Search

Introduction: Antimicrobial resistance (AMR) is a serious global threat. Identification of novel antibacterial targets is urgently warranted to help antimicrobial drug discovery programs. This study attempted identification of potential targets in two important pathogens Pseudomonas aeruginosa and Staphylococcus aureus. Methods: Transcriptomes of P. aeruginosa and S. aureus exposed to two different quorum-modulatory polyherbal formulations were subjected to network analysis to identify the most highly networked differentially expressed genes (hubs) as potential anti-virulence targets. Results: Genes associated with denitrification and sulfur metabolism emerged as the most important targets in P. aeruginosa. Increased buildup of nitrite (NO2) in P. aeruginosa culture exposed to the polyherbal formulation Panchvalkal was confirmed through in vitro assay too. Generation of nitrosative stress and inducing sulfur starvation seemed to be effective anti-pathogenic strategies against this notorious gram-negative pathogen. Important targets identified in S. aureus were the transcriptional regulator sarA, immunoglobulin-binding protein Sbi, serine protease SplA, the saeR/S response regulator system, and gamma-hemolysin components hlgB and hlgC. Conclusion: Further validation of the potential targets identified in this study is warranted through appropriate in vitro and in vivo assays in model hosts. Such validated targets can prove vital to many antibacterial drug discovery programs globally.

Introduction: Antimicrobial resistance (AMR) is a serious global threat. Identification of novel antibacterial targets is urgently warranted to help antimicrobial drug discovery programs. This study attempted identification of potential targets in two important pathogens Pseudomonas aeruginosa and Staphylococcus aureus. Methods: Transcriptomes of P. aeruginosa and S. aureus exposed to two different quorum-modulatory polyherbal formulations were subjected to network analysis to identify the most highly networked differentially expressed genes (hubs) as potential anti-virulence targets. Results: Genes associated with denitrification and sulfur metabolism emerged as the most important targets in P. aeruginosa. Increased buildup of nitrite (NO2) in P. aeruginosa culture exposed to the polyherbal formulation Panchvalkal was confirmed through in vitro assay too. Generation of nitrosative stress and inducing sulfur starvation seemed to be effective anti-pathogenic strategies against this notorious gram-negative pathogen. Important targets identified in S. aureus were the transcriptional regulator sarA, immunoglobulin-binding protein Sbi, serine protease SplA, the saeR/S response regulator system, and gamma-hemolysin components hlgB and hlgC. Conclusion: Further validation of the potential targets identified in this study is warranted through appropriate in vitro and in vivo assays in model hosts. Such validated targets can prove vital to many antibacterial drug discovery programs globally.

Detecting protein-protein interactions (PPIs) is one of the most used approaches to reveal the molecular regulation of protein of interests (POIs). Immunoprecipitation of POIs followed by mass spectrometry or western blot analysis enables us to detect co-precipitated POI-binding proteins. However, some binding proteins are lost during cell lysis or immunoprecipitation if the protein binding affinity is weak. Crosslinking POI and its binding proteins stabilizes the PPI and increases the chance of detecting the interacting proteins. Here, we introduce the method of DSP (dithiobis(succinimidyl propionate))-mediated crosslinking, followed by tandem immunoprecipitation (FLAG and HA tags). The eluted proteins interacting with POI can be analyzed by mass spectrometry or western blotting. This method has the potential to be applied to various cytoplasmic proteins.

Helicobacter pylori (H. pylori) infections have been implicated in the development of gastric ulcers and various cancers: however, the success of current therapies is compromised by rising antibiotic resistance. The virulence and pathogenicity of H. pylori is mediated by the type IV secretion system (T4SS), a multiprotein macromolecular nanomachine that transfers toxic bacterial factors and plasmid DNA between bacterial cells, thus contributing to the spread of antibiotic resistance. A key component of the T4SS is the VirB11 ATPase HP0525, which is a hexameric protein assembly. We have previously reported the design and synthesis of a series of novel 8-amino imidazo[1,2-a]pyrazine derivatives as inhibitors of HP0525. In order to improve their selectivity, and potentially develop these compounds as tools for probing the assembly of the HP0525 hexamer, we have explored the design and synthesis of potential bivalent inhibitors. We used the structural details of the subunit-subunit interactions within the HP0525 hexamer to design peptide recognition moieties of the subunit interface. Different methods (cross metathesis, click chemistry, and cysteine-malemide) for bioconjugation to selected 8-amino imidazo[1,2-a]pyrazines were explored, as well as peptides spanning larger or smaller regions of the interface. The IC50 values of the resulting linker-8-amino imidazo[1,2-a]pyrazine derivatives, and the bivalent inhibitors, were related to docking studies with the HP0525 crystal structure and to molecular dynamics simulations of the peptide recognition moieties.

