Search

Wolbachia are gram-negative endosymbiotic bacteria residing in the nematode host Brugia malayi that causes lymphatic filariasis in humans. Wolbachia settle in the host environment for their survival, growth and reproduction by maintaining an obligate mutualism relationship. However, the mechanism used by Wolbachia to maintain its mutualistic relationship with the nematode is poorly understood. Therefore, to elucidate the host-pathogen interaction (HPI) mechanism of Wolbachia and Brugia malayi, we used interolog-based approach for identification of host-pathogen protein-protein interactions (HP-PPIs) network and domain-based approach for the validation. The inter-species HP-PPIs network contained 392 proteins (258 Brugia malayi and 134 Wolbachia proteins) connected by 829 edges. Further, based on interolog approach, we identified 24 pathogen (Wolbachia) and 33 host (Brugia malayi) proteins involved in HPI. This study also reported 8 hub genes namely bma-atp-1, Bm1_25145, A0A0K0JKQ9, fusA, bma-eef-2, rplB, Bma-rpl-2, and bma-eftu-2 involved in maintaining an endosymbiotic relationship between Wolbachia and Brugia malayi. Furthermore, we also reported six-pair of host-pathogen interactions containing numerous pairs of shared domains that might be crucial for the establishment of mutualistic relationship between Wolbachia and Brugia malayi. The functional analysis revealed most of the Brugia malayi proteins are involved in the generation of precursor metabolites showing catalytic activity in the intracellular anatomical structures and organelles. The subcellular localization reported host and pathogen proteins were located in mitochondria and cytoplasm respectively. The Brugia malayi pathways involved in HPI were metabolic pathway, oxidative phosphorylation pathway, and spliceosome pathways, indicates that for maintaining mutualism energy-yielding pathways are targeted. Overall, this work provides new insights into the mechanism of HPIs between Wolbachia and Brugia malayi, and will help researchers a deeper understanding of the intracellular pathogenic activity and endosymbiotic relationship of Wolbachia with its host Brugia malayi.

Although a large number of databases are available for regulatory elements, a bottleneck has been created by the lack of bioinformatics tools to predict the interaction modes of regulatory elements. To reduce this gap, we developed the Arabidopsis Transcription Regulatory Factor Domain/Domain Interaction Analysis Tool–liquid/liquid phase separation (LLPS), oligomerization, GO analysis (ART FOUNDATION-LOG), a useful toolkit for protein–nucleic acid interaction (PNI) and protein–protein interaction (PPI) analysis based on domain–domain interactions (DDIs). LLPS, protein oligomerization, the structural properties of protein domains, and protein modifications are major components in the orchestration of the spatiotemporal dynamics of PPIs and PNIs. Our goal is to integrate PPI/PNI information into the development of a prediction model for identifying important genetic variants in peaches. Our program unified interdatabase relational keys based on protein domains to facilitate inference from the model species. A key advantage of this program lies in the integrated information of related features, such as protein oligomerization, LOG analysis, structural characterizations of domains (e.g., domain linkers, intrinsically disordered regions, DDIs, domain–motif (peptide) interactions, beta sheets, and transmembrane helices), and post-translational modification. We provided simple tests to demonstrate how to use this program, which can be applied to other eukaryotic organisms. [ABSTRACT FROM AUTHOR]

Protein tyrosine kinases (PTKs) are a large enzyme family that regulates many cellular processes. The key to their broad role in signaling is their tunable substrate specificity and regulatory mechanisms that allow each to respond to appropriate regulatory signals and phosphorylate the correct physiological protein substrates. Thus, in addition to the general PTK catalytic platform, each PTK acquires unique structural motifs that confer a unique combination of catalytic and regulatory properties. Understanding the structural basis for these properties is essential for understanding and manipulating the PTK-based signaling networks in normal and cancer cells. C-terminal Src kinase (Csk) and its homolog, Csk-homologous kinase (Chk), phosphorylate Src family kinases on a C-terminal Tyr residue and negatively regulate their kinase activity. While this regulatory function is biologically essential, Csk and Chk have also been excellent model PTKs for dissecting the structural basis of PTK catalysis and regulation. In this article, we review the structure-function studies of Csk and Chk that shed light on the regulatory and catalytic mechanisms of protein tyrosine kinases in general.

As one of the most studied Apicomplexan parasite Cryptosporidium, Cryptosporidium parvum (C. parvum) causes worldwide serious diarrhea disease cryptosporidiosis, which can be deadly to immunodeficiency individuals, newly born children, and animals. Proteome-wide identification of protein–protein interactions (PPIs) has proven valuable in the systematic understanding of the genome-phenome relationship. However, the PPIs of C. parvum are largely unknown because of the limited experimental studies carried out. Therefore, we took full advantage of three bioinformatics methods, i.e., interolog mapping (IM), domain-domain interaction (DDI)-based inference, and machine learning (ML) method, to jointly predict PPIs of C. parvum. Due to the lack of experimental PPIs of C. parvum, we used the PPI data of Plasmodium falciparum (P. falciparum), which owned the largest number of PPIs in Apicomplexa, to train an ML model to infer C. parvum PPIs. We utilized consistent results of these three methods as the predicted high-confidence PPI network, which contains 4,578 PPIs covering 554 proteins. To further explore the biological significance of the constructed PPI network, we also conducted essential network and protein functional analysis, mainly focusing on hub proteins and functional modules. We anticipate the constructed PPI network can become an important data resource to accelerate the functional genomics studies of C. parvum as well as offer new hints to the target discovery in developing drugs/vaccines.

