Identification of drug target candidates of the swine pathogen <Emphasis Type="Italic">Actinobacillus pleuropneumoniae</Emphasis> by construction of protein–protein interaction network

Porcine pleuropneumonia caused by Actinobacillus pleuropneumoniae has led to severe economic losses in the pig industry worldwide. A. pleuropneumoniae displays various levels of antimicrobial resistance, leading to the dire need to identify new drug targets. Protein–protein interaction (PPI) network can aid the identification of drug targets by discovering essential proteins during the life of bacteria. The aim of this study is to identify drug target candidates of A. pleuropneumoniae from essential proteins in PPI network. The homologous protein mapping method (HPM) was utilized to construct A. pleuropneumoniae PPI network. Afterwards, the subnetwork centered with H-NS was selected to verify the PPI network using bacterial two-hybrid assays. Drug target candidates were identified from the hub proteins by analyzing the topology of the network using interaction degree and homologous comparison with the pig proteome. An A. pleuropneumoniae PPI network containing 2737 non-redundant interaction pairs among 533 proteins was constructed. These proteins were distributed in 21 COG functional categories and 28 KEGG metabolic pathways. The A. pleuropneumoniae PPI network was scale free and the similar topological tendencies were found when compared with other bacteria PPI network. Furthermore, 56.3% of the H-NS subnetwork interactions were validated. 57 highly connected proteins (hub proteins) were identified from the A. pleuropneumoniae PPI network. Finally, 9 potential drug targets were identified from the hub proteins, with no homologs in swine. This study provides drug target candidates, which are promising for further investigations to explore lead compounds against A. pleuropneumoniae.     

Virtual identification of essential proteins within the protein interaction network of yeast

Topological analysis of large scale protein-protein interaction networks (PINs) is important for understanding the organizational and functional principles of individual proteins. The number of interactions that a protein has in a PIN has been observed to be correlated with its indispensability. Essential proteins generally have more interactions than the nonessential ones. We show here that the lethality associated with removal of a protein from the yeast proteome correlates with different centrality measures of the nodes in the PIN, such as the closeness of a protein to many other proteins, or the number of pairs of proteins which need a specific protein as an intermediary in their communications, or the participation of a protein in different protein clusters in the PIN. These measures are significantly better than random selection in identifying essential proteins in a PIN. Centrality measures based on graph spectral properties of the network, in particular the subgraph centrality, show the best performance in identifying essential proteins in the yeast PIN. Subgraph centrality gives important structural information about the role of individual proteins, and permits the selection of possible targets for rational drug discovery through the identification of essential proteins in the PIN.     

Protein bipartivity and essentiality in the yeast protein-protein interaction network

Protein-protein interaction networks (PINs) are structured by means of a few highly connected proteins linked to a large number of less-connected ones. Essential proteins have been found to be more abundant among these highly connected proteins. Here we demonstrate that the likelihood that removal of a protein in a PIN will prove lethal to yeast correlates with the lack of bipartivity of the protein. A protein is bipartite if it can be partitioned in such a way that there are two groups of proteins with intergroup, but not intragroup, interactions. The abundance of essential proteins found among the least bipartite proteins clearly exceeds that found among the most connected ones. For instance, among the top 50 proteins ranked by their lack of bipartivity 62% are essential proteins. However, this percentage is only 38% for proteins ranked according to their number of interactions. Protein bipartivity also surpasses another 5 measures of protein centrality in yeast PIN in identifying essential proteins and doubles the number of essential proteins selected at random. We propose a possible mechanism for the evolution of essential proteins in yeast PIN based on the duplication-divergence scheme. We conclude that a replica protein evolving from a nonbipartite target will also be nonbipartite with high probability. Consequently, these new replicas evolving from nonbipartite (essential) targets will with high probability be essential.     

Promiscuous domains: facilitating stability of the yeast protein-protein interaction network

Domain-domain interactions are a critical type of the mechanisms mediating protein-protein interactions (PPIs). For a given protein domain, its ability to combine with distinct domains is usually referred to as promiscuity or versatility. Interestingly, a previous study has reported that a domain's promiscuity may reflect its ability to interact with other domains in human proteins. In this work, promiscuous domains were first identified from the yeast genome. Then, we sought to determine what roles promiscuous domains might play in the PPI network. Mapping the promiscuous domains onto the proteins in this network revealed that, consistent with the previous knowledge, the hub proteins were significantly enriched with promiscuous domains. We also found that the set of hub proteins were not the same set as those proteins with promiscuous domains, although there was some overlap. Analysis of the topological properties of this yeast PPI network showed that the characteristic path length of the network increased significantly after deleting proteins with promiscuous domains. This indicated that communication between two proteins was longer and the network stability decreased. These observations suggested that, as the hub proteins, proteins with promiscuous domains might play a role in maintaining network stability. In addition, functional analysis revealed that proteins with promiscuous domains mainly participated in the "Folding, Sorting, and Degradation" and "Replication and Repair" biological pathways, and that they significantly execute key molecular functions, such as "nucleoside-triphosphatase activity (GO:0017111)."     