In silico binding site location and pose prediction for a molecule targeted at a large protein surface is a challenging task. We report a blind test with two peptidomimetic molecules that bind the flu virus hemagglutinin (HA) surface antigen, JNJ7918 and JNJ4796 (recently disclosed in van Dongen et al., Science, 2019, 363). Tests with a series of conventional approaches such as rigid (receptor) docking against available X-ray crystal structures or against an ensemble of structures generated by quick methodologies (NMA, homology modeling) gave mixed results, due to the shallowness and flexibility of the binding site and the sheer size of the target. However, tests with our Monte Carlo platform PELE in two protocols involving either exploration of the whole protein surface (global exploration), or the latter followed by refinement of best solutions (local exploration) yielded remarkably good results by locating the actual binding site and generating binding modes that recovered all native contacts found in the X-ray structures. Thus, the Monte Carlo scheme of PELE seems promising as a quick methodology to overcome the challenge of identifying entirely unknown binding sites and modes for protein–protein disruptors., Peer Reviewed, Postprint (published version)

Proteinmethylierung spielt eine immer größere Rolle in der Regulierung zellulärer Prozesse. Die Entwicklung effizienter proteomweiter Methoden zur Detektion von Methylierung auf Proteinen ist limitiert und technisch schwierig. In dieser Arbeit haben wir einen neuen Hefe-Zwei-Hybrid-Ansatz (Y2H) entwickelt, der Proteine, die miteinander wechselwirken, mit Hilfe von Sequenzierungen der zweiten Generation identifiziert (Y2H-Seq). Der neue Y2H-Seq-Ansatz wurde systematisch mit dem Y2H-Seq-Ansatz verglichen. Dafür wurde ein Bait-Set von 8 Protein-Arginin-Methyltransferasen, 17 Protein-Lysin-Methyltransferasen und 10 Demethylasen gegen 14,268 Prey-Proteine getestet. Der Y2H-Seq-Ansatz ist weniger arbeitsintensiv, hat eine höhere Sensitivität als der Standard Y2H-Matrix-Ansatz und ist deshalb besonders geeignet, um schwache Interaktionen zwischen Substraten und Protein-Methyltransferasen zu detektieren. Insgesamt wurden 523 Wechselwirkungen zwischen 22 Bait-Proteinen und 324 Prey-Pr oteinen etabliert, darunter 11 bekannte Methyltransferasen-Substrate. Netzwerkanalysen zeigen, dass Methyltransferasen bevorzugt mit Transkriptionsregulatoren, DNA- und RNA-Bindeproteinen wechselwirken. Diese Daten repräsentieren das erste proteomweite Wechselwirkungsnetzwerk über Protein-Methyltransferasen und dienen als Ressource für neue potentielle Methylierungssubstrate. In einem in vitro Methylierungsassay wurden exemplarisch mit Hilfe massenspektrometrischer Analysen die methylierten Aminosäurereste einiger Kandidatenproteine bestimmt. Von neun getesteten Proteinen waren sieben methyliert, zu denen gehören SPIN2B, DNAJA3, QKI, SAMD3, OFCC1, SYNCRIP und WDR42A. Wahrscheinlich sind viele Methylierungssubstrate im Netzwerk vorhanden. Das vorgestellte Protein-Protein-Wechselwirkungsnetzwerk zeigt, dass Proteinmethylierung sehr unterschiedliche zelluläre Prozesse beeinflusst und ermöglicht die Aufstellung neuer Hypothesen über die Regulierung Molekularer Mechanismen durch Methylierung., Protein methylation on arginine and lysine residues is a largely unexplored posttranslational modification which regulates diverse cellular processes. The development of efficient proteome-wide approaches for detecting protein methylation is limited and technically challenging. We developed a novel workload reduced yeast-two hybrid (Y2H) approach to detect protein-protein interactions utilizing second generation sequencing. The novel Y2H-seq approach was systematically evaluated against our state of the art Y2H-matrix screening approach and used to screen 8 protein arginine methyltransferases, 17 protein lysine methyltransferases and 10 demethylases against a set of 14,268 proteins. Comparison of the two approaches revealed a higher sensitivity of the new Y2H-seq approach. The increased sampling rate of the Y2H-seq approach is advantageous when assaying transient interactions between substrates and methyltransferases. Overall 523 interactions between 22 bait proteins and 324 prey proteins were identified including 11 proteins known to be methylated. Network analysis revealed enrichment of transcription regulator activity, DNA- and RNA-binding function of proteins interacting with protein methyltransferases. The dataset represents the first proteome-wide interaction network of enzymes involved in methylation and provides a comprehensively annotated resource of potential new methylation substrates. An in vitro methylation assay coupled to mass spectrometry revealed amino acid methylation of candidate proteins. Seven of nine proteins tested were methylated including SPIN2B, DNAJA3, QKI, SAMD3, OFCC1, SYNCRIP and WDR42A indicating that the interaction network is likely to contain many putative methyltransferase substrate pairs. The presented protein-protein interaction network demonstrates that protein methylation is involved in diverse cellular processes and can inform hypothesis driven investigation into molecular mechanisms regulated through methylation.