There exist several databases that provide virus-host protein interactions. While most provide curated records of interacting virus-host protein pairs, information on the strain-specific virulence factors or protein domains involved, is lacking. Some databases offer incomplete coverage of influenza strains because of the need to sift through vast amounts of literature (including those of major viruses including HIV and Dengue, besides others). None have offered complete, strain specific protein-protein interaction records for the influenza A group of viruses. In this paper, we present a comprehensive network of predicted domain-domain interaction(s) (DDI) between influenza A virus (IAV) and mouse host proteins, that will allow the systematic study of disease factors by taking the virulence information (lethal dose) into account. From a previously published dataset of lethal dose studies of IAV infection in mice, we constructed an interacting domain network of mouse and viral protein domains as nodes with weighted edges. The edges were scored with the Domain Interaction Statistical Potential (DISPOT) to indicate putative DDI. The virulence network can be easily navigated via a web browser, with the associated virulence information (LD50 values) prominently displayed. The network will aid influenza A disease modeling by providing strain-specific virulence levels with interacting protein domains. It can possibly contribute to computational methods for uncovering influenza infection mechanisms mediated through protein domain interactions between viral and host proteins. It is available at https://iav-ppi.onrender.com/home.

Abstract Background Protein-protein interactions (PPIs) play very important roles in diverse biological processes. Experimentally validated or predicted PPI data have become increasingly available in diverse plant species. To further explore the biological functions of PPIs, understanding the interaction details of plant PPIs (e.g., the 3D structural contexts of interaction sites) is necessary. By integrating bioinformatics algorithms, interaction details can be annotated at different levels and then compiled into user-friendly databases. In our previous study, we developed AraPPISite, which aimed to provide interaction site information for PPIs in the model plant Arabidopsis thaliana. Considering that the application of AraPPISite is limited to one species, it is very natural that AraPPISite should be evolved into a new database that can provide interaction details of PPIs in multiple plants. Description PlaPPISite (http://zzdlab.com/plappisite/index.php) is a comprehensive, high-coverage and interaction details-oriented database for 13 plant interactomes. In addition to collecting 121 experimentally verified structures of protein complexes, the complex structures of experimental/predicted PPIs in the 13 plants were also constructed, and the corresponding interaction sites were annotated. For the PPIs whose 3D structures could not be modelled, the associated domain-domain interactions (DDIs) and domain-motif interactions (DMIs) were inferred. To facilitate the reliability assessment of predicted PPIs, the source species of interolog templates, GO annotations, subcellular localizations and gene expression similarities are also provided. JavaScript packages were employed to visualize structures of protein complexes, protein interaction sites and protein interaction networks. We also developed an online tool for homology modelling and protein interaction site annotation of protein complexes. All data contained in PlaPPISite are also freely available on the Download page. Conclusion PlaPPISite provides the plant research community with an easy-to-use and comprehensive data resource for the search and analysis of protein interaction details from the 13 important plant species.

Abstract Background The accurate identification of protein complexes is important for the understanding of cellular organization. Up to now, computational methods for protein complex detection are mostly focus on mining clusters from protein-protein interaction (PPI) networks. However, PPI data collected by high-throughput experimental techniques are known to be quite noisy. It is hard to achieve reliable prediction results by simply applying computational methods on PPI data. Behind protein interactions, there are protein domains that interact with each other. Therefore, based on domain-protein associations, the joint analysis of PPIs and domain-domain interactions (DDI) has the potential to obtain better performance in protein complex detection. As traditional computational methods are designed to detect protein complexes from a single PPI network, it is necessary to design a new algorithm that could effectively utilize the information inherent in multiple heterogeneous networks. Results In this paper, we introduce a novel multi-network clustering algorithm to detect protein complexes from multiple heterogeneous networks. Unlike existing protein complex identification algorithms that focus on the analysis of a single PPI network, our model can jointly exploit the information inherent in PPI and DDI data to achieve more reliable prediction results. Extensive experiment results on real-world data sets demonstrate that our method can predict protein complexes more accurately than other state-of-the-art protein complex identification algorithms. Conclusions In this work, we demonstrate that the joint analysis of PPI network and DDI network can help to improve the accuracy of protein complex detection.

