel therapeutic targets. As many potential molecular markers and targets are proteins, proteomic profiling is expected to yield both clearer and more direct answers to functional and pharmacologic questions.

Periodontal proteomics: wonders never cease!

Proteins are vital parts of living organisms, as they are integral components of the physiological metabolic pathways of cells. Periodontal tissues comprise multicompartmental groups of interacting cells and matrices that provide continuous support, attachment, proprioception, and physical protection for the teeth. The proteome map, that is, complete catalogue of the matrix and cellular proteins expressed in alveolar bone, cementum, periodontal ligament, and gingiva, is to be explored for more in-depth understanding of periodontium. The ongoing research to understand the signalling pathways that allow cells to divide, differentiate, and die in controlled manner has brought us to the era of proteomics. Proteomics is defined as the study of all proteins including their relative abundance, distribution, posttranslational modifications, functions, and interactions with other macromolecules, in a given cell or organism within a given environment and at a specific stage in the cell cycle. Its application to periodontal science can be used to monitor health status, disease onset, treatment response, and outcome. Proteomics can offer answers to critical, unresolved questions such as the biological basis for the heterogeneity in gingival, alveolar bone, and cemental cell populations.

Inferring protein-protein interaction complexes from immunoprecipitation data

Background

Protein–protein interactions in cells are widely explored using small–scale experiments. However, the search for protein complexes and their interactions in data from high throughput experiments such as immunoprecipitation is still a challenge. We present "4N", a novel method for detecting protein complexes in such data. Our method is a heuristic algorithm based on Near Neighbor Network (3N) clustering. It is written in R, it is faster than model-based methods, and has only a small number of tuning parameters. We explain the application of our new method to real immunoprecipitation results and two artificial datasets. We show that the method can infer protein complexes from protein immunoprecipitation datasets of different densities and sizes.

Findings

4N was applied on the immunoprecipitation dataset that was presented by the authors of the original 3N in Cell 145:787–799, 2011. The test with our method shows that it can reproduce the original clustering results with fewer manually adapted parameters and, in addition, gives direct insight into the complex–complex interactions. We also tested 4N on the human "Tip49a/b" dataset. We conclude that 4N can handle the contaminants and can correctly infer complexes from this very dense dataset. Further tests were performed on two artificial datasets of different sizes. We proved that the method predicts the reference complexes in the two artificial datasets with high accuracy, even when the number of samples is reduced.

Conclusions

4N has been implemented in R. We provide the sourcecode of 4N and a user-friendly toolbox including two example calculations. Biologists can use this 4N-toolbox even if they have a limited knowledge of R. There are only a few tuning parameters to set, and each of these parameters has a biological interpretation. The run times for medium scale datasets are in the order of minutes on a standard desktop PC. Large datasets can typically be analyzed within a few hours.

Protein interaction networks as starting points to identify novel antimicrobial drug targets

Novel classes of antimicrobials are needed to address the challenge of multidrug-resistant bacteria. Current bacterial drug targets mainly consist of specific proteins or subsets of proteins without regard for either how these targets are integrated in cellular networks or how they may interact with host proteins. However, proteins rarely act in isolation, and the majority of biological processes are dependent on interactions with other proteins. Consequently, protein-protein interaction (PPI) networks offer a realm of unexplored potential for next-generation drug targets. In this review, we argue that the architecture of bacterial or host-pathogen protein interactomes can provide invaluable insights for the identification of novel antibacterial drug targets.

The origins of the evolutionary signal used to predict protein-protein interactions

Background

The correlation of genetic distances between pairs of protein sequence alignments has been used to infer protein-protein interactions. It has been suggested that these correlations are based on the signal of co-evolution between interacting proteins. However, although mutations in different proteins associated with maintaining an interaction clearly occur (particularly in binding interfaces and neighbourhoods), many other factors contribute to correlated rates of sequence evolution. Proteins in the same genome are usually linked by shared evolutionary history and so it would be expected that there would be topological similarities in their phylogenetic trees, whether they are interacting or not. For this reason the underlying species tree is often corrected for. Moreover processes such as expression level, are known to effect evolutionary rates. However, it has been argued that the correlated rates of evolution used to predict protein interaction explicitly includes shared evolutionary history; here we test this hypothesis.

