Prediction and analysis of the protein interactome in Pseudomonas aeruginosa to enable network-based drug target selection

Combining Network Pharmacology and Experimental Verification to Ascertain the Mechanism of Action of Asparagus officinalis Against the Brain Damage Caused by Fluorosis

Asparagus officinalis (ASP) has antioxidation, anti-inflammatory, antiaging, and immune system-enhancing effects. We explored the preventive and therapeutic consequences of ASP on the brain damage elicited by fluorosis through network pharmacology and in vivo experimental validation. We ascertained the pharmaceutically active ingredients and drug targets of ASP from the Traditional Chinese Medicine Systems Pharmacology database, predicted the disease targets of fluorosis-induced brain injury using GeneCards and Online Mendelian Inheritance in Man databases, obtained target protein-protein interaction networks in the Search Tool for the Retrieval of Interacting Genes/Proteins database, used Cytoscape to obtain key targets and active ingredients, and conducted enrichment analyses of key targets in the Database for Annotation, Visualization and Integrated Discovery. Enrichment analyses showed that "mitogen-activated protein kinase" (MAPK), "phosphoinositide 3-kinase/protein kinase B" (PI3K-Akt), "nuclear factor-kappa B" (NF-κB), and the "neurotrophin signaling pathway" were the most enriched biological processes and signaling pathways. ASP could alleviate fluorosis-based injury, improve brain-tissue damage, increase urinary fluoride content, and improve oxidation levels and inflammatory-factor levels in the body. ASP could also reduce dental fluorosis, bone damage, fluoride concentrations in blood and bone, and accumulation of lipid peroxide. Upon ASP treatment, expression of silent information regulator (SIRT)1, brain-derived neurotrophic factor (BDNF), tropomyosin receptor kinase B (TrkB), MAPK, NF-κB, PI3K, Akt, and B-cell lymphoma-2 in rat brain tissue increased gradually, whereas that of Bax, caspase-3, and p53 decreased gradually. We demonstrated that ASP could regulate the brain damage caused by fluorosis through the SIRT1/BDNF/TrkB signaling pathway, and reported the possible part played by ASP in preventing and treating fluorosis.

Artificial intelligence and machine learning in clinical pharmacological research

BACKGROUND

Clinical pharmacology research has always involved computational analysis. With the abundance of drug-related data available, the integration of artificial intelligence (AI) and machine learning (ML) methods has emerged as a promising way to enhance clinical pharmacology research.

METHODS

Based on an accepted definition of clinical pharmacology as a field of research dealing with all aspects of drug-human interactions, the analysis included publications from institutes specializing in clinical pharmacology. Research topics and the most used machine learning methods in clinical pharmacology were retrieved from the PubMed database and summarized.

RESULTS

ML was identified in 674 publications attributed to clinical pharmacology research, with a significant increase in publication activity over the last decade. Notable research topics addressed by ML/AI included Covid-19-related clinical pharmacology research, clinical neuropharmacology, drug safety and risk assessment, clinical pharmacology related to cancer research, and antimicrobial and antiviral research unrelated to Covid-19. In terms of ML methods, neural networks, random forests, and support vector machines were frequently mentioned in the abstracts of the retrieved papers.

CONCLUSIONS

ML, and AI in general, is increasingly being used in various research areas within clinical pharmacology. This report presents specific examples of applications and highlights the most used ML methods.