Background: Protein-protein interactions (PPIs) play very important roles in diverse biological processes. Experimentally validated or predicted PPI data have become increasingly available in diverse plant species. To further explore the biological functions of PPIs, understanding the interaction details of plant PPIs (e.g., the 3D structural contexts of interaction sites) is necessary. By integrating bioinformatics algorithms, interaction details can be annotated at different levels and then compiled into user-friendly databases. In our previous study, we developed AraPPISite, which aimed to provide interaction site information for PPIs in the model plant Arabidopsis thaliana. Considering that the application of AraPPISite is limited to one species, it is very natural that AraPPISite should be evolved into a new database that can provide interaction details of PPIs in multiple plants. Description: PlaPPISite (http://zzdlab.com/plappisite/index.php) is a comprehensive, high-coverage and interaction details-oriented database for 13 plant interactomes. In addition to collecting 121 experimentally verified structures of protein complexes, the complex structures of experimental/predicted PPIs in the 13 plants were also constructed, and the corresponding interaction sites were annotated. For the PPIs whose 3D structures could not be modelled, the associated domain-domain interactions (DDIs) and domain-motif interactions (DMIs) were inferred. To facilitate the reliability assessment of predicted PPIs, the source species of interolog templates, GO annotations, subcellular localizations and gene expression similarities are also provided. JavaScript packages were employed to visualize structures of protein complexes, protein interaction sites and protein interaction networks. We also developed an online tool for homology modelling and protein interaction site annotation of protein complexes. All data contained in PlaPPISite are also freely available on the Download page. Conclusion: PlaPPISite provides the plant research community with an easy-to-use and comprehensive data resource for the search and analysis of protein interaction details from the 13 important plant species. [ABSTRACT FROM AUTHOR]

Introduction. Infection caused by Mycobacterium tuberculosis (M. tb ) is still a leading cause of mortality worldwide with estimated 1.4 million deaths annually. Hypothesis/Gap statement. Despite macrophages' ability to kill bacterium, M. tb can grow inside these innate immune cells and the exploration of the infection has traditionally been characterized by a one-sided relationship, concentrating solely on the host or examining the pathogen in isolation. Aim. Because of only a handful of M. tb -host interactions have been experimentally characterized, our main goal is to predict protein-protein interactions during the early phases of the infection. Methodology. In this work, we performed an integrative computational approach that exploits differentially expressed genes obtained from Dual RNA-seq analysis combined with known domain-domain interactions. Results. A total of 2381 and 7214 genes were identified as differentially expressed in M. tb and in THP-1-like macrophages, respectively, revealing different transcriptional profiles in response to infection. Over 48 h of infection, the host-pathogen network revealed 25 016 PPIs. Analysis of the resulting predicted network based on cellular localization information of M. tb proteins, indicated the implication of interacting nodes including the bacterial PE/PPE/PE_PGRS family. In addition, M. tb proteins interacted with host proteins involved in NF-kB signalling pathway as well as interfering with the host apoptosis ability via the potential interaction of M. tb TB16.3 with human TAB1 and M. tb GroEL2 with host protein kinase C delta, respectively. Conclusion. The prediction of the full range of interactions between M. tb and host will contribute to better understanding of the pathogenesis of this bacterium and may provide advanced approaches to explore new therapeutic targets against tuberculosis.

Relative to the apolipoprotein E (apoE) E3 allele of the APOE gene, apoE4 strongly increases the risk for the development of late-onset Alzheimer's disease. However, apoE4 differs from apoE3 by only a single amino acid at position 112, which is arginine in apoE4 and cysteine in apoE3. It remains unclear why apoE3 and apoE4 are functionally different. Described here is a proposal for understanding the functional differences between these two isoforms with respect to lipid binding. A mechanism is proposed that is based on the full-length monomeric structure of the protein, on hydrogen-deuterium exchange mass spectrometry data, and on the role of intrinsically disordered regions to control protein motions. It is proposed that lipid binds between the N-terminal and C-terminal domains and that separation of the two domains, along with the presence of intrinsically disordered regions, controls this process. The mechanism explains why apoE3 differs from apoE4 with respect to different lipid-binding specificities, why lipid increases the binding of apoE to its receptor, and why specific residues are conserved. [ABSTRACT FROM AUTHOR]

Functional domains are semi-autonomous parts of proteins. The Per-Arnt-Sim (PAS) domain functions as signal-sensor in two-component systems of several bacteria. This domain exhibits large sequence diversity and is linked to other co-domains to modulate their function. In the present study, we analyzed the PAS domains found in the proteomes of several actinobacteria representing a variety of niches. PAS-domain containing proteins were identified with the HMMER program and characterized via an in silico approach. In general, the PAS proteins were found to be in the COG T (signal transduction) category implying their role was indeed in signal transduction. Most of the PAS proteins were found to be structurally conserved and moderately expressed. However, they showed a strong bias towards the lagging strand which may be a result of their involvement in adaptive evolution. A structure based phylogenetic analysis showed that PAS domains with similar interacting co-domains grouped together in a cluster irrespective of the bacterial genus from which they were identified. Thus, we may say that the association of PAS with its co-domains is based upon the PAS domain structure and not on the sequence. [ABSTRACT FROM AUTHOR]

Identifying protein complexes within Protein-Protein Interaction Networks (PPINs) is an important task in computational biology for analyzing biological processes and pathways. A protein complex can be represented as a group of two or more interacting proteins that formed a dense sub-graph in PPIN. In PPIN, it also corresponds to a cluster of proteins which not only includes interactions that are stable over time, but it also includes dynamic interactions. Two interactions are dynamic if they are mutually exclusive. Conventional protein complex detection approach used graph theoretic clustering methods on static PPI data and disregard the dynamic interaction. In this paper, we have proposed a multiobjective framework that considers interaction dynamics for detecting protein complexes. The framework is motivated by an widely used protein complex detection method called PROCOMOSS 1 that is used in static PPI data. The performance of the proposed framework is compared with other state-of-the-art techniques for both static and dynamic PPI data separately. The results show a significant improvement in the matching statistics over static PPIN. We have also performed a gene ontology and pathway based analysis of the resulting protein complexes. [ABSTRACT FROM AUTHOR]