Results

In order to identify the evolutionary mechanisms giving rise to the correlations between interaction proteins, we use phylogenetic methods to distinguish similarities in tree topologies from similarities in genetic distances. We use a range of datasets of interacting and non-interacting proteins from Saccharomyces cerevisiae. We find that the signal of correlated evolution between interacting proteins is predominantly a result of shared evolutionary rates, rather than similarities in tree topology, independent of evolutionary divergence.

Conclusions

Since interacting proteins do not have tree topologies that are more similar than the control group of non-interacting proteins, it is likely that coevolution does not contribute much to, if any, of the observed correlations.

The study of protein–protein interactions in bacteria

Many of the functions fulfilled by proteins in the cell require specific protein–protein interactions (PPI). During the last decade, the use of high-throughput experimental technologies, primarily based on the yeast 2-hybrid system, generated extensive data currently located in public databases. This information has been used to build interaction networks for different species. Unfortunately, due to the nature of the yeast 2-hybrid system, these databases contain many false positives and negatives, thus they require purging. A method for confirming these PPI is to test them using a technique that operates in vivo and detects binary PPI. This article comprises an overview of the study of PPI and describes the main techniques that have been used to identify bacterial PPI, prioritizing those that can be used for their verification, and it also mentions a number of PPI that have been identified or confirmed using these methods.

Interactions of hemoglobin in live red blood cells measured by the electrophoresis release test

Electrophoresis release test (ERT) is the starch-agarose mixed gel electrophoresis of live red blood cells (RBCs). Mixed gel electrophoresis used to be one of the classic methods to isolate proteins, and in our laboratory, this technique is usually performed to isolate hemoglobins. Recently, combined with sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and liquid chromatography coupled with tandem mass spectrometry (LC/MS/MS), ERT has been used to study the interactions between hemoglobin and other proteins in live RBCs.

SAPIN: a framework for the structural analysis of protein interaction networks

SUMMARY

Protein interaction networks are widely used to depict the relationships between proteins. These networks often lack the information on physical binary interactions, and they do not inform whether there is incompatibility of structure between binding partners. Here, we introduce SAPIN, a framework dedicated to the structural analysis of protein interaction networks. SAPIN first identifies the protein parts that could be involved in the interaction and provides template structures. Next, SAPIN performs structural superimpositions to identify compatible and mutually exclusive interactions. Finally, the results are displayed using Cytoscape Web.

AVAILABILITY

The SAPIN server is available at http://sapin.crg.es.

CONTACT

jae-seong.yang@crg.eu or christina.kiel@crg.eu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics Online.

Comparison of tertiary structures of proteins in protein-protein complexes with unbound forms suggests prevalence of allostery in signalling proteins

BACKGROUND

Most signalling and regulatory proteins participate in transient protein-protein interactions during biological processes. They usually serve as key regulators of various cellular processes and are often stable in both protein-bound and unbound forms. Availability of high-resolution structures of their unbound and bound forms provides an opportunity to understand the molecular mechanisms involved. In this work, we have addressed the question "What is the nature, extent, location and functional significance of structural changes which are associated with formation of protein-protein complexes?"

RESULTS

A database of 76 non-redundant sets of high resolution 3-D structures of protein-protein complexes, representing diverse functions, and corresponding unbound forms, has been used in this analysis. Structural changes associated with protein-protein complexation have been investigated using structural measures and Protein Blocks description. Our study highlights that significant structural rearrangement occurs on binding at the interface as well as at regions away from the interface to form a highly specific, stable and functional complex. Notably, predominantly unaltered interfaces interact mainly with interfaces undergoing substantial structural alterations, revealing the presence of at least one structural regulatory component in every complex.Interestingly, about one-half of the number of complexes, comprising largely of signalling proteins, show substantial localized structural change at surfaces away from the interface. Normal mode analysis and available information on functions on some of these complexes suggests that many of these changes are allosteric. This change is largely manifest in the proteins whose interfaces are altered upon binding, implicating structural change as the possible trigger of allosteric effect. Although large-scale studies of allostery induced by small-molecule effectors are available in literature, this is, to our knowledge, the first study indicating the prevalence of allostery induced by protein effectors.

