scholarly journals Discovery of the Novel Inhibitor Against New Delhi Metallo-β-Lactamase Based on Virtual Screening and Molecular Modelling

2020 ◽  
Vol 21 (10) ◽  
pp. 3567 ◽  
Author(s):  
Xiyan Wang ◽  
Yanan Yang ◽  
Yawen Gao ◽  
Xiaodi Niu
Keyword(s):  
Virtual Screening ◽  
Bacterial Resistance ◽  
Competitive Inhibition ◽  
Binding Free Energy ◽  
Binding Mode ◽  
Hydrolytic Activity ◽  
Dynamics Simulation ◽  
Energy Calculation ◽  
New Delhi ◽  
The Novel

New Delhi metallo-β-lactamase (NDM-1), one of the metallo-β-lactamases (MBLs), leads to antibiotic resistance in clinical treatments due to the strong ability of hydrolysis to almost all kinds of β-lactam antibiotics. Therefore, there is the urgent need for the research and development of the novel drug-resistant inhibitors targeting NDM-1. In this study, ZINC05683641 was screened as potential NDM-1 inhibitor by virtual screening and the inhibitor mechanism of this compound was explored based on molecular dynamics simulation. The nitrocefin assay showed that the IC50 value of ZINC05683641 was 13.59 ± 0.52 μM, indicating that the hydrolytic activity of NDM-1 can be obviously suppressed by ZINC05683641. Further, the binding mode of ZINC05683641 with NDM-1 was obtained by molecular modeling, binding free energy calculation, mutagenesis assays and fluorescence-quenching assays. As results, ILE-35, MET-67, VAL-73, TRP-93, CYS-208, ASN-220 and HIS-250 played the key roles in the binding of NDM-1 with ZINC05683641. Interestingly, these key residues were exactly located in the catalytic activity region of NDM-1, implying that the inhibitor mechanism of ZINC05683641 against NDM-1 was the competitive inhibition. These findings will provide an available approach to research and develop new drug against NDM-1 and treatment for bacterial resistance.

scholarly journals Structure-Based Virtual Screening of Tumor Necrosis Factor-α Inhibitors by Cheminformatics Approaches and Bio-Molecular Simulation

Biomolecules ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 329
Author(s):  
Sobia Ahsan Halim ◽  
Almas Gul Sikandari ◽  
Ajmal Khan ◽  
Abdul Wadood ◽  
Muhammad Qaiser Fatmi ◽  
...  
Keyword(s):  
Tumor Necrosis Factor ◽  
Virtual Screening ◽  
Tumor Necrosis ◽  
Tumor Necrosis Factor Α ◽  
Binding Free Energy ◽  
Dynamics Simulation ◽  
Energy Calculation ◽  
Tnf Α ◽  
Factor Α ◽  
Necrosis Factor

Tumor necrosis factor-α (TNF-α) is a drug target in rheumatoid arthritis and several other auto-immune disorders. TNF-α binds with TNF receptors (TNFR), located on the surface of several immunological cells to exert its effect. Hence, the use of inhibitors that can hinder the complex formation of TNF-α/TNFR can be of medicinal significance. In this study, multiple chem-informatics approaches, including descriptor-based screening, 2D-similarity searching, and pharmacophore modelling were applied to screen new TNF-α inhibitors. Subsequently, multiple-docking protocols were used, and four-fold post-docking results were analyzed by consensus approach. After structure-based virtual screening, seventeen compounds were mutually ranked in top-ranked position by all the docking programs. Those identified hits target TNF-α dimer and effectively block TNF-α/TNFR interface. The predicted pharmacokinetics and physiological properties of the selected hits revealed that, out of seventeen, seven compounds (4, 5, 10, 11, 13–15) possessed excellent ADMET profile. These seven compounds plus three more molecules (7, 8 and 9) were chosen for molecular dynamics simulation studies to probe into ligand-induced structural and dynamic behavior of TNF-α, followed by ligand-TNF-α binding free energy calculation using MM-PBSA. The MM-PBSA calculations revealed that compounds 4, 5, 7 and 9 possess highest affinity for TNF-α; 8, 11, 13–15 exhibited moderate affinities, while compound 10 showed weaker binding affinity with TNF-α. This study provides valuable insights to design more potent and selective inhibitors of TNF-α, that will help to treat inflammatory disorders.