Knowledge about protein interaction sites provides detailed information of protein-protein interactions (PPIs). To date, nearly 20,000 of PPIs from Arabidopsis thaliana have been identified. Nevertheless, the interaction site information has been largely missed by previously published PPI databases. Here, AraPPISite, a database that presents fine-grained interaction details for A. thaliana PPIs is established. First, the experimentally determined 3D structures of 27 A. thaliana PPIs are collected from the Protein Data Bank database and the predicted 3D structures of 3023 A. thaliana PPIs are modeled by using two well-established template-based docking methods. For each experimental/predicted complex structure, AraPPISite not only provides an interactive user interface for browsing interaction sites, but also lists detailed evolutionary and physicochemical properties of these sites. Second, AraPPISite assigns domain-domain interactions or domain-motif interactions to 4286 PPIs whose 3D structures cannot be modeled. In this case, users can easily query protein interaction regions at the sequence level. AraPPISite is a free and user-friendly database, which does not require user registration or any configuration on local machines. We anticipate AraPPISite can serve as a helpful database resource for the users with less experience in structural biology or protein bioinformatics to probe the details of PPIs, and thus accelerate the studies of plant genetics and functional genomics. AraPPISite is available at . [ABSTRACT FROM AUTHOR]

Protein self-interaction, i.e. the interaction between two or more identical proteins expressed by one gene, plays an important role in the regulation of cellular functions. Considering the limitations of experimental self-interaction identification, it is necessary to design specific bioinformatics tools for self-interacting protein (SIP) prediction from protein sequence information. In this study, we proposed an improved computational approach for SIP prediction, termed SPAR (Self-interacting Protein Analysis serveR). Firstly, we developed an improved encoding scheme named critical residues substitution (CRS), in which the fine-grained domain-domain interaction information was taken into account. Then, by employing the Random Forest algorithm, the performance of CRS was evaluated and compared with several other encoding schemes commonly used for sequence-based protein-protein interaction prediction. Through the tenfold cross-validation tests on a balanced training dataset, CRS performed the best, with the average accuracy up to 72.01 %. We further integrated CRS with other encoding schemes and identified the most important features using the mRMR (the minimum redundancy maximum relevance) feature selection method. Our SPAR model with selected features achieved an average accuracy of 92.09 % on the human-independent test set (the ratio of positives to negatives was about 1:11). Besides, we also evaluated the performance of SPAR on an independent yeast test set (the ratio of positives to negatives was about 1:8) and obtained an average accuracy of 76.96 %. The results demonstrate that SPAR is capable of achieving a reasonable performance in cross-species application. The SPAR server is freely available for academic use at . [ABSTRACT FROM AUTHOR]

Protein–protein interactions (PPIs) are vital to a number of biological processes. With computational methods, plenty of domain information can help us to predict and assess PPIs. In this study, we proposed a domain-based approach for the prediction of human PPIs based on the interactions between the proteins and the domains. In this method, an optimizing model was built with the information from InterDom, 3did, DOMINE and Pfam databases. With this model, for 147 proteins in the integrin adhesome PPI network, 736 probable PPIs have been predicted, and the corresponding confidence probabilities of these PPIs were also calculated. It provides an opportunity to visualize the PPIs by using network graphs, which were constructed with Cytoscape, so that we can indicate underlying pathways possible. [ABSTRACT FROM AUTHOR]

Understanding how different genomic mutational landscapes in patients with cancer lead to different responses to anticancer drugs is an important challenge for realizing precision medicine for cancer. Many studies have analyzed the comprehensive anticancer drug-response profiles and genomic profiles of cancer cell lines to identify the relationship between the anticancer drug response and genomic alternations. However, few studies have focused on interpreting these profiles with a network perspective. In this work, we analyzed genomic alterations in cancer cell lines by considering which interactions in the signaling pathway were perturbed by mutations. With our interaction-centric approach, we identified novel interaction/drug response associations for two drugs (afatinib and ixabepilone) for which no gene-centric association could be found. When we compared the performance of classifiers for predicting the responses to 164 drugs, the classifiers trained with interaction-centric features outperformed the classifiers trained with gene-centric features, despite the smaller number of features ( p -value = 2.0 × 10 −3 ). By incorporating the interaction information from signaling pathways, we revealed associations between genomic alterations and drug responses that could be missed when using a gene-centric approach. [ABSTRACT FROM AUTHOR]

Phage-encoded cell wall peptidoglycan hydrolyzing enzymes, called endolysins, are essential for efficient release of virions from bacteria, and show species-specific killing of the host. We have demonstrated previously that the interaction between N-terminal catalytic and C-terminal cell wall binding domains of mycobacteriophage D29 endolysin makes the enzyme inactive in Escherichia coli . Here, we demonstrate that such interaction occurs intramolecularly and is facilitated by a charged linker that connects the two domains. We also show that linker composition is crucial for the inactivation of PG hydrolase in E. coli . Such knowledge will immensely help in bioengineering of endolysins with narrow or broad spectrum antimicrobial activity. [ABSTRACT FROM AUTHOR]