Mechanism of action of Asparagus officinalis extract against multiple myeloma using bioinformatics tools, in silico and in vitro study

Introduction: Asparagus (Asparagus officinalis) is a perennial flowering plant species. Its main components have tumor-prevention, immune system-enhancement, and anti-inflammation effects. Network pharmacology is a powerful approach that is being applied increasingly to research of herbal medicines. Herb identification, study of compound targets, network construction, and network analysis have been used to elucidate how herbal medicines work. However, the interaction of bioactive substances from asparagus with the targets involved in multiple myeloma (MM) has not been elucidated. We explored the mechanism of action of asparagus in MM through network pharmacology and experimental verification. Methods: The active ingredients and corresponding targets of asparagus were acquired from the Traditional Chinese Medicine System Pharmacology database, followed by identification of MM-related target genes using GeneCards and Online Mendelian Inheritance in Man databases, which were matched with the potential targets of asparagus. Potential targets were identified and a target network of traditional Chinese medicine was constructed. The STRING database and Cytoscape were utilized to create protein-protein interaction (PPI) networks and further screening of core targets. Results: The intersection of target genes and core target genes of the phosphoinositide 3-kinase/protein kinase B (PI3K/AKT) pathway was enriched, the top-five core target genes were selected, and the binding affinity of corresponding compounds to the top-five core targets was analyzed using molecular docking. Network pharmacology identified nine active components of asparagus from databases based on oral bioavailability and drug similarity, and predicted 157 potential targets related to asparagus. Enrichment analyses showed that "steroid receptor activity" and the "PI3K/AKT signaling pathway" were the most enriched biological process and signaling pathway, respectively. According to the top-10 core genes and targets of the PPI pathway, AKT1, interleukin (IL)-6, vascular endothelial growth factor (VEGF)A, MYC, and epidermal growth factor receptor (EGFR) were selected for molecular docking. The latter showed that five core targets of the PI3K/AKT signaling pathway could bind to quercetin, among which EGFR, IL-6, and MYC showed strong docking, and the diosgenin ligand could bind to VEGFA. Cell experiments showed that asparagus, through the PI3K/AKT/NF-κB pathway, inhibited the proliferation and migration of MM cells, and caused retardation and apoptosis of MM cells in the G0/G1 phase. Discussion: In this study, the anti-cancer activity of asparagus against MM was demonstrated using network pharmacology, and potential pharmacological mechanisms were inferred using in vitro experimental data.

Computational Network Inference for Bacterial Interactomics

Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.

Metabolic reprogramming of Pseudomonas aeruginosa by phage-based quorum sensing modulation

The Pseudomonas quinolone signal (PQS) is a multifunctional quorum sensing molecule of key importance to P. aeruginosa. Here, we report that the lytic Pseudomonas bacterial virus LUZ19 targets this population density-dependent signaling system by expressing quorum sensing targeting protein (Qst) early during infection. We demonstrate that Qst interacts with PqsD, a key host quinolone signal biosynthesis pathway enzyme, resulting in decreased levels of PQS and its precursor 2-heptyl-4(1H)-quinolone. The lack of a functional PqsD enzyme impairs LUZ19 infection but is restored by external supplementation of 2-heptyl-4(1H)-quinolone, suggesting that LUZ19 exploits the PQS system for successful infection. We establish a broad functional interaction network of Qst, which includes enzymes of cofactor biosynthesis pathways (CoaC/ThiD) and a non-ribosomal peptide synthetase pathway (PA1217). Qst therefore represents an exquisite example of intricate reprogramming of the bacterium by a phage, which may be further exploited as tool to combat antibiotic resistant bacterial pathogens.

Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases

In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms that resist conventional antibiotic treatment has steadily increased. Thus, it is now unquestionable that infectious diseases are significant global burdens that urgently require innovative treatment strategies. Emerging studies have demonstrated that artificial intelligence (AI) can transform drug delivery to promote effective treatment of infectious diseases. In this review, we propose to evaluate the significance, essential principles, and popular tools of AI in drug delivery for infectious disease treatment. Specifically, we will focus on the achievements and key findings of current research, as well as the applications of AI on drug delivery throughout the whole antimicrobial treatment process, with an emphasis on drug development, treatment regimen optimization, drug delivery system and administration route design, and drug delivery outcome prediction. To that end, the challenges of AI in drug delivery for infectious disease treatments and their current solutions and future perspective will be presented and discussed.

