Structure of 2-keto-3-deoxy-D-manno-octulosonate-8-phosphate synthase from Pseudomonas aeruginosa

Identification of potential novel therapeutic drug target against Elizabethkingia anophelis by integrative pan and subtractive genomic analysis: An in silico approach

Elizabethkingia anophelis is a human pathogen responsible for severe nosocomial infections in neonates and immunocompromised patients. The significantly higher mortality rate from E. anophelis infections and the lack of available regimens highlight the critical need to explore novel drug targets. The current study investigated effective novel drug targets by employing a comprehensive in silico subtractive genomic approach integrated with pangenomic analysis of E. anophelis strains. A total of 2809 core genomic proteins were found by pangenomic analysis of non-paralogous proteins. Subsequently, 156 pathogen-specific, 442 choke point, 202 virulence factor, 53 antibiotic resistant and 119 host-pathogen interacting proteins were identified in E. anophelis. By subtractive genomic approach, at first 791 proteins were found to be indispensable for the survival of E. anophelis. 558 and 315 proteins were detected as non-homologous to human and gut microflora respectively. Following that 245 cytoplasmic, 245 novel, and 23 broad-spectrum targets were selected and finally four proteins were considered as potential therapeutic targets of E. anophelis based on highest degree score in PPI network. Among those, three proteins were subjected to molecular docking and subsequent MD simulation as one protein did not contain a plausible binding pocket with sufficient surface area and volume. All the complexes were found to be stable and compact in 100 ns molecular dynamics simulation studies as measured by RMSD, RMSF, and Rg. These three short-listed targets identified in this study may lead to the development of novel antimicrobials capable of curing infections and pave the way to prevent and control the disease progression caused by the deadly agent E. anophelis.

Using Structure-guided Fragment-Based Drug Discovery to Target Pseudomonas aeruginosa Infections in Cystic Fibrosis

Cystic fibrosis (CF) is progressive genetic disease that predisposes lungs and other organs to multiple long-lasting microbial infections. Pseudomonas aeruginosa is the most prevalent and deadly pathogen among these microbes. Lung function of CF patients worsens following chronic infections with P. aeruginosa and is associated with increased mortality and morbidity. Emergence of multidrug-resistant, extensively drug-resistant and pandrug-resistant strains of P. aeruginosa due to intrinsic and adaptive antibiotic resistance mechanisms has failed the current anti-pseudomonal antibiotics. Hence new antibacterials are urgently needed to treat P. aeruginosa infections. Structure-guided fragment-based drug discovery (FBDD) is a powerful approach in the field of drug development that has succeeded in delivering six FDA approved drugs over the past 20 years targeting a variety of biological molecules. However, FBDD has not been widely used in the development of anti-pseudomonal molecules. In this review, we first give a brief overview of our structure-guided FBDD pipeline and then give a detailed account of FBDD campaigns to combat P. aeruginosa infections by developing small molecules having either bactericidal or anti-virulence properties. We conclude with a brief overview of the FBDD efforts in our lab at the University of Cambridge towards targeting P. aeruginosa infections.

A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models

Pseudomonas aeruginosa is an opportunistic human pathogen that has been a constant global health problem due to its ability to cause infection at different body sites and its resistance to a broad spectrum of clinically available antibiotics. The World Health Organization classified multidrug-resistant Pseudomonas aeruginosa among the top-ranked organisms that require urgent research and development of effective therapeutic options. Several approaches have been taken to achieve these goals, but they all depend on discovering potential drug targets. The large amount of data obtained from sequencing technologies has been used to create computational models of organisms, which provide a powerful tool for better understanding their biological behavior. In the present work, we applied a method to integrate transcriptome data with genome-scale metabolic networks of Pseudomonas aeruginosa. We submitted both metabolic and integrated models to dynamic simulations and compared their performance with published in vitro growth curves. In addition, we used these models to identify potential therapeutic targets and compared the results to analyze the assumption that computational models enriched with biological measurements can provide more selective and (or) specific predictions. Our results demonstrate that dynamic simulations from integrated models result in more accurate growth curves and flux distribution more coherent with biological observations. Moreover, identifying drug targets from integrated models is more selective as the predicted genes were a subset of those found in the metabolic models. Our analysis resulted in the identification of 26 non-host homologous targets. Among them, we highlighted five top-ranked genes based on lesser conservation with the human microbiome. Overall, some of the genes identified in this work have already been proposed by different approaches and (or) are already investigated as targets to antimicrobial compounds, reinforcing the benefit of using integrated models as a starting point to selecting biologically relevant therapeutic targets.