Following successful completion of the Brassica rapa sequencing project, the next step is to investigate functions of individual genes/proteins. For Arabidopsis thaliana, large amounts of protein-protein interaction (PPI) data are available from the major PPI databases. It is known that Brassica crop species are closely related to A. thaliana. This provides an opportunity to infer the B. rapa interactome using PPI data available from A. thaliana. In this paper, we present an inferred B. rapa interactome that is based on the A. thaliana PPI data from two resources: (i) A. thaliana PPI data from three major databases, BioGRID, IntAct and TAIR. (ii) ortholog-based A. thaliana PPI predictions. Linking between B. rapa and A. thaliana was accomplished in three complementary ways: (i) ortholog predictions, (ii) identification of gene duplication based on synteny and collinearity, and (iii) BLAST sequence similarity search. A complementary approach was also applied, which used known/predicted domain-domain interaction data. Specifically, since the two species are closely related, we used PPI data from A. thaliana to predict interacting domains that might be conserved between the two species. The predicted interactome was investigated for the component that contains known A. thaliana meiotic proteins to demonstrate its usability.

ABSTRACT Ribosome recycling factor (RRF) is essential for bacterial growth. Structural studies revealed that RRF consists of two domains connected by two short polypeptides at the hinge region. Here, we evaluated the intrinsic stabilities (Δ G*s) of the two domains and the free energy of the domain-domain interactions (Δ Gint) for mesophilic RRF (RRF from Escherichia coli, EcRRF) and thermophilic RRF (RRF from Thermus thermophilus, TtRRF) by using differential scanning calorimetry and circular dichroic spectroscopy. Despite single endothermic peaks, a higher than unity value for the ratio of calorimetric enthalpy to van't Hoff enthalpy of the unfolding indicated the presence of unfolding intermediates for both RRFs. Deconvolution analysis based on statistical thermodynamics employing multiple states of the unfolding process with domain-domain interactions allowed us to determine Δ G*s of each domain and Δ Gint. The results indicated that domain I has a higher unfolding temperature than domain II and that it stabilizes domain II through Δ Gint, giving rise to an apparent single peak of unfolding. Interestingly, the estimated Δ Gint values of 6.28 kJ/mol for EcRRF and 26.28 kJ/mol for TtRRF reflect the observation that only EcRRF has recycling activity at ambient temperature. Our present study suggests the importance of a moderate Δ Gint for biological activity of multidomain proteins. © 2013 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 100: 366-379, 2013. [ABSTRACT FROM AUTHOR]

Mammalian bicistronic mRNA is a recently discovered mammalian gene structure. Several reported cases of mammalian bicistronic mRNA indicated that genes of this structure play roles in some important biological processes. However, a genome-wide computational identification of bicistronic mRNA in mammalian genome, such as human genome, is still lacking. Here we used a comparative genomics approach to identify the frequency of human bicistronic mRNA. We then validated the result by using a new support vector machine (SVM) model. We identified 43 human bicistronic mRNAs in 30 distinct genes. Our literature analysis shows that our method recovered 100 % (6/6) of the previously known bicistronic mRNAs which had been experimentally confirmed by other groups. Our graph theory-based analysis and GO analysis indicated that human bicistronic mRNAs are prone to produce different yet closely functionally related proteins. In addition, we also described and analyzed three different mechanisms of ORF fusion. Our method of identifying bicistronic mRNAs in human genome provides a model for the computational identification of characteristic gene structures in mammalian genomes. We anticipate that our data will facilitate further molecular characterization and functional study of human bicistronic mRNA. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Enzyme I initiates a series of phosphotransfer reactions during sugar uptake in the bacterial phosphotransferase system. Here, we have isolated a stable recombinant C-terminal domain of Enzyme I (EIC) of Escherichia coli and characterized its interaction with the N-terminal domain of Enzyme I (EIN) and also with various ligands. EIC can phosphorylate EIN, but their binding is transient regardless of the presence of phosphoenolpyruvate (PEP). Circular dichroism and NMR indicate that ligand binding to EIC induces changes near aromatic groups but not in the secondary structure of EIC. Binding of PEP to EIC is an endothermic reaction with the equilibrium dissociation constant ( KD) of 0.28 m M, whereas binding of the inhibitor oxalate is an exothermic reaction with KD of 0.66 m M from calorimetry. The binding thermodynamics of EIC and PEP compared to that of Enzyme I (EI) and PEP reveals that domain-domain motion in EI can contribute as large as ∼−3.2 kcal/mol toward PEP binding. [ABSTRACT FROM AUTHOR]

Abstract: During the viral infection and replication processes, viral proteins are highly regulated and may interact with host proteins. However, the functions and interaction partners of many viral proteins have yet to be explored. Here, we compiled a VIral Protein domain DataBase (VIP DB) to associate viral proteins with putative functions and interaction partners. We systematically assign domains and infer the functions of proteins and their protein interaction partners from their domain annotations. A total of 2,322 unique domains that were identified from 2,404 viruses are used as a starting point to correlate GO classification, KEGG metabolic pathway annotation and domain–domain interactions. Of the unique domains, 42.7% have GO records, 39.6% have at least one domain–domain interaction record and 26.3% can also be found in either mammals or plants. This database provides a resource to help virologists identify potential roles for viral protein. All of the information is available at http://vipdb.cgu.edu.tw. [Copyright &y& Elsevier]