Exploration in the mechanism of fucosterol for the treatment of non-small cell lung cancer based on network pharmacology and molecular docking

Fucosterol, a sterol isolated from brown algae, has been demonstrated to have anti-cancer properties. However, the effects and underlying molecular mechanism of fucosterol on non-small cell lung cancer remain to be elucidated. In this study, the corresponding targets of fucosterol were obtained from PharmMapper, and NSCLC related targets were gathered from the GeneCards database, and the candidate targets of fucosterol-treated NSCLC were predicted. The mechanism of fucosterol against NSCLC was identified in DAVID6.8 by enrichment analysis of GO and KEGG, and protein–protein interaction data were collected from STRING database. The hub gene GRB2 was further screened out and verified by molecular docking. Moreover, the relationship of GRB2 expression and immune infiltrates were analyzed by the TIMER database. The results of network pharmacology suggest that fucosterol acts against candidate targets, such as MAPK1, EGFR, GRB2, IGF2, MAPK8, and SRC, which regulate biological processes including negative regulation of the apoptotic process, peptidyl-tyrosine phosphorylation, positive regulation of cell proliferation. The Raf/MEK/ERK signaling pathway initiated by GRB2 showed to be significant in treating NSCLC. In conclusion, our study indicates that fucosterol may suppress NSCLC progression by targeting GRB2 activated the Raf/MEK/ERK signaling pathway, which laying a theoretical foundation for further research and providing scientific support for the development of new drugs.

Protein-protein complexes as targets for drug discovery against infectious diseases

Antibiotics are therapeutic agents against bacterial infections, however, the emergence of multiple and extremely drug-resistant microbes (Multi-Drug Resistant and Extremely Drug-Resistant) are compromising the effectiveness of the currently available treatment options. The drug resistance is not a novel crisis, the current pace of drug discovery has failed to compete with the growth of MDR and XDR pathogenic strains and therefore, it is highly central to find out novel antimicrobial drugs with unique mechanisms of action which may reduce the burden of MDR and XDR pathogenic strains. Protein-protein interactions (PPIs) are involved in a countless of the physiological and cellular phenomena and have become an attractive target to treat the diseases. Therefore, targeting PPIs in infectious agents may offer a completely novel strategy of intervention to develop anti-infective drugs that may combat the ever-increasing rate of drug resistant strains. This chapter describes how small molecule candidate inhibitors that are capable of disrupting the PPIs in pathogenic microbes and it could be an alternative lead discovery strategy to obtain novel antibiotics. Over the last three decades, there has been increasing efforts focused on the manipulation of PPIs in order to develop novel therapeutic interventions. The diversity and complexity of such a complex and highly dynamic systems pose many challenges in targeting PPIs by drug-like molecules with necessary selectivity and potency. Traditional and novel drug discovery strategies have provided tools for designing and assessing PPI inhibitors against infectious diseases.

The anti-sigma factor MucA of Pseudomonas aeruginosa: Dramatic differences of a mucA22 vs. a ΔmucA mutant in anaerobic acidified nitrite sensitivity of planktonic and biofilm bacteria in vitro and during chronic murine lung infection

