scholarly journals Molecular Insights into Binding Mode and Interactions of Structure-Based Virtually Screened Inhibitors for Pseudomonas aeruginosa Multiple Virulence Factor Regulator (MvfR)

Molecules ◽  
2021 ◽  
Vol 26 (22) ◽  
pp. 6811
Author(s):  
Raed A. H. Almihyawi ◽  
Halah M. H. Al-Hasani ◽  
Tabarak Sabah Jassim ◽  
Ziyad Tariq Muhseen ◽  
Sitong Zhang ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Virulence Factor ◽  
Experimental Studies ◽  
Binding Mode ◽  
Dynamics Simulation ◽  
Multi Drug Resistance ◽  
Binding Potential ◽  
Virulence Proteins ◽  
Long Run ◽  
Therapeutic Molecules

Multi-drug resistance (MDR) bacterial pathogens pose a threat to global health and warrant the discovery of new therapeutic molecules, particularly those that can neutralize their virulence and stop the evolution of new resistant mechanisms. The superbug nosocomial pathogen, Pseudomonas aeruginosa, uses a multiple virulence factor regulator (MvfR) to regulate the expression of multiple virulence proteins during acute and persistent infections. The present study targeted MvfR with the intention of designing novel anti-virulent compounds, which will function in two ways: first, they will block the virulence and pathogenesis P. aeruginosa by disrupting the quorum-sensing network of the bacteria, and second, they will stop the evolution of new resistant mechanisms. A structure-based virtual screening (SBVS) method was used to screen druglike compounds from the Asinex antibacterial library (~5968 molecules) and the comprehensive marine natural products database (CMNPD) (~32 thousand compounds), against the ligand-binding domain (LBD) of MvfR, to identify molecules that show high binding potential for the relevant pocket. In this way, two compounds were identified: Top-1 (4-((carbamoyloxy)methyl)-10,10-dihydroxy-2,6-diiminiodecahydropyrrolo[1,2-c]purin-9-yl sulfate) and Top-2 (10,10-dihydroxy-2,6-diiminio-4-(((sulfonatocarbamoyl)oxy)methyl)decahydropyrrolo[1,2-c]purin-9-yl sulfate), in contrast to the co-crystallized M64 control. Both of the screened leads were found to show deep pocket binding and interactions with several key residues through a network of hydrophobic and hydrophilic interactions. The docking results were validated by a long run of 200 ns of molecular dynamics simulation and MM-PB/GBSA binding free energies. All of these analyses confirmed the presence of strong complex formation and rigorous intermolecular interactions. An additional analysis of normal mode entropy and a WaterSwap assay were also performed to complement the aforementioned studies. Lastly, the compounds were found to show an acceptable range of pharmacokinetic properties, making both compounds potential candidates for further experimental studies to decipher their real biological potency.

scholarly journals Discovery of New Delhi Metallo-β-Lactamase-1(NDM-1) Inhibitors From Natural Compounds: In Silico-Based Methods

2020 ◽  
Author(s):  
Azhar Salari-jazi ◽  
Karim Mahnam ◽  
Parisa Sadeghi ◽  
Mohammad Sadegh Damavandi ◽  
Jamshid Faghri
Keyword(s):  
Binding Free Energy ◽  
Experimental Studies ◽  
Natural Compounds ◽  
Dynamics Simulation ◽  
Health Concern ◽  
Multi Drug Resistance ◽  
New Delhi ◽  
Public Health Concern ◽  
Different Types ◽  
Almost All

