High-throughput virtual screening, identification and in vitro biological evaluation of novel inhibitors of signal transducer and activator of transcription 3

2015 ◽  
Vol 24 (6) ◽  
pp. 2694-2708 ◽  
Author(s):  
Pushpendra Singh ◽  
Felix Bast
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
Biological Evaluation ◽  
Signal Transducer ◽  
Transcription 3 ◽  
High Throughput Virtual Screening

scholarly journals Potential Novel Thioether-Amide or Guanidine-Linker Class of SARS-CoV-2 Virus RNA-Dependent RNA Polymerase Inhibitors Identified by High-Throughput Virtual Screening Coupled to Free-Energy Calculations

2021 ◽  
Vol 22 (20) ◽  
pp. 11143
Author(s):  
Marko Jukič ◽  
Dušanka Janežič ◽  
Urban Bren
Keyword(s):  
Virtual Screening ◽  
Rna Polymerase ◽  
High Throughput ◽  
Biological Evaluation ◽  
Free Energy Calculations ◽  
Rna Dependent Rna Polymerase ◽  
Linear Interaction ◽  
Energy Calculations ◽  
High Throughput Virtual Screening

SARS-CoV-2, or severe acute respiratory syndrome coronavirus 2, represents a new pathogen from the family of Coronaviridae that caused a global pandemic of COVID-19 disease. In the absence of effective antiviral drugs, research of novel therapeutic targets such as SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) becomes essential. This viral protein is without a human counterpart and thus represents a unique prospective drug target. However, in vitro biological evaluation testing on RdRp remains difficult and is not widely available. Therefore, we prepared a database of commercial small-molecule compounds and performed an in silico high-throughput virtual screening on the active site of the SARS-CoV-2 RdRp using ensemble docking. We identified a novel thioether-amide or guanidine-linker class of potential RdRp inhibitors and calculated favorable binding free energies of representative hits by molecular dynamics simulations coupled with Linear Interaction Energy calculations. This innovative procedure maximized the respective phase-space sampling and yielded non-covalent inhibitors representing small optimizable molecules that are synthetically readily accessible, commercially available as well as suitable for further biological evaluation and mode of action studies.

A novel compound active against SARS-CoV-2 targeting Uridylate-specific endoribonuclease (NendoU/NSP15): In silico and in vitro investigations

2021 ◽  
Author(s):  
Sumit Kumar ◽  
Yash Gupta ◽  
Samantha Zak ◽  
Charu Upadhyay ◽  
Neha Sharma ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
In Silico ◽  
Specific Enzyme ◽  
Cyclic Phosphates ◽  
High Throughput Virtual Screening

NendoU (NSP15) is an Mn(2+)-dependent, uridylate-specific enzyme, which leaves 2'-3'-cyclic phosphates 5' to the cleaved bond. Our in-house library was subjected to high throughput virtual screening (HTVS) to identify compounds...

scholarly journals In Silico De Novo Curcuminoid Derivatives From the Compound Library of Natural Products Research Laboratories Inhibit COVID-19 3CLpro Activity

2020 ◽  
Vol 15 (9) ◽  
pp. 1934578X2095326
Author(s):  
Jai-Sing Yang ◽  
Jo-Hua Chiang ◽  
Shih‑Chang Tsai ◽  
Yuan-Man Hsu ◽  
Da-Tian Bau ◽  
...  
Keyword(s):  
Natural Products ◽  
Virtual Screening ◽  
Binding Affinity ◽  
High Throughput ◽  
In Silico ◽  
De Novo ◽  
Compound Library ◽  
Therapeutic Success ◽  
High Throughput Virtual Screening