Integrative identification of Arabidopsis mitochondrial proteome and its function exploitation through protein interaction network

Mitochondria are major players on the production of energy, and host several key reactions involved in basic metabolism and biosynthesis of essential molecules. Currently, the majority of nucleus-encoded mitochondrial proteins are unknown even for model plant Arabidopsis. We reported a computational framework for predicting Arabidopsis mitochondrial proteins based on a probabilistic model, called Naive Bayesian Network, which integrates disparate genomic data generated from eight bioinformatics tools, multiple orthologous mappings, protein domain properties and co-expression patterns using 1,027 microarray profiles. Through this approach, we predicted 2,311 candidate mitochondrial proteins with 84.67% accuracy and 2.53% FPR performances. Together with those experimental confirmed proteins, 2,585 mitochondria proteins (named CoreMitoP) were identified, we explored those proteins with unknown functions based on protein-protein interaction network (PIN) and annotated novel functions for 26.65% CoreMitoP proteins. Moreover, we found newly predicted mitochondrial proteins embedded in particular subnetworks of the PIN, mainly functioning in response to diverse environmental stresses, like salt, draught, cold, and wound etc. Candidate mitochondrial proteins involved in those physiological acitivites provide useful targets for further investigation. Assigned functions also provide comprehensive information for Arabidopsis mitochondrial proteome.     

A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory

Background

The study of biological interaction networks is a central theme of systems biology. Here, we investigate the relationships between two distinct types of interaction networks: the metabolic pathway map and the protein-protein interaction network (PIN). It has long been established that successive enzymatic steps are often catalyzed by physically interacting proteins forming permanent or transient multi-enzymes complexes. Inspecting high-throughput PIN data, it was shown recently that, indeed, enzymes involved in successive reactions are generally more likely to interact than other protein pairs. In our study, we expanded this line of research to include comparisons of the underlying respective network topologies as well as to investigate whether the spatial organization of enzyme interactions correlates with metabolic efficiency.

Results

Analyzing yeast data, we detected long-range correlations between shortest paths between proteins in both network types suggesting a mutual correspondence of both network architectures. We discovered that the organizing principles of physical interactions between metabolic enzymes differ from the general PIN of all proteins. While physical interactions between proteins are generally dissortative, enzyme interactions were observed to be assortative. Thus, enzymes frequently interact with other enzymes of similar rather than different degree. Enzymes carrying high flux loads are more likely to physically interact than enzymes with lower metabolic throughput. In particular, enzymes associated with catabolic pathways as well as enzymes involved in the biosynthesis of complex molecules were found to exhibit high degrees of physical clustering. Single proteins were identified that connect major components of the cellular metabolism and may thus be essential for the structural integrity of several biosynthetic systems.

Conclusion

Our results reveal topological equivalences between the protein interaction network and the metabolic pathway network. Evolved protein interactions may contribute significantly towards increasing the efficiency of metabolic processes by permitting higher metabolic fluxes. Thus, our results shed further light on the unifying principles shaping the evolution of both the functional (metabolic) as well as the physical interaction network.     

Evolution of Specificity in Protein-Protein Interactions

Hub proteins are proteins that maintain promiscuous molecular recognition. Because they are reported to play essential roles in cellular control, there has been a special interest in the study of their structural and functional properties, yet the mechanisms by which they evolve to maintain functional interactions are poorly understood. By combining biophysical simulations of coarse-grained proteins and analysis of proteins-complex crystallographic structures, we seek to elucidate those mechanisms. We focus on two types of hub proteins: Multi hubs, which interact with their partners through different interfaces, and Singlish hubs, which do so through a single interface. We show that loss of structural stability is required for the evolution of protein-protein-interaction (PPI) networks, and it is more profound in Singlish hub systems. In addition, different ratios of hydrophobic to electrostatic interfacial amino acids are shown to support distinct network topologies (i.e., Singlish and Multi systems), and therefore underlie a fundamental design principle of PPI in a crowded environment. We argue that the physical nature of hydrophobic and electrostatic interactions, in particular, their favoring of either same-type interactions (hydrophobic-hydrophobic), or opposite-type interactions (negatively-positively charged) plays a key role in maintaining the network topology while allowing the protein amino acid sequence to evolve.     