Mucoid mucA22 Pseudomonas aeruginosa (PA) is an opportunistic lung pathogen of cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) patients that is highly sensitive to acidified nitrite (A-NO₂^-). In this study, we first screened PA mutant strains for sensitivity or resistance to 20 mM A-NO₂^- under anaerobic conditions that represent the chronic stages of the aforementioned diseases. Mutants found to be sensitive to A-NO₂^- included PA0964 (pmpR, PQS biosynthesis), PA4455 (probable ABC transporter permease), katA (major catalase, KatA) and rhlR (quorum sensing regulator). In contrast, mutants lacking PA0450 (a putative phosphate transporter) and PA1505 (moaA2) were A-NO₂^- resistant. However, we were puzzled when we discovered that mucA22 mutant bacteria, a frequently isolated mucA allele in CF and to a lesser extent COPD, were more sensitive to A-NO₂^- than a truncated ΔmucA deletion (Δ157–194) mutant in planktonic and biofilm culture, as well as during a chronic murine lung infection. Subsequent transcriptional profiling of anaerobic, A-NO₂^--treated bacteria revealed restoration of near wild-type transcript levels of protective NO₂^- and nitric oxide (NO) reductase (nirS and norCB, respectively) in the ΔmucA mutant in contrast to extremely low levels in the A-NO₂^--sensitive mucA22 mutant. Proteins that were S-nitrosylated by NO derived from A-NO₂^- reduction in the sensitive mucA22 strain were those involved in anaerobic respiration (NirQ, NirS), pyruvate fermentation (UspK), global gene regulation (Vfr), the TCA cycle (succinate dehydrogenase, SdhB) and several double mutants were even more sensitive to A-NO₂^-. Bioinformatic-based data point to future studies designed to elucidate potential cellular binding partners for MucA and MucA22. Given that A-NO₂^- is a potentially viable treatment strategy to combat PA and other infections, this study offers novel developments as to how clinicians might better treat problematic PA infections in COPD and CF airway diseases.

Interface-Based Structural Prediction of Novel Host-Pathogen Interactions

About 20% of the cancer incidences worldwide have been estimated to be associated with infections. However, the molecular mechanisms of exactly how they contribute to host tumorigenesis are still unknown. To evade host defense, pathogens hijack host proteins at different levels: sequence, structure, motif, and binding surface, i.e., interface. Interface similarity allows pathogen proteins to compete with host counterparts to bind to a target protein, rewire physiological signaling, and result in persistent infections, as well as cancer. Identification of host-pathogen interactions (HPIs)-along with their structural details at atomic resolution-may provide mechanistic insight into pathogen-driven cancers and innovate therapeutic intervention. HPI data including structural details is scarce and large-scale experimental detection is challenging. Therefore, there is an urgent and mounting need for efficient and robust computational approaches to predict HPIs and their complex (bound) structures. In this chapter, we review the first and currently only interface-based computational approach to identify novel HPIs. The concept of interface mimicry promises to identify more HPIs than complete sequence or structural similarity. We illustrate this concept with a case study on Kaposi's sarcoma herpesvirus (KSHV) to elucidate how it subverts host immunity and helps contribute to malignant transformation of the host cells.

Protein-protein interactions in bacteria: a promising and challenging avenue towards the discovery of new antibiotics

Antibiotics are potent pharmacological weapons against bacterial infections; however, the growing antibiotic resistance of microorganisms is compromising the efficacy of the currently available pharmacotherapies. Even though antimicrobial resistance is not a new problem, antibiotic development has failed to match the growth of resistant pathogens and hence, it is highly critical to discover new anti-infective drugs with novel mechanisms of action which will help reducing the burden of multidrug-resistant microorganisms. Protein-protein interactions (PPIs) are involved in a myriad of vital cellular processes and have become an attractive target to treat diseases. Therefore, targeting PPI networks in bacteria may offer a new and unconventional point of intervention to develop novel anti-infective drugs which can combat the ever-increasing rate of multidrug-resistant bacteria. This review describes the progress achieved towards the discovery of molecules that disrupt PPI systems in bacteria for which inhibitors have been identified and whose targets could represent an alternative lead discovery strategy to obtain new anti-infective molecules.