The coronavirus disease 2019 (COVID‐19) outbreak caused by the 2019 novel coronavirus (2019-nCOV) is becoming increasingly serious. In March 2019, the Food and Drug Administration (FDA) designated remdesivir for compassionate use to treat COVID-19. Thus, the development of novel antiviral agents, antibodies, and vaccines against COVID-19 is an urgent research subject. Many laboratories and research organizations are actively investing in the development of new compounds for COVID-19. Through in silico high-throughput virtual screening, we have recently identified compounds from the compound library of Natural Products Research Laboratories (NPRL) that can bind to COVID-19 3Lpro polyprotein and block COVID-19 3Lpro activity through in silico high-throughput virtual screening. Curcuminoid derivatives (including NPRL334, NPRL339, NPRL342, NPRL346, NPRL407, NPRL415, NPRL420, NPRL472, and NPRL473) display strong binding affinity to COVID-19 3Lpro polyprotein. The binding site of curcuminoid derivatives to COVID-19 3Lpro polyprotein is the same as that of the FDA-approved human immunodeficiency virus protease inhibitor (lopinavir) to COVID-19 3Lpro polyprotein. The binding affinity of curcuminoid derivatives to COVID-19 3Lpro is stronger than that of lopinavir and curcumin. Among curcuminoid derivatives, NPRL-334 revealed the strongest binding affinity to COVID-19 3Lpro polyprotein and is speculated to have an anti-COVID-19 effect. In vitro and in vivo ongoing experiments are currently underway to confirm the present findings. This study sheds light on the drug design for COVID-19 3Lpro polyprotein. Basing on lead compound development, we provide new insights on inhibiting COVID-19 attachment to cells, reducing COVID-19 infection rate and drug side effects, and increasing therapeutic success rate.

scholarly journals In silico homology modelling and identification of Tousled-like kinase 1 inhibitors for glioblastoma therapy via high throughput virtual screening protein-ligand docking

2016 ◽  
Author(s):  
Kamariah Ibrahim ◽  
Abubakar Danjuma ◽  
Chyan Leong Ng ◽  
Nor Azian Abdul Murad ◽  
Roslan Harun ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
Energy Minimization ◽  
In Silico ◽  
Homology Modelling ◽  
Ligand Docking ◽  
Molegro Virtual Docker ◽  
Homology Models ◽  
High Throughput Virtual Screening

Background: Glioblastoma multiforme (GBM) is a grade IV brain tumor that arises from star-shaped glial cells supporting neural cells called astrocytes. The survival of GBM patients remains poor despite many specific molecular targets that have been developed and used for therapy. Tousled-like kinase 1 (TLK1), a serine-threonine kinase, was identified to be overexpressed in cancers such as GBM. TLK1 plays an important role in controlling chromosomal aggregation, cell survival and proliferation. In vitro studies suggested that TLK1 is a potential target for some cancers; hence, the identification of suitable molecular inhibitors for TLK1 is warranted as a new therapeutic agents in GBM. To date, there is no structure available for TLK1. In this study, we aimed to create a homology model of TLK1 and to identify suitable molecular inhibitors or compounds that are likely to bind and inhibit TLK1 activity via in silico high-throughput virtual screening (HTVS) protein-ligand docking. Methods: 3D homology models of TLK1 were derived from various servers including HOmology ModellER, i-Tasser, Psipred and Swiss Model. All models were evaluated using Swiss Model Q-Mean server. Only one model was selected for further analysis. Further validation was performed using PDBsum, 3d2go, ProSA, Procheck analysis and ERRAT. Energy minimization was performed using YASARA energy minimization server. Subsequently, HTVS was performed using Molegro Virtual Docker 6.0 and candidate ligands from ligand.info database. Ligand-docking procedures were analyzed at the putative catalytic site of TLK1. Drug-like molecules were filtered using FAF-Drugs3, which is an ADME-Tox filtering program. Results and conclusion: High quality homology models were obtained from the Aurora B kinase (PDB ID:4B8M) derived from Xenopus levias structure that share 33% sequence identity to TLK1. From the HTVS ligand-docking, two compounds were identified to be the potential inhibitors as it did not violate the Lipinski rule of five and the CNS-based filter as a potential drug-like molecule for GBM.

scholarly journals High throughput screening of tubulin inhibitors from parasitic fungi

