scholarly journals In Silico Screening of Available Drugs Targeting Non-Small Cell Lung Cancer Targets: A Drug Repurposing Approach

Pharmaceutics ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 59
Author(s):  
Muthu Kumar Thirunavukkarasu ◽  
Utid Suriya ◽  
Thanyada Rungrotmongkol ◽  
Ramanathan Karuppasamy
Keyword(s):  
Drug Resistance ◽  
Virtual Screening ◽  
High Throughput ◽  
In Silico ◽  
Limiting Factor ◽  
Energy Calculation ◽  
Lead Compounds ◽  
Study Results ◽  
Glide Docking ◽  
High Throughput Virtual Screening

The RAS–RAF–MEK–ERK pathway plays a key role in malevolent cell progression in many tumors. The high structural complexity in the upstream kinases limits the treatment progress. Thus, MEK inhibition is a promising strategy since it is easy to inhibit and is a gatekeeper for the many malignant effects of its downstream effector. Even though MEK inhibitors are under investigation in many cancers, drug resistance continues to be the principal limiting factor to achieving cures in patients with cancer. Hence, we accomplished a high-throughput virtual screening to overcome this bottleneck by the discovery of dual-targeting therapy in cancer treatment. Here, a total of 11,808 DrugBank molecules were assessed through high-throughput virtual screening for their activity against MEK. Further, the Glide docking, MLSF and prime-MM/GBSA methods were implemented to extract the potential lead compounds from the database. Two compounds, DB012661 and DB07642, were outperformed in all the screening analyses. Further, the study results reveal that the lead compounds also have a significant binding capability with the co-target PIM1. Finally, the SIE-based free energy calculation reveals that the binding of compounds was majorly affected by the van der Waals interactions with MEK receptor. Overall, the in silico binding efficacy of these lead compounds against both MEK and PIM1 could be of significant therapeutic interest to overcome drug resistance in the near future.

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 Natural products as inhibitors of COVID-19 main protease – A virtual screening by molecular docking

2020 ◽  
Author(s):  
Marzieh omrani ◽  
Mohammad Bayati ◽  
Parvaneh Mehrbod ◽  
Samad Nejad-Ebrahimi
Keyword(s):  
Natural Products ◽  
Virtual Screening ◽  
High Throughput ◽  
Herbal Medicines ◽  
Respiratory Illness ◽  
Fast Method ◽  
Main Protease ◽  
Glide Docking ◽  
Adme Properties ◽  
High Throughput Virtual Screening

Abstract Background: The novel coronavirus (2019-nCoV) causes a severe respiratory illness that was unknown in the human before. Its alarmingly quick transmission to many countries across the world resulted in a worldwide health emergency. It has caused a notable percentage of morbidity and mortality. Therefore, an imminent need for drugs to combat this disease has been increased. Global collaborative efforts from scientists are underway to find a therapy to treat infections and reduce death cases. Herbal medicines and purified natural products have been reported to have antiviral activity against Coronaviruses (CoVs).Methods: In this study, a High Throughput Virtual Screening (HTVS) protocol was used as a fast method on the discovery of novel drug candidates as the COVID-19 main protease inhibitors. Over 180,000 natural product-based compounds were obtained from the ZINC database and virtually screened against the COVID-19 main protease. In this study, the Glide docking program was applied for high throughput virtual screening. Extra precision (XP) and in a combination of Prime module, induced-fit docking (IFD) approach was also used. Additionally, the ADME properties of all compounds were analyzed, and the final selection was carried out based on the Lipinski rule of five. Results: The nineteen compounds were selected and introduced as new potential inhibitors. The compound ZINC08765174 (1-[3-(1H-indol-3-yl) propanoyl]-N-(4-phenylbutan-2-yl)piperidine-3-carboxamide) showed a strong binding affinity (-11.5 kcal/mol) to the crucial residues of COVID-19 main protease comparing to peramivir (-9.8 kcal/mol) as a positive control.Conclusions: The excellent ADME properties proposed the opportunity of this compound to be a promising candidate for the treatment of COVID-19.

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

2015 ◽  
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 ◽  
Structural Information ◽  
Homology Modelling ◽  
Ligand Docking ◽  
Nmr Studies ◽  
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 have been developed. Tousled-like kinase 1 (TLK1), a serine-threonine kinase, was identified to be overexpressed in cancer 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, identification of suitable molecular inhibitors for TLK1 is warranted as new therapeutic agents in GBM. To date, there is no direct structural information available from X-ray crystallography and NMR studies 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 catalytic site of TLK1. Drug-like molecules were filtered using FAFDrugs3 ADME-Tox filter. Results and conclusion: High quality homology models were obtained from the 4B8M Aurora B kinase 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 CNS-based filter as a potential drug-like molecule for GBM.

scholarly journals Natural products as inhibitors of COVID-19 main protease–A virtual screening by molecular docking

2021 ◽  
Author(s):  
Marzieh Omrani ◽  
Mohammad Bayati ◽  
Parvaneh Mehrbod ◽  
Kamal Asmari Bardazard ◽  
Samad Nejad-Ebrahimi
Keyword(s):  
Natural Products ◽  
Virtual Screening ◽  
High Throughput ◽  
Herbal Medicines ◽  
Respiratory Illness ◽  
Fast Method ◽  
Main Protease ◽  
Glide Docking ◽  
Adme Properties ◽  
High Throughput Virtual Screening

Background: The novel coronavirus (2019-nCoV) causes a severe respiratory illness unknown to a human before. Its alarmingly quick transmission to many countries across the world has resulted in a global health emergency. Therefore, an imminent need for drugs to combat this disease has been increased. Worldwide collaborative efforts from scientists are underway to determine a therapy to treat COVID-19 infections and reduce mortality rates. Since herbal medicines and purified natural products have been reported to have antiviral activity against Coronaviruses (CoVs), this in silico evaluation was performed for identifying potential natural compounds with promising inhibitory activities against COVID-19. Methods: In this study, a High Throughput Virtual Screening (HTVS) protocol was used as a fast method for discovering novel drug candidates as potential COVID-19 main protease (Mpro) inhibitors. Over 180,000 natural product-based compounds were obtained from the ZINC database and virtually screened against the COVID-19 Mpro. In this study, the Glide docking program was applied for high throughput virtual screening. Also, Extra precision (XP) has been used following the induced-fit docking (IFD) approach. The ADME properties of all compounds were analyzed and a final selection was made based on the Lipinski rule of five. Also, molecular dynamics (MD) simulations were conducted for a virtual complex of the best scoring compound with COVID-19 protease. Results: Nineteen compounds were introduced as new potential inhibitors. Compound ZINC08765174 (1-[3-(1H-indol-3-yl) propanoyl]-N-(4-phenylbutan-2-yl)piperidine-3-carboxamide showed a strong binding affinity (-11.5 kcal/mol) to the COVID-19 Mpro comparing to peramivir (-9.8 kcal/mol) as a positive control. Conclusions: Based on these findings, nineteen compounds were proposed as possible new COVID-19 inhibitors, of which ZINC08765174 had a high affinity to Mpro. Furthermore, the promising ADME properties of the selected compounds emphasize their potential as attractive candidates for the treatments of COVID-19.

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 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.

Sign in / Sign up

Export Citation Format

Share Document