A new and unexpected domain-domain interaction in the AraC protein

An interaction between the dimerization domains and DNA binding domains of the dimeric AraC protein has previously been shown to facilitate repression of the Escherichia coli araBAD operon by AraC in the absence of arabinose. A new interaction between the domains of AraC in the presence of arabinose is reported here, the regulatory consequences of which are unknown. Evidence for the interaction is the following: the dissociation rate of arabinose-bound AraC from half-site DNA is considerably faster than that of free DNA binding domain, and the affinity of the dimerization domains for arabinose is increased when half-site DNA is bound. In addition, an increase in the fluorescence intensity of tryptophan residues located in the arabinose-bound dimerization domain is observed upon binding of half-site DNA to the DNA binding domains. Direct physical evidence of the new domain-domain interaction is demonstrated by chemical crosslinking and NMR experiments.     

Functional evaluation of domain-domain interactions and human protein interaction networks

MOTIVATION: Large amounts of protein and domain interaction data are being produced by experimental high-throughput techniques and computational approaches. To gain insight into the value of the provided data, we used our new similarity measure based on the Gene Ontology (GO) to evaluate the molecular functions and biological processes of interacting proteins or domains. The applied measure particularly addresses the frequent annotation of proteins or domains with multiple GO terms. RESULTS: Using our similarity measure, we compare predicted domain-domain and human protein-protein interactions with experimentally derived interactions. The results show that our similarity measure is of significant benefit in quality assessment and confidence ranking of domain and protein networks. We also derive useful confidence score thresholds for dividing domain interaction predictions into subsets of low and high confidence. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

ANAP: an integrated knowledge base for Arabidopsis protein interaction network analysis

Protein interactions are fundamental to the molecular processes occurring within an organism and can be utilized in network biology to help organize, simplify, and understand biological complexity. Currently, there are more than 10 publicly available Arabidopsis (Arabidopsis thaliana) protein interaction databases. However, there are limitations with these databases, including different types of interaction evidence, a lack of defined standards for protein identifiers, differing levels of information, and, critically, a lack of integration between them. In this paper, we present an interactive bioinformatics Web tool, ANAP (Arabidopsis Network Analysis Pipeline), which serves to effectively integrate the different data sets and maximize access to available data. ANAP has been developed for Arabidopsis protein interaction integration and network-based study to facilitate functional protein network analysis. ANAP integrates 11 Arabidopsis protein interaction databases, comprising 201,699 unique protein interaction pairs, 15,208 identifiers (including 11,931 The Arabidopsis Information Resource Arabidopsis Genome Initiative codes), 89 interaction detection methods, 73 species that interact with Arabidopsis, and 6,161 references. ANAP can be used as a knowledge base for constructing protein interaction networks based on user input and supports both direct and indirect interaction analysis. It has an intuitive graphical interface allowing easy network visualization and provides extensive detailed evidence for each interaction. In addition, ANAP displays the gene and protein annotation in the generated interactive network with links to The Arabidopsis Information Resource, the AtGenExpress Visualization Tool, the Arabidopsis 1,001 Genomes GBrowse, the Protein Knowledgebase, the Kyoto Encyclopedia of Genes and Genomes, and the Ensembl Genome Browser to significantly aid functional network analysis. The tool is available open access at http://gmdd.shgmo.org/Computational-Biology/ANAP.     

Decomposing protein networks into domain-domain interactions

The application of novel experimental techniques has generated large networks of protein-protein interactions. Frequently, important information on the structure and cellular function of protein-protein interactions can be gained from the domains of interacting proteins. We have designed a Cytoscape plugin that decomposes interacting proteins into their respective domains and computes a putative network of corresponding domain-domain interactions. To this end, the network graph of proteins has been extended by additional node and edge types for domain interactions, including different node and edge shapes and coloring schemes used for visualization. An additional plugin provides supplementary web links to Internet resources on domain function and structure. AVAILABILITY: Both Cytoscape plugins can be downloaded from http://www.cytoscape.org     

An integrated approach to the prediction of domain-domain interactions

Background  

The development of high-throughput technologies has produced several large scale protein interaction data sets for multiple species, and significant efforts have been made to analyze the data sets in order to understand protein activities. Considering that the basic units of protein interactions are domain interactions, it is crucial to understand protein interactions at the level of the domains. The availability of many diverse biological data sets provides an opportunity to discover the underlying domain interactions within protein interactions through an integration of these biological data sets.     

Protein domain annotation with predicted domain-domain interaction networks

This paper presents a framework for annotating protein domains with predicted domain-domain interaction networks. Specially, domain annotation is formalized as a multi-class classification problem in this work. The numerical experiments on InterPro domains show promising results, which proves the efficiency of our proposed methods.     