Application of the ESMACS Binding Free Energy Protocol to a Highly Varied Ligand Dataset: Lactate Dehydogenase A

2019 ◽  
Author(s):  
David Wright ◽  
Fouad Husseini ◽  
Shunzhou Wan ◽  
Christophe Meyer ◽  
Herman Van Vlijmen ◽  
...  
Keyword(s):  
Free Energy ◽  
Surface Area ◽  
Binding Free Energy ◽  
Normal Mode Analysis ◽  
Binding Mode ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
Mode Analysis ◽  
Energy Calculation ◽  
Accessible Surface

<div>Here, we evaluate the performance of our range of ensemble simulation based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) for use in fragment based drug design scenarios. ESMACS is designed to generate reproducible binding affinity predictions from the widely used molecular mechanics Poisson-Boltzmann surface area (MMPBSA) approach. We study ligands designed to target two binding pockets in the lactate dehydogenase A target protein, which vary in size, charge and binding mode. When comparing to experimental results, we obtain excellent statistical rankings across this highly diverse set of ligands. In addition, we investigate three approaches to account for entropic contributions not captured by standard MMPBSA calculations: (1) normal mode analysis, (2) weighted solvent accessible surface area (WSAS) and (3) variational entropy. </div>

Machine Learning-based Virtual Screening Strategy Reveals Some Natural Compounds As Potential PAK4 Inhibitors In Triple Negative Breast Cancer

2020 ◽  
Vol 18 ◽  
Author(s):  
Opeyemi Iwaloye ◽  
Olusola Olalekan Elekofehinti ◽  
Babatomiwa Kikiowo ◽  
Emmanuel Ayo Oluwarotimi ◽  
Toyin Mary Fadipe
Keyword(s):  
Breast Cancer ◽  
Virtual Screening ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Binding Free Energy ◽  
Natural Compounds ◽  
Energy Calculation ◽  
Reference Drug ◽  
Predictive Quality

Background: P-21 activating kinase 4 (PAK4) is implicated in poor prognosis of many cancers, especially in the progression of Triple Negative Breast Cancer (TNBC). The present study was aimed at designing some potential drug candidates as PAK4 inhibitors for breast cancer therapy. Objective: This study aimed to finding novel inhibitors of PAK4 from natural compounds using computational approach. Methods: An e-pharmacophore model was developed from docked PAK4-coligand complex and used to screen over a thousand natural compounds downloaded from BIOFACQUIM and NPASS databases to match a minimum of 5 sites for selected (ADDDHRR) hypothesis. The robustness of the virtual screening method was accessed by well-established methods including EF, ROC, BEDROC, AUAC, and the RIE. Compounds with fitness score greater than one were filtered by applying molecular docking (HTVS, SP, XP and Induced fit docking) and ADME prediction. Using Machine learningbased approach QSAR model was generated using Automated QSAR. The computed top model kpls_des_17 (R2= 0.8028, RMSE = 0.4884 and Q2 = 0.7661) was used to predict the pIC50 of the lead compounds. Internal and external validations were accessed to determine the predictive quality of the model. Finally the binding free energy calculation was computed. Results: The robustness/predictive quality of the models were affirmed. The hits had better binding affinity than the reference drug and interacted with key amino acids for PAK4 inhibition. Overall, the present analysis yielded three potential inhibitors that are predicted to bind with PAK4 better than reference drug tamoxifen. The three potent novel inhibitors vitexin, emodin and ziganein recorded IFD score of -621.97 kcal/mol, -616.31 kcal/mol and -614.95 kcal/mol, respectively while showing moderation for ADME properties and inhibition constant. Conclusion: It is expected that the findings reported in this study may provide insight for designing effective and less toxic PAK4 inhibitors for triple negative breast cancer.

scholarly journals Scaffold Hopping of α-Rubromycin Enables Direct Access to FDA-Approved Cromoglicic Acid as a SARS-CoV-2 MPro Inhibitor

2021 ◽  
Vol 14 (6) ◽  
pp. 541
Author(s):  
Hani A. Alhadrami ◽  
Ahmed M. Sayed ◽  
Heba Al-Khatabi ◽  
Nabil A. Alhakamy ◽  
Mostafa E. Rateb
Keyword(s):  
Binding Free Energy ◽  
Human Fibroblasts ◽  
Critical Time ◽  
Dynamics Simulation ◽  
Direct Access ◽  
Energy Calculation ◽  
Wide Range ◽  
Normal Human ◽  
Novel Scaffold