Abstract New Delhi metallo-β-lactamase variants and different types of metallo-β-lactamases have attracted enormous consideration for hydrolyzing almost all β-lactam antibiotics, which leads to multi drug resistance bacteria. Metallo-β-lactamases genes have disseminated in hospitals and all parts of the world and became a public health concern. There is no inhibitor for new Delhi metallo-β-lactamase-1 and other metallo-β-lactamases classes, so metallo-β-lactamases inhibitor drugs became an urgent need. In this study, multi-steps virtual screening was done over the NPASS database with 35032 natural compounds. At first Captopril was extracted from 4EXS PDB code and use as a template for the first structural screening and 500 compounds obtained as hit compounds by molecular docking. Then the best ligand, i.e. NPC120633 was used as templet and 800 similar compounds were obtained. As a final point, ten compounds i.e. NPC171932, NPC100251, NPC18185, NPC98583, NPC112380, NPC471403, NPC471404, NPC472454, NPC473010 and NPC300657 had proper docking scores, and a 50 ns molecular dynamics simulation was performed for calculation binding free energy of each compound with new Delhi metallo-β-lactamase. 3D conformational alignment and protein sequence alignment of all new Delhi metallo-β-lactamase variants and all types of metallo-β-lactamases were done. Then the conserved and crucial residues in the catalytic activity of metallo-β-lactamases were detected. These residues had similar 3D coordinates in the 3D conformational alignment. So it is possible that all types of metallo-β-lactamases can inhibit by these ten compounds. Therefore, these compounds were proper to mostly inhibit all new Delhi metallo-β-lactamase and metallo-β-lactamases in experimental studies.

scholarly journals Discovery of potential inhibitors against New Delhi metallo-β-lactamase-1 from natural compounds: in silico-based methods

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Azhar Salari-jazi ◽  
Karim Mahnam ◽  
Parisa Sadeghi ◽  
Mohamad Sadegh Damavandi ◽  
Jamshid Faghri
Keyword(s):  
Binding Free Energy ◽  
Experimental Studies ◽  
Spatial Location ◽  
Natural Compounds ◽  
Pharmacophore Modeling ◽  
Dynamics Simulation ◽  
Health Concern ◽  
Multi Drug Resistance ◽  
New Delhi ◽  
Almost All

AbstractNew Delhi metallo-β-lactamase variants and different types of metallo-β-lactamases have attracted enormous consideration for hydrolyzing almost all β-lactam antibiotics, which leads to multi drug resistance bacteria. Metallo-β-lactamases genes have disseminated in hospitals and all parts of the world and became a public health concern. There is no inhibitor for New Delhi metallo-β-lactamase-1 and other metallo-β-lactamases classes, so metallo-β-lactamases inhibitor drugs became an urgent need. In this study, multi-steps virtual screening was done over the NPASS database with 35,032 natural compounds. At first Captopril was extracted from 4EXS PDB code and use as a template for the first structural screening and 500 compounds obtained as hit compounds by molecular docking. Then the best ligand, i.e. NPC120633 was used as templet and 800 similar compounds were obtained. As a final point, ten compounds i.e. NPC171932, NPC100251, NPC18185, NPC98583, NPC112380, NPC471403, NPC471404, NPC472454, NPC473010 and NPC300657 had proper docking scores, and a 50 ns molecular dynamics simulation was performed for calculation binding free energy of each compound with New Delhi metallo-β-lactamase. Protein sequence alignment, 3D conformational alignment, pharmacophore modeling on all New Delhi metallo-β-lactamase variants and all types of metallo-β-lactamases were done. Quantum chemical perspective based on the fragment molecular orbital (FMO) method was performed to discover conserved and crucial residues in the catalytic activity of metallo-β-lactamases. These residues had similar 3D coordinates of spatial location in the 3D conformational alignment. So it is posibble that all types of metallo-β-lactamases can inhibit by these ten compounds. Therefore, these compounds were proper to mostly inhibit all metallo-β-lactamases in experimental studies.