1970 ◽  
pp. 319-322
Author(s):  
P. A. Karpov ◽  
O. M. Demchuk ◽  
S. P. Ozheriedov ◽  
S. I. Spivak ◽  
O. V. Raievskyi ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
High Throughput ◽  
High Throughput Screening ◽  
Protein Structures ◽  
Pathogenic Fungi ◽  
Tubulin Inhibitors ◽  
Parasitic Fungi ◽  
High Throughput Virtual Screening

Aim. Implementation of 3D-modeling, molecular dynamics, high-throughput screening and molecular docking for search of new inhibitors of parasitic fungi tubulin. Methods. Protein structures were constructed using I-TASSER server and optimized by Gromacs. Ligands library was prepared in Mopac7 program and screened using UCSF Dock 6. Best ligands were docked in CCDC Gold. Results. It was reconstructed spatial molecular structure for 93 α-, 95 β- and 78 γ-tubulins from 76 species of pathogenic fungi genus: Microsporum, Arthroderma, Histoplasma, Blastomyces, Emmonsia, Uncinocarpus, Coccidioides, Paracoccidioides, Aspergillus, Botrytis cinerea, Sclerotinia, Rhynchosporium, Marssonina, Scedosporium, Fusarium, Gibberella, Candida, Ceraceosorus, Malassezia, Anthracocystis, Melanopsichium, Sporisorium, Ustilago, Cryptococcus, Trichosporon, Mucor, Rhizopus and Lichtheimia. Libraries of 3D-models of parasitic fungi tubulins and perspective ligands were created. Based on results of high-throughput virtual screening, 200 perspective agents were selected from more than 7 million compounds. After resulting molecular docking in CCDC GOLD, we specify 19 leading compounds. We propose these compounds as potent tubulin inhibitors and recommend them for in vitro testing as new fungicides. Conclusions. Based on results of high-throughput virtual screening in Grid, 19 new imidazole inhibitors of parasitic fungi tubulin were selected.Keywords: microtubule, tubulins, fungicides, imidazole derivatives, virtual screening, molecular docking.

scholarly journals In silico homology modelling and identification of Tousled-like kinase 1 inhibitors for glioblastoma therapy via high throughput virtual screening protein-ligand docking

2016 ◽  
Author(s):  
Kamariah Ibrahim ◽  
Abubakar Danjuma ◽  
Chyan Leong Ng ◽  
Nor Azian Abdul Murad ◽  
Roslan Harun ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
Energy Minimization ◽  
In Silico ◽  
Homology Modelling ◽  
Ligand Docking ◽  
Molegro Virtual Docker ◽  
Homology Models ◽  
High Throughput Virtual Screening

Background: Glioblastoma multiforme (GBM) is a grade IV brain tumor that arises from star-shaped glial cells supporting neural cells called astrocytes. The survival of GBM patients remains poor despite many specific molecular targets that have been developed and used for therapy. Tousled-like kinase 1 (TLK1), a serine-threonine kinase, was identified to be overexpressed in cancers such as GBM. TLK1 plays an important role in controlling chromosomal aggregation, cell survival and proliferation. In vitro studies suggested that TLK1 is a potential target for some cancers; hence, the identification of suitable molecular inhibitors for TLK1 is warranted as a new therapeutic agents in GBM. To date, there is no structure available for TLK1. In this study, we aimed to create a homology model of TLK1 and to identify suitable molecular inhibitors or compounds that are likely to bind and inhibit TLK1 activity via in silico high-throughput virtual screening (HTVS) protein-ligand docking. Methods: 3D homology models of TLK1 were derived from various servers including HOmology ModellER, i-Tasser, Psipred and Swiss Model. All models were evaluated using Swiss Model Q-Mean server. Only one model was selected for further analysis. Further validation was performed using PDBsum, 3d2go, ProSA, Procheck analysis and ERRAT. Energy minimization was performed using YASARA energy minimization server. Subsequently, HTVS was performed using Molegro Virtual Docker 6.0 and candidate ligands from ligand.info database. Ligand-docking procedures were analyzed at the putative catalytic site of TLK1. Drug-like molecules were filtered using FAF-Drugs3, which is an ADME-Tox filtering program. Results and conclusion: High quality homology models were obtained from the Aurora B kinase (PDB ID:4B8M) derived from Xenopus levias structure that share 33% sequence identity to TLK1. From the HTVS ligand-docking, two compounds were identified to be the potential inhibitors as it did not violate the Lipinski rule of five and the CNS-based filter as a potential drug-like molecule for GBM.