A Comprehensive In Silico Method to Study the QSTR of the Aconitine Alkaloids for Designing Novel Drugs

A combined in silico method was developed to predict potential protein targets that are involved in cardiotoxicity induced by aconitine alkaloids and to study the quantitative structure⁻toxicity relationship (QSTR) of these compounds. For the prediction research, a Protein-Protein Interaction (PPI) network was built from the extraction of useful information about protein interactions connected with aconitine cardiotoxicity, based on nearly a decade of literature and the STRING database. The software Cytoscape and the PharmMapper server were utilized to screen for essential proteins in the constructed network. The Calcium-Calmodulin-Dependent Protein Kinase II alpha (CAMK2A) and gamma (CAMK2G) were identified as potential targets. To obtain a deeper insight on the relationship between the toxicity and the structure of aconitine alkaloids, the present study utilized QSAR models built in Sybyl software that possess internal robustness and external high predictions. The molecular dynamics simulation carried out here have demonstrated that aconitine alkaloids possess binding stability for the receptor CAMK2G. In conclusion, this comprehensive method will serve as a tool for following a structural modification of the aconitine alkaloids and lead to a better insight into the cardiotoxicity induced by the compounds that have similar structures to its derivatives.

Prediction of Host-Pathogen Interactions for Helicobacter pylori by Interface Mimicry and Implications to Gastric Cancer

There is a strong correlation between some pathogens and certain cancer types. One example is Helicobacter pylori and gastric cancer. Exactly how they contribute to host tumorigenesis is, however, a mystery. Pathogens often interact with the host through proteins. To subvert defense, they may mimic host proteins at the sequence, structure, motif, or interface levels. Interface similarity permits pathogen proteins to compete with those of the host for a target protein and thereby alter the host signaling. Detection of host-pathogen interactions (HPIs) and mapping the re-wired superorganism HPI network-with structural details-can provide unprecedented clues to the underlying mechanisms and help therapeutics. Here, we describe the first computational approach exploiting solely interface mimicry to model potential HPIs. Interface mimicry can identify more HPIs than sequence or complete structural similarity since it appears more common than the other mimicry types. We illustrate the usefulness of this concept by modeling HPIs of H. pylori to understand how they modulate host immunity, persist lifelong, and contribute to tumorigenesis. H. pylori proteins interfere with multiple host pathways as they target several host hub proteins. Our results help illuminate the structural basis of resistance to apoptosis, immune evasion, and loss of cell junctions seen in H. pylori-infected host cells.

Pseudomonas aeruginosa cells attached to a surface display a typical proteome early as 20 minutes of incubation

Biofilms are present in all environments and often result in negative effects due to properties of the biofilm lifestyle and especially antibiotics resistance. Biofilms are associated with chronic infections. Controlling bacterial attachment, the first step of biofilm formation, is crucial for fighting against biofilm and subsequently preventing the persistence of infection. Thus deciphering the underlying molecular mechanisms involved in attachment could allow discovering molecular targets from it would be possible to develop inhibitors against bacterial colonization and potentiate antibiotherapy. To identify the key components and pathways that aid the opportunistic pathogen Pseudomonas aeruginosa in attachment we performed for the first time a proteomic analysis as early as after 20 minutes of incubation using glass wool fibers as a surface. We compared the protein contents of the attached and unattached bacteria. Using mass spectrometry, 3043 proteins were identified. Our results showed that, as of 20 minutes of incubation, using stringent quantification criteria 616 proteins presented a modification of their abundance in the attached cells compared to their unattached counterparts. The attached cells presented an overall reduced gene expression and characteristics of slow-growing cells. The over-accumulation of outer membrane proteins, periplasmic folding proteins and O-antigen chain length regulators was also observed, indicating a profound modification of the cell envelope. Consistently the sigma factor AlgU required for cell envelope homeostasis was highly over-accumulated in attached cells. In addition our data suggested a role of alarmone (p)ppGpp and polyphosphate during the early attachment phase. Furthermore, almost 150 proteins of unknown function were differentially accumulated in the attached cells. Our proteomic analysis revealed the existence of distinctive biological features in attached cells as early as 20 minutes of incubation. Analysis of some mutants demonstrated the interest of this proteomic approach in identifying genes involved in the early phase of adhesion to a surface.