CONCLUSIONS

The enrichment of allosteric sites in signalling proteins, whose mutations commonly lead to diseases such as cancer, provides support for the usage of allosteric modulators in combating these diseases.

Time-controlled transcardiac perfusion crosslinking for in vivo interactome studies

The time-controlled transcardiac perfusion crosslinking (tcTPC) method differs from conventional perfusion fixation in that the crosslinking reagent is administered throughout the circulatory system for only a relatively short period of time, thereby allowing limited crosslinking to occur. Bait protein complexes are isolated by affinity capture (AC) under stringent conditions and are recovered from the AC matrix by acidic elution. Affinity-purified proteins are reduced, alkylated, and digested with a specific endoproteinase, such as trypsin. Subsequently, peptides are isotopically labeled, separated by reversed-phase chromatography and analyzed by quantitative tandem mass spectrometry (MS/MS). The proteins crosslinked to the bait protein during tcTPC are identified by database searches with conventional protein identification software. The tcTPC strategy offers unique advantages over alternative approaches for studying a subset of protein complexes which require a particular environment for their structural integrity, such as membrane protein complexes that are notorious for their tendency to dissociate upon detergent solubilization. The sensitivity and utility of this method are influenced by the spatial distribution of chemical groups within the bait protein complexes that can engage in productive crosslinks.

A novel method for prediction of protein interaction sites based on integrated RBF neural networks

Protein interactions are very important for control life activities. If we want to study the principle of protein interactions, we have to find the seats of a protein which are involved in the interactions called interaction sites firstly. In this paper, a novel method based on an integrated RBF neural networks is proposed for prediction of protein interaction sites. At first, a number of features were extracted, i.e., sequence profiles, entropy, relative entropy, conservation weight, accessible surface area and sequence variability. Then 6 sliding windows about these features were made, and they contained 1, 3, 5, 7, 9 and 11 amino acid residues respectively. These sliding windows were put into the input layers of six radial basis functional neural networks that were optimized by Particle Swarm Optimization. Thus, six group results were obtained. Finally, these six group results were integrated by decision fusion (DF) and Genetic Algorithm based Selective Ensemble (GASEN). The experimental results show that the proposed method performs better than the other related methods such as neural networks and support vector machine.

Network approaches to the functional analysis of microbial proteins

Large amounts of detailed biological data have been generated over the past few decades. Much of these data is freely available in over 1000 online databases; an enticing, but frustrating resource for microbiologists interested in a systems-level view of the structure and function of microbial cells. The frustration engendered by the need to trawl manually through hundreds of databases in order to accumulate information about a gene, protein, pathway, or organism of interest can be alleviated by the use of computational data integration to generated network views of the system of interest. Biological networks can be constructed from a single type of data, such as protein-protein binding information, or from data generated by multiple experimental approaches. In an integrated network, nodes usually represent genes or gene products, while edges represent some form of interaction between the nodes. Edges between nodes may be weighted to represent the probability that the edge exists in vivo. Networks may also be enriched with ontological annotations, facilitating both visual browsing and computational analysis via web service interfaces. In this review, we describe the construction, analysis of both single-data source and integrated networks, and their application to the inference of protein function in microbes.

Interactome data and databases: different types of protein interaction

In recent years, the biomolecular sciences have been driven forward by overwhelming advances in new biotechnological high-throughput experimental methods and bioinformatic genome-wide computational methods. Such breakthroughs are producing huge amounts of new data that need to be carefully analysed to obtain correct and useful scientific knowledge. One of the fields where this advance has become more intense is the study of the network of ‘protein–protein interactions’, i.e. the ‘interactome’. In this short review we comment on the main data and databases produced in this field in last 5 years. We also present a rationalized scheme of biological definitions that will be useful for a better understanding and interpretation of ‘what a protein–protein interaction is’ and ‘which types of protein–protein interactions are found in a living cell’. Finally, we comment on some assignments of interactome data to defined types of protein interaction and we present a new bioinformatic tool called APIN (Agile Protein Interaction Network browser), which is in development and will be applied to browsing protein interaction databases.

