An explorative study on <i>Staphylococcus aureus</i> MurE inhibitor: induced fit docking, binding free energy calculation, and molecular dynamics

Azam, Mohammed Afzal; Saha, Niladri; Jupudi, Srikanth

doi:10.1080/10799893.2019.1605528

Cited by 6 publications

(4 citation statements)

References 45 publications

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“…S. aureus is an important human pathogen and is among the leading causes of skin and soft tissue and device-related infections, as well as infective endocarditis. The S. aureus MurE enzyme is one of the potential targets for the development of new therapeutic agents due to its high substrate specificity and ubiquitous nature among bacteria ( Azam, Saha & Jupudi, 2019 ). The residues and binding types where both ligands bind to the active binding site of the S. aureus receptor are also shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Three hydroxyl groups and one oxygen atom in quercetin were connected to the residues (ASP207, ASN151, LYS114, and ARG187) at the active binding site via hydrogen bonds (2.03, 2.71, 2.12, 2.14 and 2.20 Å), as shown in Table S1 . In a study on the Staphylococcus aureus MurE inhibitor, it was observed that the molecule whose inhibitory activity was investigated provides this activity by making hydrogen bonds with the active binding site of the similar residues with ASN151, THR152, SER180, ARG187 AND LYS219 ( Azam, Saha & Jupudi, 2019 ). These hydrogen bonding interactions are considered important interactions for the stabilization of the inhibitor within the catalytic pocket of S. aureus MurE.…”

Section: Resultsmentioning

confidence: 99%

“…The 3D crystal structure of S. aureus MurE, which was obtained at a resolution of 1.9 and formed a single A-chain of 501 amino acids containing natural ligands, phosphate, potassium, chloride, and magnesium ions, was extracted from the protein database to perform the molecular docking analysis. Magnesium ions that were near the natural ligand were retained, while all water and ions that were not in the binding region of the natural ligand were deleted, as in the literature ( Azam, Saha & Jupudi, 2019 ). Polar hydrogens were added, binding orders were assigned, and preprocessing was performed.…”

Section: Methodsmentioning

confidence: 99%

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In vitroandin silicoevaluation of the design of nano-phyto-drug candidate for oral use againstStaphylococcus aureus

Budama‐Kilinc

Gök

Aluc

et al. 2023

PeerJ

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Onopordum acanthium is a medicinal plant with many important properties, such as antibacterial, anticancer, and anti-hypotensive properties. Although various studies reported the biological activities of O. acanthium, there is no study on its nano-phyto-drug formulation. The aim of this study is to develop a candidate nano-drug based on phytotherapeutic constituents and evaluate its efficiency in vitro and in silico. In this context, poly (lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) of O. acanthium extract (OAE) were synthesized and characterized. It was determined that the average particle size of OAE-PLGA-NPs was 214.9 ± 6.77 nm, and the zeta potential was −8.03 ± 0.85 mV, and PdI value was 0.064 ± 0.013. The encapsulation efficiency of OAE-PLGA-NPs was calculated as 91%, and the loading capacity as 75.83%. The in vitro drug release study showed that OAE was released from the PLGA NPs with 99.39% over the 6 days. Furthermore, the mutagenic and cytotoxic activity of free OAE and OAE-PLGA-NPs were evaluated by the Ames test and MTT test, respectively. Although 0.75 and 0.37 mg/mL free OAE concentrations caused both frameshift mutation and base pair substitution (p < 0.05), the administered OAE–PLGA NP concentrations were not mutagenic. It was determined with the MTT analysis that the doses of 0.75 and 1.5 mg/mL of free OAE had a cytotoxic effect on the L929 fibroblast cell line (p < 0.05), and OAE-PLGA-NPs had no cytotoxic effect. Moreover, the interaction between the OAE and S. aureus was also investigated using the molecular docking analysis method. The molecular docking and molecular dynamics (MD) results were implemented to elucidate the S. aureus MurE inhibition potential of OAE. It was shown that quercetin in the OAE content interacted significantly with the substantial residues in the catalytic pocket of the S. aureus MurE enzyme, and quercetin performed four hydrogen bond interactions corresponding to a low binding energy of −6.77 kcal/mol with catalytic pocket binding residues, which are crucial for the inhibition mechanism of S. aureus MurE. Finally, the bacterial inhibition values of free OAE and OAE–PLGA NPs were determined against S. aureus using a microdilution method. The antibacterial results showed that the inhibition value of the OAE–PLGA NPs was 69%. In conclusion, from the in vitro and in silico results of the nano-sized OAE-PLGA NP formulation produced in this study, it was evaluated that the formulation may be recommended as a safe and effective nano-phyto-drug candidate against S. aureus.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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In vitroandin silicoevaluation of the design of nano-phyto-drug candidate for oral use againstStaphylococcus aureus