Proposing novel MDM2 inhibitors: Combined physics‐driven high‐throughput virtual screening and in vitro studies

2020 ◽  
Vol 96 (1) ◽  
pp. 684-700
Author(s):  
Gulsah Aydin ◽  
Maide Nur Paksoy ◽  
Müge Didem Orhan ◽  
Timucin Avsar ◽  
Mine Yurtsever ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
In Vitro Studies ◽  
High Throughput Virtual Screening

Inhibitors of multidrug efflux pumps of Pseudomonas aeruginosa from natural sources: An in silico high-throughput virtual screening and in vitro validation

2016 ◽  
Vol 26 (2) ◽  
pp. 414-430 ◽  
Author(s):  
Gianmarco Mangiaterra ◽  
Emiliano Laudadio ◽  
Marta Cometti ◽  
Giovanna Mobbili ◽  
Cristina Minnelli ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Virtual Screening ◽  
High Throughput ◽  
In Silico ◽  
Efflux Pumps ◽  
Multidrug Efflux ◽  
Natural Sources ◽  
Multidrug Efflux Pumps ◽  
High Throughput Virtual Screening

scholarly journals In silico homology modelling and identification of Tousled-like kinase 1 inhibitors for glioblastoma therapy via high throughput virtual screening protein-ligand docking

2016 ◽  
Author(s):  
Kamariah Ibrahim ◽  
Abubakar Danjuma ◽  
Chyan Leong Ng ◽  
Nor Azian Abdul Murad ◽  
Roslan Harun ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
Energy Minimization ◽  
In Silico ◽  
Homology Modelling ◽  
Ligand Docking ◽  
Molegro Virtual Docker ◽  
Homology Models ◽  
High Throughput Virtual Screening

Background: Glioblastoma multiforme (GBM) is a grade IV brain tumor that arises from star-shaped glial cells supporting neural cells called astrocytes. The survival of GBM patients remains poor despite many specific molecular targets that have been developed and used for therapy. Tousled-like kinase 1 (TLK1), a serine-threonine kinase, was identified to be overexpressed in cancers such as GBM. TLK1 plays an important role in controlling chromosomal aggregation, cell survival and proliferation. In vitro studies suggested that TLK1 is a potential target for some cancers; hence, the identification of suitable molecular inhibitors for TLK1 is warranted as a new therapeutic agents in GBM. To date, there is no structure available for TLK1. In this study, we aimed to create a homology model of TLK1 and to identify suitable molecular inhibitors or compounds that are likely to bind and inhibit TLK1 activity via in silico high-throughput virtual screening (HTVS) protein-ligand docking. Methods: 3D homology models of TLK1 were derived from various servers including HOmology ModellER, i-Tasser, Psipred and Swiss Model. All models were evaluated using Swiss Model Q-Mean server. Only one model was selected for further analysis. Further validation was performed using PDBsum, 3d2go, ProSA, Procheck analysis and ERRAT. Energy minimization was performed using YASARA energy minimization server. Subsequently, HTVS was performed using Molegro Virtual Docker 6.0 and candidate ligands from ligand.info database. Ligand-docking procedures were analyzed at the putative catalytic site of TLK1. Drug-like molecules were filtered using FAF-Drugs3, which is an ADME-Tox filtering program. Results and conclusion: High quality homology models were obtained from the Aurora B kinase (PDB ID:4B8M) derived from Xenopus levias structure that share 33% sequence identity to TLK1. From the HTVS ligand-docking, two compounds were identified to be the potential inhibitors as it did not violate the Lipinski rule of five and the CNS-based filter as a potential drug-like molecule for GBM.

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