TRIP Database: a manually curated database of protein-protein interactions for mammalian TRP channels

Transient receptor potential (TRP) channels are a superfamily of Ca²⁺-permeable cation channels that translate cellular stimuli into electrochemical signals. Aberrant activity of TRP channels has been implicated in a variety of human diseases, such as neurological disorders, cardiovascular disease and cancer. To facilitate the understanding of the molecular network by which TRP channels are associated with biological and disease processes, we have developed the TRIP (TRansient receptor potential channel-Interacting Protein) Database (http://www.trpchannel.org), a manually curated database that aims to offer comprehensive information on protein–protein interactions (PPIs) of mammalian TRP channels. The TRIP Database was created by systematically curating 277 peer-reviewed literature; the current version documents 490 PPI pairs, 28 TRP channels and 297 cellular proteins. The TRIP Database provides a detailed summary of PPI data that fit into four categories: screening, validation, characterization and functional consequence. Users can find in-depth information specified in the literature on relevant analytical methods and experimental resources, such as gene constructs and cell/tissue types. The TRIP Database has user-friendly web interfaces with helpful features, including a search engine, an interaction map and a function for cross-referencing useful external databases. Our TRIP Database will provide a valuable tool to assist in understanding the molecular regulatory network of TRP channels.

Interactions of hemoglobin in live red blood cells measured by the electrophoresis release test

To elucidate the protein-protein interactions of hemoglobin (Hb) variants A and A(2), HbA was first shown to bind with HbA(2) in live red blood cells (RBCs) by diagonal electrophoresis and then the interaction between HbA and HbA(2) outside the RBC was shown by cross electrophoresis. The starch-agarose gel electrophoresis of hemolysate, RBCs, freeze-thawed RBCs and the supernatant of freeze-thawed RBCs showed that the interaction between HbA and HbA(2) was affected by membrane integrity. To identify the proteins involved in the interaction, protein components located between HbA and HbA(2) in RBCs (RBC HbA-HbA(2)) and hemolysate (hemolysate HbA-HbA(2)) were isolated from the starch-agarose gel and separated by 5-12% SDS-PAGE. The results showed that there was a ≈22 kDa protein band located in the RBC HbA-HbA(2) but not in hemolysate HbA-HbA(2). Sequencing by LC/MS/MS showed that this band was a protein complex that included mainly thioredoxin peroxidase B, α-globin, δ-globin and β-globin. Thus, using our unique in vivo whole blood cell electrophoresis release test, Hbs were proven for the first time to interact with other proteins in the live RBC.

Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP

BACKGROUND

A new paradigm of biological investigation takes advantage of technologies that produce large high throughput datasets, including genome sequences, interactions of proteins, and gene expression. The ability of biologists to analyze and interpret such data relies on functional annotation of the included proteins, but even in highly characterized organisms many proteins can lack the functional evidence necessary to infer their biological relevance.

RESULTS

Here we have applied high confidence function predictions from our automated prediction system, PFP, to three genome sequences, Escherichia coli, Saccharomyces cerevisiae, and Plasmodium falciparum (malaria). The number of annotated genes is increased by PFP to over 90% for all of the genomes. Using the large coverage of the function annotation, we introduced the functional similarity networks which represent the functional space of the proteomes. Four different functional similarity networks are constructed for each proteome, one each by considering similarity in a single Gene Ontology (GO) category, i.e. Biological Process, Cellular Component, and Molecular Function, and another one by considering overall similarity with the funSim score. The functional similarity networks are shown to have higher modularity than the protein-protein interaction network. Moreover, the funSim score network is distinct from the single GO-score networks by showing a higher clustering degree exponent value and thus has a higher tendency to be hierarchical. In addition, examining function assignments to the protein-protein interaction network and local regions of genomes has identified numerous cases where subnetworks or local regions have functionally coherent proteins. These results will help interpreting interactions of proteins and gene orders in a genome. Several examples of both analyses are highlighted.

CONCLUSION

The analyses demonstrate that applying high confidence predictions from PFP can have a significant impact on a researchers' ability to interpret the immense biological data that are being generated today. The newly introduced functional similarity networks of the three organisms show different network properties as compared with the protein-protein interaction networks.