Proteinmethylierung spielt eine immer größere Rolle in der Regulierung zellulärer Prozesse. Die Entwicklung effizienter proteomweiter Methoden zur Detektion von Methylierung auf Proteinen ist limitiert und technisch schwierig. In dieser Arbeit haben wir einen neuen Hefe-Zwei-Hybrid-Ansatz (Y2H) entwickelt, der Proteine, die miteinander wechselwirken, mit Hilfe von Sequenzierungen der zweiten Generation identifiziert (Y2H-Seq). Der neue Y2H-Seq-Ansatz wurde systematisch mit dem Y2H-Seq-Ansatz verglichen. Dafür wurde ein Bait-Set von 8 Protein-Arginin-Methyltransferasen, 17 Protein-Lysin-Methyltransferasen und 10 Demethylasen gegen 14,268 Prey-Proteine getestet. Der Y2H-Seq-Ansatz ist weniger arbeitsintensiv, hat eine höhere Sensitivität als der Standard Y2H-Matrix-Ansatz und ist deshalb besonders geeignet, um schwache Interaktionen zwischen Substraten und Protein-Methyltransferasen zu detektieren. Insgesamt wurden 523 Wechselwirkungen zwischen 22 Bait-Proteinen und 324 Prey-Pr oteinen etabliert, darunter 11 bekannte Methyltransferasen-Substrate. Netzwerkanalysen zeigen, dass Methyltransferasen bevorzugt mit Transkriptionsregulatoren, DNA- und RNA-Bindeproteinen wechselwirken. Diese Daten repräsentieren das erste proteomweite Wechselwirkungsnetzwerk über Protein-Methyltransferasen und dienen als Ressource für neue potentielle Methylierungssubstrate. In einem in vitro Methylierungsassay wurden exemplarisch mit Hilfe massenspektrometrischer Analysen die methylierten Aminosäurereste einiger Kandidatenproteine bestimmt. Von neun getesteten Proteinen waren sieben methyliert, zu denen gehören SPIN2B, DNAJA3, QKI, SAMD3, OFCC1, SYNCRIP und WDR42A. Wahrscheinlich sind viele Methylierungssubstrate im Netzwerk vorhanden. Das vorgestellte Protein-Protein-Wechselwirkungsnetzwerk zeigt, dass Proteinmethylierung sehr unterschiedliche zelluläre Prozesse beeinflusst und ermöglicht die Aufstellung neuer Hypothesen über die Regulierung Molekularer Mechanismen durch Methylierung., Protein methylation on arginine and lysine residues is a largely unexplored posttranslational modification which regulates diverse cellular processes. The development of efficient proteome-wide approaches for detecting protein methylation is limited and technically challenging. We developed a novel workload reduced yeast-two hybrid (Y2H) approach to detect protein-protein interactions utilizing second generation sequencing. The novel Y2H-seq approach was systematically evaluated against our state of the art Y2H-matrix screening approach and used to screen 8 protein arginine methyltransferases, 17 protein lysine methyltransferases and 10 demethylases against a set of 14,268 proteins. Comparison of the two approaches revealed a higher sensitivity of the new Y2H-seq approach. The increased sampling rate of the Y2H-seq approach is advantageous when assaying transient interactions between substrates and methyltransferases. Overall 523 interactions between 22 bait proteins and 324 prey proteins were identified including 11 proteins known to be methylated. Network analysis revealed enrichment of transcription regulator activity, DNA- and RNA-binding function of proteins interacting with protein methyltransferases. The dataset represents the first proteome-wide interaction network of enzymes involved in methylation and provides a comprehensively annotated resource of potential new methylation substrates. An in vitro methylation assay coupled to mass spectrometry revealed amino acid methylation of candidate proteins. Seven of nine proteins tested were methylated including SPIN2B, DNAJA3, QKI, SAMD3, OFCC1, SYNCRIP and WDR42A indicating that the interaction network is likely to contain many putative methyltransferase substrate pairs. The presented protein-protein interaction network demonstrates that protein methylation is involved in diverse cellular processes and can inform hypothesis driven investigation into molecular mechanisms regulated through methylation.