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

2017 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes.</div><div><br></div><div>In this technique the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.</div>

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

2018 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes.</div><div><br></div><div>In this technique the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.</div>

scholarly journals Computational Investigations on the Binding Mode of Ligands for the Cannabinoid-Activated G Protein-Coupled Receptor GPR18

Biomolecules ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 686 ◽  
Author(s):  
Alexander Neumann ◽  
Viktor Engel ◽  
Andhika B. Mahardhika ◽  
Clara T. Schoeder ◽  
Vigneshwaran Namasivayam ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
G Protein ◽  
Phenyl Ring ◽  
Binding Mode ◽  
Dynamics Simulation ◽  
Simulation Studies ◽  
Binding Modes ◽  
G Protein Coupled Receptor ◽  
G Protein Coupled

GPR18 is an orphan G protein-coupled receptor (GPCR) expressed in cells of the immune system. It is activated by the cannabinoid receptor (CB) agonist ∆9-tetrahydrocannabinol (THC). Several further lipids have been proposed to act as GPR18 agonists, but these results still require unambiguous confirmation. In the present study, we constructed a homology model of the human GPR18 based on an ensemble of three GPCR crystal structures to investigate the binding modes of the agonist THC and the recently reported antagonists which feature an imidazothiazinone core to which a (substituted) phenyl ring is connected via a lipophilic linker. Docking and molecular dynamics simulation studies were performed. As a result, a hydrophobic binding pocket is predicted to accommodate the imidazothiazinone core, while the terminal phenyl ring projects towards an aromatic pocket. Hydrophobic interaction of Cys251 with substituents on the phenyl ring could explain the high potency of the most potent derivatives. Molecular dynamics simulation studies suggest that the binding of imidazothiazinone antagonists stabilizes transmembrane regions TM1, TM6 and TM7 of the receptor through a salt bridge between Asp118 and Lys133. The agonist THC is presumed to bind differently to GPR18 than to the distantly related CB receptors. This study provides insights into the binding mode of GPR18 agonists and antagonists which will facilitate future drug design for this promising potential drug target.

Assessment of elastase as a Pseudomonas aeruginosa virulence factor in experimental lung infection in mink

1987 ◽  
Vol 13 (3) ◽  
pp. 281-289 ◽  
Author(s):  
Laila Elsadig Elsheikh ◽  
Tony Kronevi ◽  
Bengt Wretlind ◽  
Saleem Abaas ◽  
Barbara H. Iglewski
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Virulence Factor ◽  
Lung Infection

scholarly journals Effects of Antibiotics on Quorum Sensing in Pseudomonas aeruginosa

2008 ◽  
Vol 52 (10) ◽  
pp. 3648-3663 ◽  
Author(s):  
Mette E. Skindersoe ◽  
Morten Alhede ◽  
Richard Phipps ◽  
Liang Yang ◽  
Peter O. Jensen ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Quorum Sensing ◽  
Virulence Factor ◽  
Dna Microarrays ◽  
Underlying Mechanism ◽  
Homoserine Lactone ◽  
Reverse Transcription Pcr ◽  
Animal Infection ◽  
Beneficial Action ◽  
Severe Infections

ABSTRACT During infection, Pseudomonas aeruginosa employs bacterial communication (quorum sensing [QS]) to coordinate the expression of tissue-damaging factors. QS-controlled gene expression plays a pivotal role in the virulence of P. aeruginosa, and QS-deficient mutants cause less severe infections in animal infection models. Treatment of cystic fibrosis (CF) patients chronically infected with P. aeruginosa with the macrolide antibiotic azithromycin (AZM) has been demonstrated to improve the clinical outcome. Several studies indicate that AZM may accomplish its beneficial action in CF patients by impeding QS, thereby reducing the pathogenicity of P. aeruginosa. This led us to investigate whether QS inhibition is a common feature of antibiotics. We present the results of a screening of 12 antibiotics for their QS-inhibitory activities using a previously described QS inhibitor selector 1 strain. Three of the antibiotics tested, AZM, ceftazidime (CFT), and ciprofloxacin (CPR), were very active in the assay and were further examined for their effects on QS-regulated virulence factor production in P. aeruginosa. The effects of the three antibiotics administered at subinhibitory concentrations were investigated by use of DNA microarrays. Consistent results from the virulence factor assays, reverse transcription-PCR, and the DNA microarrays support the finding that AZM, CFT, and CPR decrease the expression of a range of QS-regulated virulence factors. The data suggest that the underlying mechanism may be mediated by changes in membrane permeability, thereby influencing the flux of N-3-oxo-dodecanoyl-l-homoserine lactone.

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