Comparative subtractive proteomics based ranking for antibiotic targets against the dirtiest superbug: Acinetobacter baumannii

Multidrug-resistant Acinetobacter baumannii is indeed to be the most successful nosocomial pathogen responsible for myriad infections in modern health care system. Computational methodologies based on genomics and proteomics proved to be powerful tools for providing substantial information about different aspects of A. baumannii biology that made it possible to design new approaches for treating multi, extensive and total drug resistant isolates of A. baumannii. In this current approach, 35 completely annotated proteomes of A. bauamnnii were filtered through a comprehensive subtractive proteomics pipeline for broad-spectrum drug candidates. In total, 10 proteins (KdsA, KdsB, LpxA, LpxC, LpxD, GpsE, PhoB, UvrY, KdpE and OmpR) could serve as ideal candidates for designing novel antibiotics. The work was extended with KdsA enzyme for structure information, prediction of intrinsic disorders, active site details, and structure based virtual screening of library containing natural product-like scaffolds. Most of the enzyme structure has fixed three-dimensional conformation. The selection of inhibitor for KdsA enzyme was based on druglikeness, pharmacokinetics and docking scores. Compound-4636 (5-((3-chloro-5-methyl-2-phenyl-2,3-dihydro-1H-pyrazol-4-yl)methoxy)-2-(((1-hydroxy-4-methylpentan-2-yl)amino)methyl)phenol) was revealed as the most potent inhibitor against A. baumannii KdsA enzyme having Gold fitness score of 77.68 and Autodock binding energy of -6.2 kcal/mol. The inhibitor completely follows Lipinski rule of five, Ghose rule, and Egan rule. Molecular dynamics simulation for KdsA and KdsA-4636 complex was performed for 100 ns to unveil what conformational changes the enzyme underwent in the absence and presence of the inhibitor, respectively. The average root means square deviation (RMSD) for both systems was found 3.5 Å, which signifies stable structure of the enzyme in both bounded and unbounded states. Absolute binding energy using Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) reflected high affinity and vigorous interactions of the inhibitor with enzyme active residues. Findings of the current study could open up new avenues for experimentalists to design new potent antibiotics by targeting the targets screened in this study.

Identification of novel scaffolds for potential anti-Helicobacter pylori agents based on the crystal structure of H. pylori 3-deoxy-d-manno-octulosonate 8-phosphate synthase (HpKDO8PS)

The crystal structure of 3-deoxy-d-manno-octulosonate-8-phosphate synthase (KDO8PS) from Helicobacter pylori (HpKDO8PS) was determined alone and within various complexes, revealing an extra helix (HE) that is absent in the structures of KDO8PS from other organisms. In contrast to the metal coordination of the KDO8PS enzyme from Aquifex aeolicus, HpKDO8PS is specifically coordinated with Cd(2+) or Zn(2+) ions, and isothermal titration calorimetry (ITC) and differential scanning fluorimetry (DSF) revealed that Cd(2+) thermally stabilizes the protein structure more efficiently than Zn(2+). In the substrate-bound structure, water molecules play a key role in fixing residues in the proper configuration to achieve a compact structure. Using the structures of HpKDO8PS and API [arabinose 5-phosphate (A5P) and phosphoenolpyruvate (PEP) bisubstrate inhibitor], we generated 21 compounds showing potential HpKDO8PS-binding properties via in silico virtual screening. The capacity of three, avicularin, hyperin, and MC181, to bind to HpKDO8PS was confirmed through saturation transfer difference (STD) experiments, and we identified their specific ligand binding modes by combining competition experiments and docking simulation analysis. Hyperin was confirmed to bind to the A5P binding site, primarily via hydrophilic interaction, whereas MC181 bound to both the PEP and A5P binding sites through hydrophilic and hydrophobic interactions. These results were consistent with the epitope mapping by STD. Our results are expected to provide clues for the development of HpKDO8PS inhibitors.

Heterologous expression, purification, crystallization and preliminary X-ray diffraction analysis of KDO8P synthase from Arabidopsis thaliana

3-Deoxy-d-manno-octulosonate 8-phosphate synthase (KDO8PS) [EC 4.1.2.16] is the first and rate-limiting enzyme in the 3-deoxy-d-manno-octulosonate (KDO) biosynthetic pathway. The enzyme is widely expressed in bacteria and plants. Their well conserved protein sequences imply a similar oligomeric arrangement. However, the reported size exclusion chromatrographic analysis suggested a species-dependent self-assembling. To clarify the discrepancy and explore the self-assembling property of KDO8PS, we expressed and purified the Arabidopsis enzyme in Escherichia coli system. The enzyme was highly purified using a two-step purification strategy including nickel affinity and size exclusion chromatography with an expected pH activity profile. The identity of the purified enzyme was confirmed by Western-blot and mass fingerprints. Further analysis by analytical ultracentrifugation indicated that both bacteria and Arabidopsis enzymes are homotetramer. Furthermore, the purified enzyme from the plant has been crystallized and a complete set of X-ray data to 2.1Å resolution has been collected.