Deciphering soluble and membrane protein function using yeast systems (Review)

Membrane protein-protein interactions are important for regulation, targeting, and activity of proteins in membranes but are difficult to detect and analyse. This review covers current approaches to studying membrane protein interactions. In addition to standard biochemical and genetic techniques, the classic yeast nuclear two-hybrid system has been highly successful in identification and characterization of soluble protein-protein interactions. However, classic yeast two-hybrid assays do not work for membrane proteins because such yeast-based interactions must occur in the nucleus. Here, we highlight recent advances in yeast systems for the detection and characterization of eukaryote membrane protein-protein interactions. We discuss these implications for drug screening and discovery.

A general system for studying protein-protein interactions in Gram-negative bacteria

One of the most promising methods for large-scale studies of protein interactions is isolation of an affinity-tagged protein with its in vivo interaction partners, followed by mass spectrometric identification of the copurified proteins. Previous studies have generated affinity-tagged proteins using genetic tools or cloning systems that are specific to a particular organism. To enable protein-protein interaction studies across a wider range of Gram-negative bacteria, we have developed a methodology based on expression of affinity-tagged "bait" proteins from a medium copy-number plasmid. This construct is based on a broad-host-range vector backbone (pBBR1MCS5). The vector has been modified to incorporate the Gateway DEST vector recombination region, to facilitate cloning and expression of fusion proteins bearing a variety of affinity, fluorescent, or other tags. We demonstrate this methodology by characterizing interactions among subunits of the DNA-dependent RNA polymerase complex in two metabolically versatile Gram-negative microbial species of environmental interest, Rhodopseudomonas palustris CGA010 and Shewanella oneidensis MR-1. Results compared favorably with those for both plasmid and chromosomally encoded affinity-tagged fusion proteins expressed in a model organism, Escherichia coli.

From pull-down data to protein interaction networks and complexes with biological relevance

MOTIVATION

Recent improvements in high-throughput Mass Spectrometry (MS) technology have expedited genome-wide discovery of protein-protein interactions by providing a capability of detecting protein complexes in a physiological setting. Computational inference of protein interaction networks and protein complexes from MS data are challenging. Advances are required in developing robust and seamlessly integrated procedures for assessment of protein-protein interaction affinities, mathematical representation of protein interaction networks, discovery of protein complexes and evaluation of their biological relevance.

RESULTS

A multi-step but easy-to-follow framework for identifying protein complexes from MS pull-down data is introduced. It assesses interaction affinity between two proteins based on similarity of their co-purification patterns derived from MS data. It constructs a protein interaction network by adopting a knowledge-guided threshold selection method. Based on the network, it identifies protein complexes and infers their core components using a graph-theoretical approach. It deploys a statistical evaluation procedure to assess biological relevance of each found complex. On Saccharomyces cerevisiae pull-down data, the framework outperformed other more complicated schemes by at least 10% in F(1)-measure and identified 610 protein complexes with high-functional homogeneity based on the enrichment in Gene Ontology (GO) annotation. Manual examination of the complexes brought forward the hypotheses on cause of false identifications. Namely, co-purification of different protein complexes as mediated by a common non-protein molecule, such as DNA, might be a source of false positives. Protein identification bias in pull-down technology, such as the hydrophilic bias could result in false negatives.

Microsystems technology and biosensing

This review addresses the recent developments in miniaturized microsystems or lab-on-a-chip devices for biosensing of different biomolecules: DNA, proteins, small molecules, and cells, especially at the single-molecule and single-cell level. In order to sense these biomolecules with sensitivity we have fabricated chip devices with respect to the biomolecule to be analyzed. The details of the fabrication are also dealt with in this review. We mainly developed microarray and microfluidic chip devices for DNA, protein, and cell analyses. In addition, we have introduced the porous anodic alumina layer chip with nanometer scale and gold nanoparticles for label-free sensing of DNA and protein interactions. We also describe the use of microarray and microfluidic chip devices for cell-based assays and single-cell analysis in drug discovery research.