Abstract: The two protein tyrosine phosphatase (PTP) domains in bi-domain PTPs share high sequence and structural similarity. However, only one of the two PTP domains is catalytically active. Here we describe biochemical studies on the two tandem PTP domains of the bi-domain PTP, PTP99A. Phosphatase activity, monitored using small molecule as well as peptide substrates, revealed that the inactive (D2) domain activates the catalytic (D1) domain. Thermodynamic measurements suggest that the inactive D2 domain stabilizes the bi-domain (D1–D2) protein. The mechanism by which the D2 domain activates and stabilizes the bi-domain protein is governed by few interactions at the inter-domain interface. In particular, mutating Lys990 at the interface attenuates inter-domain communication. This residue is located at a structurally equivalent location to the so-called allosteric site of the canonical single domain PTP, PTP1B. These observations suggest functional optimization in bi-domain PTPs whereby the inactive PTP domain modulates the catalytic activity of the bi-domain enzyme. [Copyright &y& Elsevier]

Developing In Silico Computational Techniques to predict Protein Protein Interactions (PPIs) is one of the challenging research area for computational biologists. Experimental techniques for interaction prediction, lack accuracy and are error prone. Observing the limitations and low accuracy of existing methods, this work focuses on improvement in computational efficiency with increased performance for domain base prediction of PPIs. In the present paper, a novel approach of Domain Frequency Count (DFC) method with association score base classification, using feed forward back-propagation neural network, has been proposed for prediction of interacting protein pairs based on their domain characteristic features data. Results obtained are quite encouraging. When compared with the existing MLE, DT, CL NN, and RDF techniques [8][10] on similar datasets, it is observed that accuracy and sensitivity are increased by 8.91% and 5.70%, respectively. [ABSTRACT FROM AUTHOR]

The amyloid precursor protein (APP) is the key player in Alzheimer's disease pathology, yet APP and its analogues are also essential for neuronal development and cell homeostasis in mammals. We have determined the crystal structure of the entire N-terminal APP-El domain consisting of the growth factor like and the copper binding domains at 2.7-Å resolution and show that El functions as a rigid functional entity. The two subdomains interact tightly in a pH-dependent manner via an evolutionarily conserved interface area. Two El entities dimerize upon their interaction with heparin. requiring 8-12 sugar rings to form the heparin-bridged APP-E1 dimer in an endothermic and pH-dependent process that is characterized by a low micromolar dissociation constant. Limited proteolysis confirms that the heparin-bridged El dimers obtained in solution correspond to a dimer contaët in our crystal, enabling us to model this heparin-[APP-E1]2 complex. Correspondingly, the APP-based signal transduction, cell-celland/or ceIl-ECM interaction should depend on dimerization induced by heparin, as well as on pH, arguing that APP could fulfill different functions depending on its (sub)cellular localization. [ABSTRACT FROM AUTHOR]

The interactions of recombinant human eIF4A (4A) and its N- and C-terminal side domains (AN and AC, respectively) with the middle- and C-terminal-domain-linked fragment (GMC) of eIF4G and its middle and C-terminal domains (GM and GC, respectively) were investigated by surface plasmon resonance (SPR) analysis and isothermal titration calorimetry (ITC). It is remarkable that the kinetic parameter-dependent SPR profile observed for the 4A–GMC pair was quite different from the steady affinity profiles of the 4A–GM/GC pairs, suggesting the simultaneous contribution of the middle and C-terminal domains of eIF4G for the binding with eIF4A. On the other hand, ITC yielded the enthalpy energies of −1.5 × 104 to −2.5 × 104 J/mol for the domain–domain interactions of 4A with GMC. Although the ITC profile of the 4A–GM pair reflects well the structural feature shown previously by NMR and X-ray analyses, it was essentially different from that of the 4A-GMC pair. The present results suggest that the intimate interaction between the eIF4A N- and C-terminal domains and the eIF4G middle and C-terminal domains is necessary to reveal the biologically active function of the eIF4A–eIF4G complex. [ABSTRACT FROM PUBLISHER]

Cellular processes are highly interconnected and many proteins are shared in different pathways. Some of these shared proteins or protein families may interact with diverse partners using the same interface regions; such multibinding proteins are the subject of our study. The main goal of our study is to attempt to decipher the mechanisms of specific molecular recognition of multiple diverse partners by promiscuous protein regions. To address this, we attempt to analyze the physicochemical properties of multibinding interfaces and highlight the major mechanisms of functional switches realized through multibinding. We find that only 5% of protein families in the structure database have multibinding interfaces, and multibinding interfaces do not show any higher sequence conservation compared with the background interface sites. We highlight several important functional mechanisms utilized by multibinding families. (a) Overlap between different functional pathways can be prevented by the switches involving nearby residues of the same interfacial region. (b) Interfaces can be reused in pathways where the substrate should be passed from one protein to another sequentially. (c) The same protein family can develop different specificities toward different binding partners reusing the same interface; and finally, (d) inhibitors can attach to substrate binding sites as substrate mimicry and thereby prevent substrate binding. [ABSTRACT FROM AUTHOR]