DDOMAIN: Dividing structures into domains using a normalized domain-domain interaction profile

Dividing protein structures into domains is proven useful for more accurate structural and functional characterization of proteins. Here, we develop a method, called DDOMAIN, that divides structure into DOMAINs using a normalized contact-based domain-domain interaction profile. Results of DDOMAIN are compared to AUTHORS annotations (domain definitions are given by the authors who solved protein structures), as well as to popular SCOP and CATH annotations by human experts and automatic programs. DDOMAIN's automatic annotations are most consistent with the AUTHORS annotations (90% agreement in number of domains and 88% agreement in both number of domains and at least 85% overlap in domain assignment of residues) if its three adjustable parameters are trained by the AUTHORS annotations. By comparison, the agreement is 83% (81% with at least 85% overlap criterion) between SCOP-trained DDOMAIN and SCOP annotations and 77% (73%) between CATH-trained DDOMAIN and CATH annotations. The agreement between DDOMAIN and AUTHORS annotations goes beyond single-domain proteins (97%, 82%, and 56% for single-, two-, and three-domain proteins, respectively). For an "easy" data set of proteins whose CATH and SCOP annotations agree with each other in number of domains, the agreement is 90% (89%) between "easy-set"-trained DDOMAIN and CATH/SCOP annotations. The consistency between SCOP-trained DDOMAIN and SCOP annotations is superior to two other recently developed, SCOP-trained, automatic methods PDP (protein domain parser), and DomainParser 2. We also tested a simple consensus method made of PDP, DomainParser 2, and DDOMAIN and a different version of DDOMAIN based on a more sophisticated statistical energy function. The DDOMAIN server and its executable are available in the services section on http://sparks.informatics.iupui.edu.     

Automated SPR-LC-MS/MS system for protein interaction analysis

We have developed a novel automated system to analyze protein complexes by integrating a surface plasmon resonance (SPR) biosensor with highly sensitive nanoflow liquid chromatography-tandem mass spectrometry (LC-MS/MS). A His-tagged protein, which is also tagged with FLAG and biotinylated sequences, was expressed in mammalian cells. After purification by using the His tag from the cell lysate, the sample protein mixture was applied to an SPR biosensor and the protein complex was captured on the sensor chip. The automated SPR-LC-MS/MS was then performed: (1) two-step on-chip purification of the protein complex by using the FLAG and the biotinylated tags, (2) on-chip protease digestion of the complex, and (3) online nanoflow LC-MS/MS analysis of the resulting peptide fragments for protein identification. All of these processes could be monitored in real-time by the SPR biosensor. We validated the performance of the system using either FK506-binding protein 52 kDa (FKBP52) or ribosomal protein S19 (rpS19) as bait. Thus, the fully automated SPR-LC-MS/MS system appeared to be a powerful tool for functional proteomics studies, particularly for snapshot analysis of functional cellular complexes and machines.     

Protein complex prediction based on maximum matching with domain-domain interaction

With the development of high-throughput methods for identifying protein-protein interactions, large scale interaction networks are available. Computational methods to analyze the networks to detect functional modules as protein complexes are becoming more important. However, most of the existing methods only make use of the protein-protein interaction networks without considering the structural limitations of proteins to bind together. In this paper, we design a new protein complex prediction method by extending the idea of using domain-domain interaction information. Here we formulate the problem into a maximum matching problem (which can be solved in polynomial time) instead of the binary integer linear programming approach (which can be NP-hard in the worst case). We also add a step to predict domain-domain interactions which first searches the database Pfam using the hidden Markov model and then predicts the domain-domain interactions based on the database DOMINE and InterDom which contain confirmed DDIs. By adding the domain-domain interaction prediction step, we have more edges in the DDI graph and the recall value is increased significantly (at least doubled) comparing with the method of Ozawa et al. (2010) [1] while the average precision value is slightly better. We also combine our method with three other existing methods, such as COACH, MCL and MCODE. Experiments show that the precision of the combined method is improved. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.     

A sol-gel-integrated protein array system for affinity analysis of aptamer-target protein interaction

A sol-gel microarray system was developed for a protein interaction assay with high activity. Comparing to 2-dimensional microarray surfaces, sol-gel can offer a more dynamic and broad range for proteins. In the present study, this sol-gel-integrated protein array was used in binding affinity analysis for aptamers. Six RNA aptamers and their target protein, yeast TBP (TATA-binding protein), were used to evaluate this method. A TBP-containing sol-gel mixture was spotted using a dispensing workstation under high-humidity conditions and each Cy-3-labeled aptamer was incubated. The dissociation constants (K(d)) were calculated by plotting the fluorescent intensity of the bound aptamers as a function of the TBP concentrations. The K(d) value of the control aptamer was found to be 8?nM, which agrees well with the values obtained using the conventional method, electric mobility shift assay. The sol-gel-based binding affinity measurements fit well with conventional binding affinity measurements, suggesting their possible use as an alternative to the conventional method. In addition, aptamer affinity measurements by the sol-gel-integrated protein chip make it possible to develop a simple high-throughput affinity method for screening high-affinity aptamers.     