Methods for the detection and analysis of protein-protein interactions

A large number of methods have been developed over the years to study protein-protein interactions. Many of these techniques are now available to the nonspecialist researcher thanks to new affordable instruments and/or resource centres. A typical protein-protein interaction study usually starts with an initial screen for novel binding partners. We start this review by describing three techniques that can be used for this purpose: (i) affinity-tagged proteins (ii) the two-hybrid system and (iii) some quantitative proteomic techniques that can be used in combination with, e.g., affinity chromatography and coimmunoprecipitation for screening of protein-protein interactions. We then describe some public protein-protein interaction databases that can be searched to identify previously reported interactions for a given bait protein. Four strategies for validation of protein-protein interactions are presented: confocal microscopy for intracellular colocalization of proteins, coimmunoprecipitation, surface plasmon resonance (SPR) and spectroscopic studies. Throughout the review we focus particularly on the advantages and limitations of each method.

Human protein–protein interaction networks and the value for drug discovery

Systematic genome-wide and pathway-specific protein-protein interaction screens have generated a putative, organizing framework of the spatial interconnectivity of a large number of human proteins, including numerous therapeutically relevant disease-associated proteins. The intrinsic value for drug discovery is that these physical protein-protein interaction networks may contribute to a mechanistic understanding of the pathophysiology of disease and can aid in the identification and prioritization of tractable targets and generate hypotheses on how to best drug non-tractable, disease-associated targets. Here, we review the 'therapeutic potential' of the 1st generation sub-genome-scale human interaction networks and disease-associated protein networks generated by yeast two-hybrid screens and affinity purification-mass spectrometry approaches.

Pathway Proteomics and Chemical Proteomics Team Up in Drug Discovery

Over the last 5 years, impressive technical advances in mass spectrometry-based analysis of proteins have enabled the parallel analysis of subproteomes and entire proteomes, thus triggering the departure from the traditional single gene-single protein-single target paradigm. Today, immunoaffinity chromatography as well as generic purification methods employing engineered composite affinity tags make streamlined identification of protein complexes as molecular machines possible. In addition, use of stable isotope techniques in protein mass spectrometry allows for the characterization of protein complex composition and posttranslational modifications in an increasingly quantitative fashion. Together, these methodologies allow the elucidation of medically relevant biological pathways, and the study of the interaction of their protein components with therapeutic agents, on a much larger scale. The present review discusses some of the current experimental strategies, with a focus on applications in neurobiology.

Mass spectrometry identifiable cross-linking strategy for studying protein-protein interactions

A new mass spectrometry identifiable cross-linking strategy has been developed to study protein-protein interactions. The new cross-linker was designed to have two low-energy MS/MS-cleavable bonds in the spacer chain to provide three primary benefits: First, a reporter tag can be released from cross-link due to cleavage of the two labile bonds in the spacer chain. Second, a relatively simple MS/MS spectrum can be generated owing to favorable cleavage of labile bonds. And finally, the cross-linked peptide chains are dissociated from each other, and each then can be fragmented separately to get sequence information. Therefore, this novel type of cross-linker was named protein interaction reporter (PIR). To this end, two RINK groups were utilized to make our first-generation cross-linker using solid-phase peptide synthesis chemistry. The RINK group contains a bond more labile than peptide bonds during low-energy activation. The new cross-linker was applied to cross-link ribonuclease S (RNase S), a noncovalent complex of S-peptide and S-protein. The results demonstrated that the new cross-linker effectively reacted with RNase S to generate various types of cross-linked products. More importantly, the cross-linked peptides successfully released reporter ions during selective MS/MS conditions, and the dissociated peptide chains remained intact during MS(2), thus enabling MS(3) to be performed subsequently. In addition, dead-end, intra-, and inter-cross-linked peptides can be distinguished by analyzing MS/MS spectra.

Intracellular protein interaction mapping with FRET hybrids

A quantitative methodology was developed to identify protein interactions in a broad range of cell types by using FRET between fluorescent proteins. Genetic fusions of a target receptor to a FRET acceptor and a large library of candidate peptide ligands to a FRET donor enabled high-throughput optical screening for optimal interaction partners in the cytoplasm of Escherichia coli. Flow cytometric screening identified a panel of peptide ligands capable of recognizing the target receptors in the intracellular environment. For both SH3 and PDZ domain-type target receptors, physiologically meaningful consensus sequences were apparent among the isolated ligands. The relative dissociation constants of interacting partners could be measured directly by using a dilution series of cell lysates containing FRET hybrids, providing a previously undescribed high-throughput approach to rank the affinity of many interaction partners. FRET hybrid interaction screening provides a powerful tool to discover protein ligands in the cellular context with potential applications to a wide variety of eukaryotic cell types.