Protein-protein interaction and molecular dynamics analysis for identification of novel inhibitors in Burkholderia cepacia GG4

The lack of complete treatments and appearance of multiple drug-resistance strains of Burkholderia cepacia complex (Bcc) are causing an increased risk of lung infections in cystic fibrosis patients. Bcc infection is a big risk to human health and demands an urgent need to identify new therapeutics against these bacteria. Network biology has emerged as one of the prospective hope in identifying novel drug targets and hits. We have applied protein-protein interaction methodology to identify new drug-target candidates (orthologs) in Burkhloderia cepacia GG4, which is an important strain for studying the quorum-sensing phenomena. An evolutionary based ortholog mapping approach has been applied for generating the large scale protein-protein interactions in B. Cepacia. As a case study, one of the identified drug targets; GEM_3202, a NH (3)-dependent NAD synthetase protein has been studied and the potential ligand molecules were screened using the ZINC database. The three dimensional structure (NH (3)-dependent NAD synthetase protein) has been predicted from MODELLERv9.11 tool using multiple PDB templates such as 3DPI, 2PZ8 and 1NSY with sequence identity of 76%, 50% and 50% respectively. The structure has been validated with Ramachandaran plot having 100% residues of NadE in allowed region and overall quality factor of 81.75 using ERRAT tool. High throughput screening and Vina resulted in two potential hits against NadE such as ZINC83103551 and ZINC38008121. These molecules showed lowest binding energy of -5.7kcalmol^-1 and high stability in the binding pockets during molecular dynamics simulation analysis. The similar approach for target identification could be applied for clinical strains of other pathogenic microbes.

Network-assisted investigation of virulence and antibiotic-resistance systems in Pseudomonas aeruginosa

Pseudomonas aeruginosa is a Gram-negative bacterium of clinical significance. Although the genome of PAO1, a prototype strain of P. aeruginosa, has been extensively studied, approximately one-third of the functional genome remains unknown. With the emergence of antibiotic-resistant strains of P. aeruginosa, there is an urgent need to develop novel antibiotic and anti-virulence strategies, which may be facilitated by an approach that explores P. aeruginosa gene function in systems-level models. Here, we present a genome-wide functional network of P. aeruginosa genes, PseudomonasNet, which covers 98% of the coding genome, and a companion web server to generate functional hypotheses using various network-search algorithms. We demonstrate that PseudomonasNet-assisted predictions can effectively identify novel genes involved in virulence and antibiotic resistance. Moreover, an antibiotic-resistance network based on PseudomonasNet reveals that P. aeruginosa has common modular genetic organisations that confer increased or decreased resistance to diverse antibiotics, which accounts for the pervasiveness of cross-resistance across multiple drugs. The same network also suggests that P. aeruginosa has developed mechanism of trade-off in resistance across drugs by altering genetic interactions. Taken together, these results clearly demonstrate the usefulness of a genome-scale functional network to investigate pathogenic systems in P. aeruginosa.

Probing the protein interaction network of Pseudomonas aeruginosa cells by chemical cross-linking mass spectrometry

In pathogenic Gram-negative bacteria, interactions among membrane proteins are key mediators of host cell attachment, invasion, pathogenesis, and antibiotic resistance. Membrane protein interactions are highly dependent upon local properties and environment, warranting direct measurements on native protein complex structures as they exist in cells. Here we apply in vivo chemical cross-linking mass spectrometry, to reveal the first large-scale protein interaction network in Pseudomonas aeruginosa, an opportunistic human pathogen, by covalently linking interacting protein partners, thereby fixing protein complexes in vivo. A total of 626 cross-linked peptide pairs, including previously unknown interactions of many membrane proteins, are reported. These pairs not only define the existence of these interactions in cells but also provide linkage constraints for complex structure predictions. Structures of three membrane proteins, namely, SecD-SecF, OprF, and OprI are predicted using in vivo cross-linked sites. These findings improve understanding of membrane protein interactions and structures in cells.