Abstract: Nucleotide Binding Domains (NBDs) are responsible for the ATPase activity of the multidrug resistance protein 1 (MRP1). A series of NBD1-linker-NBD2 chimeric fusion proteins were constructed, expressed and purified, and their ATPase activities were analyzed. We report here that a GST linked NBD1642–890-GST-NBD21286–1531 was able to hydrolyze ATP at a rate of about 4.6nmol/mg/min (Km =2.17mM, Vmax =12.36nmol/mg/min), which was comparable to the purified and reconstituted MRP1. In contrast, neither a mixture of NBD1 and GST-NBD2 nor the NBD1-GST-NBD1 fusion protein showed detectable ATPase activity. Additionally, the E1455Q mutant was found to be nonfunctional. Measurements by both MIANS labeling and circular dichroism spectroscopy revealed significant conformational differences in the NBD1-GST-NBD2 chimeric fusion protein compared to the mixture of NBD1 and GST-NBD2. The results suggest a direct interaction mediated by GST between the two NBDs of MRP1 leading to conformational changes which would enhance its ATPase activity. [Copyright &y& Elsevier]

Summary: Csk and Src protein tyrosine kinases are structurally homologous but use opposite regulatory strategies. The isolated catalytic domain of Csk is intrinsically inactive and is activated by interactions with the regulatory Src homology 3 (SH3) and SH2 domains, while the isolated catalytic domain of Src is intrinsically active and is suppressed by interactions with the regulatory SH3 and SH2 domains. The structural basis for why one isolated catalytic domain is intrinsically active while the other is inactive is not clear. In this study, we identified structural elements in the N-terminal lobe of the catalytic domain that render the Src catalytic domain active. These structural elements include the α-helix C region, a β turn between the β4 and β5 strands, and an Arg residue at the beginning of the catalytic domain. These three motifs interact with one another to activate the Src catalytic domain, but the equivalent motifs in Csk directly interact with the regulatory domains that are important for Csk activation. The Src motifs can be grafted to the Csk catalytic domain to obtain an active Csk catalytic domain. These results, together with available Src and Csk tertiary structures, reveal an important structural switch that determines the kinase activity of a catalytic domain and dictates the regulatory strategy of a kinase. [Copyright &y& Elsevier]

Protein–protein interactions (PPIs) are intrinsic to almost all cellular processes. Different computational methods offer new chances to study PPIs. To predict PPIs, while the integrative methods use multiple data sources instead of a single source, the domain-based methods often use only protein domain features. Integration of both protein domain features and genomic/proteomic features from multiple databases can more effectively predict PPIs. Moreover, it allows discovering the reciprocal relationships between PPIs and biological features of their interacting partners. We developed a novel integrative domain-based method for predicting PPIs using inductive logic programming (ILP). Two principal domain features used were domain fusions and domain–domain interactions (DDIs). Various relevant features of proteins were exploited from five popular genomic and proteomic databases. By integrating these features, we constructed biologically significant ILP background knowledge of more than 278,000 ground facts. The experimental results through multiple 10-fold cross-validations demonstrated that our method predicts PPIs better than other computational methods in terms of typical performance measures. The proposed ILP framework can be applied to predict DDIs with high sensitivity and specificity. The induced ILP rules gave us many interesting, biologically reciprocal relationships among PPIs, protein domains, and PPI-related genomic/proteomic features. Supplementary material is available at . [ABSTRACT FROM AUTHOR]

Abstract: The substrate-induced conformational change of the truncated C-terminal catalytic domain (CAT) of Geobacillus stearothermophilus lysyl-tRNA synthetase was examined by measuring tryptophan fluorescence of the truncated CAT domain in the presence or absence of the truncated N-terminal tRNA anticodon-binding domain (TAB). The fluorescence spectrum of CAT was not changed by the addition of l-lysine or ATP, whereas the intensity increased by adding a lysyl-adenylate analogue, suggesting that the CAT fluorescence increases when lysyl-adenylate is formed in the active site of CAT in l-lysine activation. In the presence of TAB, the addition of l-lysine to CAT decreased the fluorescence, and the subsequent addition of ATP recovered partially the decreased intensity, as is similar to the case of the intact enzyme. The static parameters of the CAT–TAB complex were similar to those of the intact enzyme, suggesting that a somewhat impaired structure of CAT is repaired on the formation of the complex with TAB. The mutational analysis of the fluorescence showed that Trp314 but not Trp332 is responsible for the observed fluorescence changes. The role of the TAB domain in the intact enzyme is considered to enhance the binding efficiency of lysyl-adenylate to the CAT domain. [Copyright &y& Elsevier]

Abstract: The domain combination pair approach is employed to derive putative protein domain–domain interactions (DDI) from the protein–protein interactions (PPI) database DIP. The results of putative DDI are computed for seven species. To determine the prediction performance, putative DDI results are compared with that of the database InterDom, where an average matching ratio of about 76% can be achieved. Several real PPI pathways are reconstructed based on the predicted DDI results. It is found that the pathways could be reconstructed with reasonable accuracy. Furthermore, a novel quantity, so called AP-order index, is introduced to predict the regulatory order for six PPI pathways. It is found that the AP-order index is a very reliable parameter to determine the regulatory order of PPI. [Copyright &y& Elsevier]