Multiple Label-Free Detection of Antigen−Antibody Reaction Using Localized Surface Plasmon Resonance-Based Core−Shell Structured Nanoparticle Layer Nanochip

In this research, a localized surface plasmon resonance (LSPR)-based bioanalysis method for developing multiarray optical nanochip suitable for screening bimolecular interactions is described. LSPR-based label-free monitoring enables to solve the problems of conventional methods that require large sample volumes and time-consuming labeling procedures. We developed a multiarray LSPR-based nanochip for the label-free detection of proteins. The multiarray format was constructed by a core-shell-structured nanoparticle layer, which provided 300 nanospots on the sensing surface. Antibodies were immobilized onto the nanospots using their interaction with Protein A. The concentrations of antigens were determined from the peak absorption intensity of the LSPR spectra. We demonstrated the capability of the array measurement using immunoglobulins (IgA, IgD, IgG, IgM), C-reactive protein, and fibrinogen. The detection limit of our label-free method was 100 pg/mL. Our nanochip is readily transferable to monitor the interactions of other biomolecules, such as whole cells or receptors, with a massively parallel detection capability in a highly miniaturized package. We anticipate that the direct label-free optical immunoassay of proteins reported here will revolutionize clinical diagnosis and accelerate the development of hand-held and user-friendly point-of-care devices.

An improved strategy for high-level production of TEV protease in Escherichia coli and its purification and characterization

Because of its stringent sequence specificity, tobacco etch virus (TEV) protease emerges as a useful reagent with wide application in the cleavage of recombinant fusion proteins. However, the solubility of TEV protease expressed in Escherichia coli is extremely low. In the present study, we introduced a more efficient system to improve and facilitate the soluble production of TEV protease in E. coli. Optimal expression of soluble His6-TEV was achieved by examining the contribution of chaperone co-expression and lower temperature fermentation. When further purified by Ni(2+) affinity chromatography, 65mg of His6-TEV was isolated with purity over 95% from 1L of culture. The enzyme activity of His6-TEV was generally characterized by using GST-EGFP and His6-L-TNF fusion protein as substrates, which contained a TEV cleavage site between two moieties.

Cystic fibrosis enters the proteomics scene: New answers to old questions

The discovery in 1989 of the gene encoding for the cystic fibrosis transmembrane conductance regulator (CFTR) and its mutation as the primary cause of cystic fibrosis (CF), generated an optimistic reaction with respect to the development of potential therapies. This extraordinary milestone, however, represented only the initial key step in a long path. Many of the mechanisms that govern the pathogenesis of CF, the most commonly inherited lethal pulmonary disorder in Caucasians, remain even today unknown. As a continuation to genomic research, proteomics now offers the unique advantage to examine global alterations in the protein expression patterns of CF cells and tissues. The systematic use of this approach will probably provide new insights into the cellular mechanisms involved in CF dysfunctions, and should ultimately result in the finding of new prognostic markers, and in the generation of new therapies. In this article we review the current status of proteomic research applied to the study of CF, including CFTR-related interactomics, and evaluate the potential of these technologies for future investigations.

Capturing protein-protein complexes at equilibrium: the holdup comparative chromatographic retention assay

The popular pulldown chromatographic assay detects complexes mediated by fusion proteins retained on affinity resin. The main limitation of this method is that it does not analyze complexes at equilibrium but after several washing steps. Consequently, fast-dissociating complexes may remain undetected. Here, we present the holdup assay, based on the principle of comparative chromatographic retention which eliminates the use of washing steps. The assay evaluates fractions of free and bound species at equilibrium. We used human papillomavirus oncoprotein E6, an E6-binding peptide and an E6-binding PDZ domain, to test several protocols utilizing pure proteins or expression extracts. The holdup assay is faster and more informative than the pulldown assay. It detects fast-dissociating complexes and it is also suited for evaluating equilibrium constants. It is potentially adaptable for automated determination of affinity constants and high-throughput analysis of interactions between proteins and other proteins, peptides, nucleic acids, or small regulatory molecules.