Evolution of Specificity in Protein-Protein Interactions

Hub proteins are proteins that maintain promiscuous molecular recognition. Because they are reported to play essential roles in cellular control, there has been a special interest in the study of their structural and functional properties, yet the mechanisms by which they evolve to maintain functional interactions are poorly understood. By combining biophysical simulations of coarse-grained proteins and analysis of proteins-complex crystallographic structures, we seek to elucidate those mechanisms. We focus on two types of hub proteins: Multi hubs, which interact with their partners through different interfaces, and Singlish hubs, which do so through a single interface. We show that loss of structural stability is required for the evolution of protein-protein-interaction (PPI) networks, and it is more profound in Singlish hub systems. In addition, different ratios of hydrophobic to electrostatic interfacial amino acids are shown to support distinct network topologies (i.e., Singlish and Multi systems), and therefore underlie a fundamental design principle of PPI in a crowded environment. We argue that the physical nature of hydrophobic and electrostatic interactions, in particular, their favoring of either same-type interactions (hydrophobic-hydrophobic), or opposite-type interactions (negatively-positively charged) plays a key role in maintaining the network topology while allowing the protein amino acid sequence to evolve.     

Network Neighbors of Drug Targets Contribute to Drug Side-Effect Similarity

In pharmacology, it is essential to identify the molecular mechanisms of drug action in order to understand adverse side effects. These adverse side effects have been used to infer whether two drugs share a target protein. However, side-effect similarity of drugs could also be caused by their target proteins being close in a molecular network, which as such could cause similar downstream effects. In this study, we investigated the proportion of side-effect similarities that is due to targets that are close in the network compared to shared drug targets. We found that only a minor fraction of side-effect similarities (5.8 %) are caused by drugs targeting proteins close in the network, compared to side-effect similarities caused by overlapping drug targets (64%). Moreover, these targets that cause similar side effects are more often in a linear part of the network, having two or less interactions, than drug targets in general. Based on the examples, we gained novel insight into the molecular mechanisms of side effects associated with several drug targets. Looking forward, such analyses will be extremely useful in the process of drug development to better understand adverse side effects.     

Evidence of physical interactions of puroindoline proteins using the yeast two-hybrid system

The puroindoline proteins PINA and PINB play key roles in determining wheat grain texture and also have potential antimicrobial roles. Many recent studies show that their roles in grain texture involve some interaction or interdependence, and their antimicrobial activity may also involve formation of protein complexes. The issue of whether any homo- and/or heteromeric associations occur amongst the PIN proteins is thus critical for understanding their biological functions and exploiting them for grain texture modifications or antimicrobial applications, but is as yet unresolved. This work has utilised the well-established yeast two-hybrid system to directly address this issue. The results confirm occurrence of in vivo interactions between the two PIN proteins for the first time, and show that PINB interacts with itself and also interacts, although somewhat weakly, with PINA, while PINA is a weaker interactor. The results explain the many reported observations suggesting a co-operative interaction between the two proteins and provide a rapid and efficient tool for testing the effects of various alleles/mutations on the interactions and lipid binding properties of these proteins, which are of functional significance to grain texture and antimicrobial defence functions.     

Beyond conventional antibiotics for the future treatment of methicillin-resistant Staphylococcus aureus infections: two novel alternatives

The majority of antibiotics currently used to treat methicillin-resistant Staphylococus aureus (MRSA) infections target bacterial cell wall synthesis or protein synthesis. Only daptomycin has a novel mode of action. Reliance on limited targets for MRSA chemotherapy, has contributed to antimicrobial resistance. Two alternative approaches to the treatment of S.?aureus infection, particularly those caused by MRSA, that have alternative mechanisms of action and that address the challenge of antimicrobial resistance are cationic host defence peptides and agents that target S.?aureus virulence. Cationic host defence peptides have multiple mechanisms of action and are less likely than conventional agents to select resistant mutants. They are amenable to modifications that improve their stability, effectiveness and selectivity. Some cationic defence peptides such as bactenecin, mucroporin and imcroporin have potent in vitro bactericidal activity against MRSA. Antipathogenic agents also have potential to limit the pathogenesis of S?aureus. These are generally small molecules that inhibit virulence targets in S.?aureus without killing the bacterium and therefore have limited capacity to promote resistance development. Potential antipathogenic targets include the sortase enzyme system, the accessory gene regulator (agr) and the carotenoid biosynthetic pathway. Inhibitors of these targets have been identified and these may have potential for further development.     

Strategic genome-scale prioritization of unique drug targets: A case study of Streptococcus gordonii

The current reach of genomics extends facilitated identification of microbial virulence factors, a primary objective for antimicrobial drug and vaccine design. Many putative proteins are yet to be identified which can act as potent drug targets. There is lack and limitation of methods which appropriately combine several omics ways for putative and new drug target identification. The study emphasizes a combined bioinformatic and theoretical method of screening unique and putative drug targets, lacking similarity with experimentally reported essential genes and drug targets. Synteny based comparison was carried out with 11 streptococci considering S. gordonii as reference genome. It revealed 534 non-homologous genes of which 334 were putative. Similarity search against host proteome, metabolic pathway annotation and subcellular localization predication identified 16 potent drug targets. This is a first attempt of several combinational approaches of similarity search with target protein structural features for screening drug targets, yielding a pipeline which can be substantiated to other human pathogens.     