PRASA: An integrated web server that analyzes protein interaction types

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

MPSA: integrated system for multiple protein sequence analysis with client/server capabilities

SUMMARY: MPSA is a stand-alone software intended to protein sequence analysis with a high integration level and Web clients/server capabilities. It provides many methods and tools, which are integrated into an interactive graphical user interface. It is available for most Unix/Linux and non-Unix systems. MPSA is able to connect to a Web server (e.g. http://pbil.ibcp.fr/NPSA) in order to perform large-scale sequence comparison on up-to-date databanks. AVAILABILITY: Free to academic http://www.ibcp.fr/mpsa/ CONTACT: c.blanchet@ibcp.fr     

Stability of dimer and domain-domain interaction of Arabidopsis phototropin 1 LOV2

Transient grating signals after photoexcitation of Arabidopsis phototropin 1 light-oxygen-voltage 2 (phot1LOV2) domain without the linker were found to be very sensitive to temperature. In particular, the diffusion signal drastically increased with rising temperature. The signal was consistently explained by the superposition of the photo-induced dissociation and association reactions. This observation indicated the presence of an equilibrium between the monomer and dimer forms of the phot1LOV2 domain in the dark. The equilibrium was confirmed by a gel chromatographic technique. The equilibrium constants at various temperatures were calculated from the fraction of the dimer, and the stabilization enthalpy and entropy were determined. Interestingly, the transient grating signal of phot1LOV2 with the linker (phot1LOV2-linker), which exists as the monomer form, was also temperature dependent; the diffusion signal intensity decreased with increasing temperature. Because the diffusion signal reflects a conformation change of the linker upon photoexcitation, this temperature dependence indicated that there were two forms of the phot1LOV2-linker. One form exhibited a conformational change upon photoexcitation whereas the other form showed no change. These two forms are not distinguishable spectroscopically. The fraction of these species depended on the temperature. Considering the monomer-dimer equilibrium of the phot1LOV2 domain, we suggest that the nonreactive form possesses the linker region that is dissociated from the LOV2 domain. Because the dissociation of the linker region from the LOV2 domain is a key step for the conformation change of the phot1LOV2-linker to induce biological activity, we proposed that the phototropins could have a role as a temperature sensor.     

Requirement of domain-domain interaction for conformational change and functional ATP hydrolysis in myosin

Coordination between the nucleotide-binding site and the converter domain of myosin is essential for its ATP-dependent motor activities. To unveil the communication pathway between these two sites, we investigated contact between side chains of Phe-482 in the relay helix and Gly-680 in the SH1-SH2 helix. F482A myosin, in which Phe-482 was changed to alanine with a smaller side chain, was not functional in vivo. In vitro, F482A myosin did not move actin filaments and the Mg2+-ATPase activity of F482A myosin was hardly activated by actin. Phosphate burst and tryptophan fluorescence analyses, as well as fluorescence resonance energy transfer measurements to estimate the movements of the lever arm domain, indicated that the transition from the open state to the closed state, which precedes ATP hydrolysis, is very slow. In contrast, F482A/G680F doubly mutated myosin was functional in vivo and in vitro. The fact that a larger side chain at the 680th position suppresses the defects of F482A myosin suggests that the defects are caused by insufficient contact between side chains of Ala-482 and Gly-680. Thus, the contact between these two side chains appears to play an important role in the coordinated conformational changes and subsequent ATP hydrolysis.     

IWS: integrated web server for protein sequence and structure analysis

Rapid increase in protein sequence information from genome sequencing projects demand the intervention of bioinformatics tools to recognize interesting gene-products and associated function. Often, multiple algorithms need to be employed to improve accuracy in predictions and several structure prediction algorithms are on the public domain. Here, we report the availability of an Integrated Web-server as a bioinformatics online package dedicated for in-silico analysis of protein sequence and structure data (IWS). IWS provides web interface to both in-house and widely accepted programs from major bioinformatics groups, organized as 10 different modules. IWS also provides interactive images for Analysis Work Flow, which will provide transparency to the user to carry out analysis by moving across modules seamlessly and to perform their predictions in a rapid manner. AVAILABILITY: IWS IS AVAILABLE FROM THE URL: http://caps.ncbs.res.in/iws.     

Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

Background  

Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles.     

An integrated system for genetic analysis

Background  

Large-scale genetic mapping projects require data management systems that can handle complex phenotypes and detect and correct high-throughput genotyping errors, yet are easy to use.