Systematic identification of hypothetical bacteriophage proteins targeting key protein complexes of Pseudomonas aeruginosa

Addressing the functionality of predicted genes remains an enormous challenge in the postgenomic era. A prime example of genes lacking functional assignments are the poorly conserved, early expressed genes of lytic bacteriophages, whose products are involved in the subversion of the host metabolism. In this study, we focused on the composition of important macromolecular complexes of Pseudomonas aeruginosa involved in transcription, DNA replication, fatty acid biosynthesis, RNA regulation, energy metabolism, and cell division during infection with members of seven distinct clades of lytic phages. Using affinity purifications of these host protein complexes coupled to mass spectrometric analyses, 37 host complex-associated phage proteins could be identified. Importantly, eight of these show an inhibitory effect on bacterial growth upon episomal expression, suggesting that these phage proteins are potentially involved in hijacking the host complexes. Using complementary protein-protein interaction assays, we further mapped the inhibitory interaction of gp12 of phage 14-1 to the α subunit of the RNA polymerase. Together, our data demonstrate the powerful use of interactomics to unravel the biological role of hypothetical phage proteins, which constitute an enormous untapped source of novel antibacterial proteins. (Data are available via ProteomeXchange with identifier PXD001199.).

Inspecting the potential physiological and biomedical value of 44 conserved uncharacterised proteins of Streptococcus pneumoniae

BACKGROUND

The major Gram-positive coccoid pathogens cause similar invasive diseases and show high rates of antimicrobial resistance. Uncharacterised proteins shared by these organisms may be involved in virulence or be targets for antimicrobial therapy.

RESULTS

Forty four uncharacterised proteins from Streptococcus pneumoniae with homologues in Enterococcus faecalis and/or Staphylococcus aureus were selected for analysis. These proteins showed differences in terms of sequence conservation and number of interacting partners. Twenty eight of these proteins were monodomain proteins and 16 were modular, involving domain combinations and, in many cases, predicted unstructured regions. The genes coding for four of these 44 proteins were essential. Genomic and structural studies showed one of the four essential genes to code for a promising antibacterial target. The strongest impact of gene removal was on monodomain proteins showing high sequence conservation and/or interactions with many other proteins. Eleven out of 40 knockouts (one for each gene) showed growth delay and 10 knockouts presented a chaining phenotype. Five of these chaining mutants showed a lack of putative DNA-binding proteins. This suggest this phenotype results from a loss of overall transcription regulation. Five knockouts showed defective autolysis in response to penicillin and vancomycin, and attenuated virulence in an animal model of sepsis.

CONCLUSIONS

Uncharacterised proteins make up a reservoir of polypeptides of different physiological importance and biomedical potential. A promising antibacterial target was identified. Five of the 44 examined proteins seemed to be virulence factors.

Discovery of an allosteric inhibitor binding site in 3-Oxo-acyl-ACP reductase from Pseudomonas aeruginosa

3-Oxo-acyl-acyl carrier protein (ACP) reductase (FabG) plays a key role in the bacterial fatty acid synthesis II system in pathogenic microorganisms, which has been recognized as a potential drug target. FabG catalyzes reduction of a 3-oxo-acyl-ACP intermediate during the elongation cycle of fatty acid biosynthesis. Here, we report gene deletion experiments that support the essentiality of this gene in P. aeruginosa and the identification of a number of small molecule FabG inhibitors with IC50 values in the nanomolar to low micromolar range and good physicochemical properties. Structural characterization of 16 FabG-inhibitor complexes by X-ray crystallography revealed that the compounds bind at a novel allosteric site located at the FabG subunit-subunit interface. Inhibitor binding relies primarily on hydrophobic interactions, but specific hydrogen bonds are also observed. Importantly, the binding cavity is formed upon complex formation and therefore would not be recognized by virtual screening approaches. The structure analysis further reveals that the inhibitors act by inducing conformational changes that propagate to the active site, resulting in a displacement of the catalytic triad and the inability to bind NADPH.