The COVID-19 pandemic is still active around the globe despite the newly introduced vaccines. Hence, finding effective medications or repurposing available ones could offer great help during this serious situation. During our anti-COVID-19 investigation of microbial natural products (MNPs), we came across α-rubromycin, an antibiotic derived from Streptomyces collinus ATCC19743, which was able to suppress the catalytic activity (IC50 = 5.4 µM and Ki = 3.22 µM) of one of the viral key enzymes (i.e., MPro). However, it showed high cytotoxicity toward normal human fibroblasts (CC50 = 16.7 µM). To reduce the cytotoxicity of this microbial metabolite, we utilized a number of in silico tools (ensemble docking, molecular dynamics simulation, binding free energy calculation) to propose a novel scaffold having the main pharmacophoric features to inhibit MPro with better drug-like properties and reduced/minimal toxicity. Nevertheless, reaching this novel scaffold synthetically is a time-consuming process, particularly at this critical time. Instead, this scaffold was used as a template to explore similar molecules among the FDA-approved medications that share its main pharmacophoric features with the aid of pharmacophore-based virtual screening software. As a result, cromoglicic acid (aka cromolyn) was found to be the best hit, which, upon in vitro MPro testing, was 4.5 times more potent (IC50 = 1.1 µM and Ki = 0.68 µM) than α-rubromycin, with minimal cytotoxicity toward normal human fibroblasts (CC50 > 100 µM). This report highlights the potential of MNPs in providing unprecedented scaffolds with a wide range of therapeutic efficacy. It also revealed the importance of cheminformatics tools in speeding up the drug discovery process, which is extremely important in such a critical situation.

scholarly journals Discovery of Potential Chemical Probe as Inhibitors of CXCL12 Using Ligand-Based Virtual Screening and Molecular Dynamic Simulation

Molecules ◽  
2020 ◽  
Vol 25 (20) ◽  
pp. 4829
Author(s):  
Sajjad Haider ◽  
Assem Barakat ◽  
Zaheer Ul-Haq
Keyword(s):  
Virtual Screening ◽  
Molecular Dynamic ◽  
Root Mean Square ◽  
Cancer Metastasis ◽  
Pharmacophore Model ◽  
Binding Mode ◽  
Dynamics Simulation ◽  
Radius Of Gyration ◽  
Mean Square ◽  
Dynamic Simulations

CXCL12 are small pro-inflammatory chemo-attractant cytokines that bind to a specific receptor CXCR4 with a role in angiogenesis, tumor progression, metastasis, and cell survival. Globally, cancer metastasis is a major cause of morbidity and mortality. In this study, we targeted CXCL12 rather than the chemokine receptor (CXCR4) because most of the drugs failed in clinical trials due to unmanageable toxicities. Until now, no FDA approved medication has been available against CXCL12. Therefore, we aimed to find new inhibitors for CXCL12 through virtual screening followed by molecular dynamics simulation. For virtual screening, active compounds against CXCL12 were taken as potent inhibitors and utilized in the generation of a pharmacophore model, followed by validation against different datasets. Ligand based virtual screening was performed on the ChEMBL and in-house databases, which resulted in successive elimination through the steps of pharmacophore-based and score-based screenings, and finally, sixteen compounds of various interactions with significant crucial amino acid residues were selected as virtual hits. Furthermore, the binding mode of these compounds were refined through molecular dynamic simulations. Moreover, the stability of protein complexes, Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), and radius of gyration were analyzed, which led to the identification of three potent inhibitors of CXCL12 that may be pursued in the drug discovery process against cancer metastasis.