Affinity capillary electrophoresis for the screening of novel antimicrobial targets

The increasing number of multiantibiotic-resistant organisms, including methicillin-resistant Staphylococcus aureus (MRSA), requires the development of novel chemotherapies that are structurally distinct and exempt from current resistance mechanisms. Bioinformatics data mining of microbial genomes has revealed numerous previously unexploited essential open reading frames (ORFs) of unknown biochemical function. The potential of these proteins as screening targets is not readily apparent because most screening technologies rely on knowledge of biological function. To address this problem, the authors employed affinity capillary electrophoresis (ACE) to identify antimicrobial compounds that bound the novel target YihA. Screening a small-molecule library of 44,000 compounds initially identified 115 binders, of which 76% were confirmed. Furthermore, the ACE assay distinguished diverse compounds that possessed drug-like properties and antimicrobial activity against drug-resistant clinical isolates. These data validate ACE as a valuable tool for the fast, efficient detection of specific binding molecules that possess biological activity.     

Distinct roles of overlapping and non-overlapping regions of hub protein interfaces in recognition of multiple partners

Cellular functions of an organism are maintained by protein-protein interactions. Those proteins that bind multiple partners asynchronously (date hub proteins) are important to make the interaction network coordinated. It is known that many date hub proteins bind different partners at overlapping (OV) interfaces. To understand how OV interfaces of date hub proteins can recognize multiple partners, we analyzed the difference between OV and non-overlapping (Non-OV) regions of interfaces involved in the binding of different partners. By using the structures of 16 date hub proteins with various interaction partners (ranging from 5 to 33), we compared buried surface area, compositions of amino acid residues and secondary structures, and side-chain orientations. It was found that buried interface residues are important for recognizing multiple partners, while exposed interface residues are important for determining specificity to a particular ligand. In addition, our analyses reveal that residue compositions in OV and Non-OV regions are different and that residues in OV region show diverse side-chain torsion angles to accommodate binding to multiple targets.     

The Roles of Post-translational Modifications in the Context of Protein Interaction Networks

Among other effects, post-translational modifications (PTMs) have been shown to exert their function via the modulation of protein-protein interactions. For twelve different main PTM-types and associated subtypes and across 9 diverse species, we investigated whether particular PTM-types are associated with proteins with specific and possibly “strategic” placements in the network of all protein interactions by determining informative network-theoretic properties. Proteins undergoing a PTM were observed to engage in more interactions and positioned in more central locations than non-PTM proteins. Among the twelve considered PTM-types, phosphorylated proteins were identified most consistently as being situated in central network locations and with the broadest interaction spectrum to proteins carrying other PTM-types, while glycosylated proteins are preferentially located at the network periphery. For the human interactome, proteins undergoing sumoylation or proteolytic cleavage were found with the most characteristic network properties. PTM-type-specific protein interaction network (PIN) properties can be rationalized with regard to the function of the respective PTM-carrying proteins. For example, glycosylation sites were found enriched in proteins with plasma membrane localizations and transporter or receptor activity, which generally have fewer interacting partners. The involvement in disease processes of human proteins undergoing PTMs was also found associated with characteristic PIN properties. By integrating global protein interaction networks and specific PTMs, our study offers a novel approach to unraveling the role of PTMs in cellular processes.     

Identification of transient hub proteins and the possible structural basis for their multiple interactions

Proteins that can interact with multiple partners play central roles in the network of protein-protein interactions. They are called hub proteins, and recently it was suggested that an abundance of intrinsically disordered regions on their surfaces facilitates their binding to multiple partners. However, in those studies, the hub proteins were identified as proteins with multiple partners, regardless of whether the interactions were transient or permanent. As a result, a certain number of hub proteins are subunits of stable multi-subunit proteins, such as supramolecules. It is well known that stable complexes and transient complexes have different structural features, and thus the statistics based on the current definition of hub proteins will hide the true nature of hub proteins. Therefore, in this paper, we first describe a new approach to identify proteins with multiple partners dynamically, using the Protein Data Bank, and then we performed statistical analyses of the structural features of these proteins. We refer to the proteins as transient hub proteins or sociable proteins, to clarify the difference with hub proteins. As a result, we found that the main difference between sociable and nonsociable proteins is not the abundance of disordered regions, in contrast to the previous studies, but rather the structural flexibility of the entire protein. We also found greater predominance of charged and polar residues in sociable proteins than previously reported.