Protein–protein interactions (PPIs) play important roles in most fundamental cellular processes including cell cycle, metabolism, and cell proliferation. Therefore, the development of effective statistical approaches to predicting protein interactions based on recently available large-scale experimental data is very important. Because protein domains are the functional units of proteins and PPIs are mostly achieved through domain–domain interactions (DDIs), the modeling and analysis of protein interactions at the domain level may be more informative and insightful. However, due to the large number of domains, the number of parameters to be estimated is very large, yet the amount of information for statistical inference is quite limited. In this article we propose a full Bayesian method and a semi-Bayesian method for simultaneously estimating DDI probabilities, the false positive rate, and the false negative rate of high-throughput data through integrating data from several organisms. We also propose a model to associate protein interaction probabilities with domain interaction probabilities that reflects the number of domains in each protein. Our Bayesian methods are compared with the likelihood-based approach ( Deng et al., 2002 , Genome Research 12, 1504–1508; Liu, Liu, and Zhao, 2005 , Bioinformatics 21, 3279–3285) developed using the expectation maximization algorithm. We show that the full Bayesian method has the smallest mean square error through both simulations and theoretical justification under a special scenario. The large-scale PPI data obtained from high-throughput yeast two-hybrid experiments are used to demonstrate the advantages of the Bayesian approaches. [ABSTRACT FROM AUTHOR]

Abstract: Structural data as collated in the Protein Data Bank (PDB) have been widely applied in the study and prediction of protein–protein interactions. However, since the basic PDB Entries contain only the contents of the asymmetric unit rather than the biological unit, some key interactions may be missed by analysing only the PDB Entry. A total of 69,054 SCOP (Structural Classification of Proteins) domains were examined systematically to identify the number of additional novel interacting domain pairs and interfaces found by considering the biological unit as stored in the PQS (Protein Quaternary Structure) database. The PQS data adds 25,965 interacting domain pairs to those seen in the PDB Entries to give a total of 61,783 redundant interacting domain pairs. Redundancy filtering at the level of the SCOP family shows PQS to increase the number of novel interacting domain-family pairs by 302 (13.3%) from 2277, but only 16/302 (1.4%) of the interacting domain pairs have the two domains in different SCOP families. This suggests the biological units add little to the elucidation of novel biological interaction networks. However, when the orientation of the domain pairs is considered, the PQS data increases the number of novel domain–domain interfaces observed by 1455 (34.5%) to give 5677 non-redundant domain–domain interfaces. In all, 162/1455 novel domain–domain interfaces are between domains from different families, an increase of 8.9% over the PDB Entries. Overall, the PQS biological units provide a rich source of novel domain–domain interfaces that are not seen in the studied PDB Entries, and so PQS domain–domain interaction data should be exploited wherever possible in the analysis and prediction of protein–protein interactions. [Copyright &y& Elsevier]

Abstract: Recent advances in functional genomics have helped generate large-scale high-throughput protein interaction data. Such networks, though extremely valuable towards molecular level understanding of cells, do not provide any direct information about the regions (domains) in the proteins that mediate the interaction. Here, we performed co-evolutionary analysis of domains in interacting proteins in order to understand the degree of co-evolution of interacting and non-interacting domains. Using a combination of sequence and structural analysis, we analyzed protein–protein interactions in F1-ATPase, Sec23p/Sec24p, DNA-directed RNA polymerase and nuclear pore complexes, and found that interacting domain pair(s) for a given interaction exhibits higher level of co-evolution than the non-interacting domain pairs. Motivated by this finding, we developed a computational method to test the generality of the observed trend, and to predict large-scale domain–domain interactions. Given a protein–protein interaction, the proposed method predicts the domain pair(s) that is most likely to mediate the protein interaction. We applied this method on the yeast interactome to predict domain–domain interactions, and used known domain–domain interactions found in PDB crystal structures to validate our predictions. Our results show that the prediction accuracy of the proposed method is statistically significant. Comparison of our prediction results with those from two other methods reveals that only a fraction of predictions are shared by all the three methods, indicating that the proposed method can detect known interactions missed by other methods. We believe that the proposed method can be used with other methods to help identify previously unrecognized domain–domain interactions on a genome scale, and could potentially help reduce the search space for identifying interaction sites. [Copyright &y& Elsevier]

The ClC family of chloride channels and transporters includes several members in which mutations have been associated with human disease. Clearly, an understanding of the structure–function relationships of these proteins will be critical in defining the molecular mechanisms underlying disease pathogenesis. The X-ray crystal structure of prokaryotic ClC proteins provides an exquisite template with which to model molecular aspects of eukaryotic ClC protein function. The dimeric structure of these proteins highlights the pivotal importance of intermolecular interactions in the modulation of channel/transporter activity, while mutagenesis studies implicate a crucial role for intrinsic interdomain interactions in regulated function. In this review, we will initially focus on the channel forming members of this family and discuss interactions within homodimeric channel complexes important for gating. Finally, with regard to both channel and transporter family members, we will discuss the multiple heteromeric interactions which occur with cytosolic proteins, and the putative functional impact of these interactions. [ABSTRACT FROM AUTHOR]