Virtual Screening and Molecular Dynamics Simulation Suggest Valproic Acid Co-A could Bind to SARS-CoV2 RNA Depended RNA Polymerase

2020 ◽  
Author(s):  
Neel Sarovar Bhavesh ◽  
Anupam Patra
Keyword(s):  
Valproic Acid ◽  
Virtual Screening ◽  
Rna Polymerase ◽  
Specific Inhibitor ◽  
Viral Rna ◽  
Dynamics Simulation ◽  
Energy Calculation ◽  
Good Target ◽  
Binding Energy Calculation ◽  
Essential Enzyme

SARS-CoV2 RNA depended RNA polymerase is an essential enzyme for the survival of the virus in hosts as it helps in the replication of viral RNA. There are no human polymerases that share either sequence or structural homology with viral RNA depended RNA polymerase. These make it a good target for inhibitor discovery, as a specific inhibitor cannot cross-react with the human polymerases. We have used virtual screening, docking, binding energy calculation and simulation to show that valproic acid Co-A, a metabolite from prodrug valproic acid, forms stable interaction with nsP12 of CoV. Our results suggest valproic acid Co-A could be a potential inhibitor of nsP12 of SARS-CoV2.

scholarly journals Identification of potential carboxylic acid-containing drug candidate to design novel competitive NDM inhibitors: An in-silico approach comprising combined virtual screening and molecular dynamics simulation

2021 ◽  
Author(s):  
Anand Anbarasu ◽  
Balaji Veeraraghavan ◽  
Karthick Vasudevan ◽  
Soumya Basu ◽  
Amala Arumugam ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Carboxylic Acid ◽  
In Silico ◽  
Hydrolytic Activity ◽  
Dynamics Simulation ◽  
Drug Candidate ◽  
New Delhi ◽  
Beta Lactamase ◽  
Simulation Studies

Metallo-β-lactamases (MBLs) producing bacteria especially the ones with New Delhi metallo-beta-lactamase-1 (NDM-1) and its variants can potentially hydrolyse all the major β-lactam antibiotics, ultimately escalating anti-microbial resistance world-wide. There is a dearth of approved inhibitors to combat NDM and other MBLs producing bacteria. Hence we focussed to find novel inhibitor(s) in-silico which can potentially suppress the activity of NDM/ MBLs. 2400 compounds were virtually screened to identify a promising carboxylic acid-containing compound (CID-53986787) analogous to NDM antagonist Captopril. Our lead compound can bind adjacent to the active site zinc ions (Zn1 and Zn2) in all highly resistant NDM variants. CID-53986787 possesses ~5-8% higher binding affinity than Captopril, exhibiting molecular interactions with crucial residues that can destabilize the hydrolytic activity of NDM. CID-53986787 was virtually evaluated to ascertain its safe pharmacological/ toxicity profile. Molecular dynamics simulation studies elucidated its stable interaction with the target protein (NDM-1).

scholarly journals Hydrogen Bond Stability of Quinazoline Derivatives Compounds in Complex against EGFR using Molecular Dynamics Simulation

2019 ◽  
Vol 19 (2) ◽  
pp. 461
Author(s):  
Herlina Rasyid ◽  
Bambang Purwono ◽  
Thomas S Hofer ◽  
Harno Dwi Pranowo
Keyword(s):  
Molecular Dynamics ◽  
Hydrogen Bond ◽  
Free Energy ◽  
Molecular Dynamics Simulation ◽  
Binding Free Energy ◽  
Dynamics Simulation ◽  
Inhibition Mechanism ◽  
Energy Calculation ◽  
Protein Receptor ◽  
The Stability

Lung cancer was a second common cancer case due to the high cigarette smoking activity both in men and women. One of protein receptor which plays an important role in the growth of the tumor is Epidermal Growth Factor Receptor (EGFR). EGFR protein is the most frequent protein mutation in cancer and promising target to inhibit the cancer growth. In this work, the stability of the hydrogen bond as the main interaction in the inhibition mechanism of cancer will be evaluated using molecular dynamics simulation. There were two compounds (A1 and A2) as new potential inhibitors that were complexed against the EGFR protein. The dynamic properties of each complexed were compared with respect to erlotinib against EGFR. The result revealed that both compounds had an interaction in the main catalytic area of protein receptor which is at methionine residue. Inhibitor A1 showed additional interactions during simulation time but the interactions tend to be weak. Inhibitor A2 displayed a more stable interaction. Following dynamics simulation, binding free energy calculation was performed by two scoring techniques MM/GB(PB)SA method and gave a good correlation with the stability of the complex. Furthermore, potential inhibitor A2 had a lower binding free energy as a direct consequence of the stability of hydrogen bond interaction.

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