Budama‐Kilinc

Gök

Aluc

et al. 2023

PeerJ

View full text Add to dashboard Cite

show abstract

“…For inhibition of the CYP450, as it is implied from the physics of the problem, the free energy of solvation of drug candidates in different solvents can determine the free energy of docking the drugs to the active site of the enzyme [64,65]. To that end, the most widely considered solvents are octanol and water, commonly employed as octanol-water partition coefficient [66][67][68][69][70].…”

Section: Analysis Of the Performance Of Studied Solvents And Representationsmentioning

confidence: 99%

Implicitly perturbed Hamiltonian: a class of versatile and general-purpose molecular representations for machine learning

Alibakhshi¹,

Hartke²

2021

Preprint

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Unraveling challenging problems by machine learning has recently become a hot topic in many scientific disciplines. For developing rigorous machine-learning models to study problems of interest in molecular sciences, translating molecular structures to quantitative representations as suitable machine-learning inputs play a central role. Many different molecular representations and the state-of-the-art ones, although efficient in studying numerous molecular features, still are suboptimal in many challenging cases, as discussed in the context of the present research. The main aim of the present study is to introduce the Implicitly Perturbed Hamiltonian (ImPerHam) as a class of versatile representations for more efficient machine learning of challenging problems in molecular sciences. ImPerHam representations are defined as energy attributes of the molecular Hamiltonian, implicitly perturbed by a number of hypothetic or real arbitrary solvents based on continuum solvation models. We demonstrate the outstanding performance of machine-learning models based on ImPerHam representations for three diverse and challenging cases of predicting inhibition of the CYP450 enzyme, high precision, and transferrable evaluation of conformational energy of molecular systems, and accurately reproducing solvation free energies for large benchmark sets.

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Implicitly perturbed Hamiltonian as a class of versatile and general-purpose molecular representations for machine learning

Alibakhshi

Hartke

2022

Nat Commun

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Unraveling challenging problems by machine learning has recently become a hot topic in many scientific disciplines. For developing rigorous machine-learning models to study problems of interest in molecular sciences, translating molecular structures to quantitative representations as suitable machine-learning inputs play a central role. Many different molecular representations and the state-of-the-art ones, although efficient in studying numerous molecular features, still are suboptimal in many challenging cases, as discussed in the context of the present research. The main aim of the present study is to introduce the Implicitly Perturbed Hamiltonian (ImPerHam) as a class of versatile representations for more efficient machine learning of challenging problems in molecular sciences. ImPerHam representations are defined as energy attributes of the molecular Hamiltonian, implicitly perturbed by a number of hypothetic or real arbitrary solvents based on continuum solvation models. We demonstrate the outstanding performance of machine-learning models based on ImPerHam representations for three diverse and challenging cases of predicting inhibition of the CYP450 enzyme, high precision, and transferrable evaluation of non-covalent interaction energy of molecular systems, and accurately reproducing solvation free energies for large benchmark sets.

show abstract

An explorative study on Staphylococcus aureus MurE inhibitor: induced fit docking, binding free energy calculation, and molecular dynamics

Cited by 6 publications

References 45 publications

In vitroandin silicoevaluation of the design of nano-phyto-drug candidate for oral use againstStaphylococcus aureus

In vitroandin silicoevaluation of the design of nano-phyto-drug candidate for oral use againstStaphylococcus aureus

Implicitly perturbed Hamiltonian: a class of versatile and general-purpose molecular representations for machine learning

Implicitly perturbed Hamiltonian as a class of versatile and general-purpose molecular representations for machine learning

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