Pathogenicity phenomena in three model systems: from network mining to emerging system-level properties

Understanding the interconnections of microbial pathogenicity phenomena, such as biofilm formation, quorum sensing and antimicrobial resistance, is a tremendous open challenge for biomedical research. Progress made by wet-lab researchers and bioinformaticians in understanding the underlying regulatory phenomena has been significant, with converging evidence from multiple high-throughput technologies. Notably, network reconstructions are already of considerable size and quality, tackling both intracellular regulation and signal mediation in microbial infection. Therefore, it stands to reason that in silico investigations would play a more active part in this research. Drug target identification and drug repurposing could take much advantage of the ability to simulate pathogen regulatory systems, host-pathogen interactions and pathogen cross-talking. Here, we review the bioinformatics resources and tools available for the study of the gram-negative bacterium Pseudomonas aeruginosa, the gram-positive bacterium Staphylococcus aureus and the fungal species Candida albicans. The choice of these three microorganisms fits the rationale of the review converging into pathogens of great clinical importance, which thrive in biofilm consortia and manifest growing antimicrobial resistance.

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

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

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Comparative Molecular docking analysis of DNA Gyrase subunit A in Pseudomonas aeruginosaPAO1

Pseudomonas aeruginosa is an opportunistic bacterium known for causing chronic infections in cystic fibrosis and chronic obstructive pulmonary disease (COPD) patients. Recently, several drug targets in Pseudomonas aeruginosa PAO1 have been reported using network biology approaches on the basis of essentiality and topology and further ranked on network measures viz. degree and centrality. Till date no drug/ligand molecule has been reported against this targets.In our work we have identified the ligand /drug molecules, through Orthologous gene mapping against Bacillus subtilis subsp. subtilis str. 168 and performed modelling and docking analysis. From the predicted drug targets in PA PAO1, we selected those drug targets which show statistically significant orthology with a model organism and whose orthologs are present in all the selected drug targets of PA PAO1.Modeling of their structure has been done using I-Tasser web server. Orthologous gene mapping has been performed using Cluster of Orthologs (COGs) and based on orthology; drugs available for Bacillus sp. have been docked with PA PAO1 protein drug targets using MoleGro virtual docker version 4.0.2.Orthologous gene for PA3168 gyrA is BS gyrAfound in Bacillus subtilis subsp. subtilis str. 168. The drugs cited for Bacillus sp. have been docked with PA genes and energy analyses have been made. Based on Orthologous gene mapping andin-silico studies, Nalidixic acid is reported as an effective drug against PA3168 gyrA for the treatment of CF and COPD.

SynSysNet: integration of experimental data on synaptic protein-protein interactions with drug-target relations

We created SynSysNet, available online at http://bioinformatics.charite.de/synsysnet, to provide a platform that creates a comprehensive 4D network of synaptic interactions. Neuronal synapses are fundamental structures linking nerve cells in the brain and they are responsible for neuronal communication and information processing. These processes are dynamically regulated by a network of proteins. New developments in interaction proteomics and yeast two-hybrid methods allow unbiased detection of interactors. The consolidation of data from different resources and methods is important to understand the relation to human behaviour and disease and to identify new therapeutic approaches. To this end, we established SynSysNet from a set of ∼1000 synapse specific proteins, their structures and small-molecule interactions. For two-thirds of these, 3D structures are provided (from Protein Data Bank and homology modelling). Drug-target interactions for 750 approved drugs and 50 000 compounds, as well as 5000 experimentally validated protein–protein interactions, are included. The resulting interaction network and user-selected parts can be viewed interactively and exported in XGMML. Approximately 200 involved pathways can be explored regarding drug-target interactions. Homology-modelled structures are downloadable in Protein Data Bank format, and drugs are available as MOL-files. Protein–protein interactions and drug-target interactions can be viewed as networks; corresponding PubMed IDs or sources are given.