scholarly journals Natural Product Compounds in Alpinia officinarum and Ginger are Potent SARS-CoV-2 Papain-like Protease Inhibitors

2020 ◽  
Author(s):  
Dibakar Goswami ◽  
Mukesh Kumar ◽  
Sunil K. Ghosh ◽  
Amit Das
Keyword(s):  
Virtual Screening ◽  
Natural Product ◽  
Protease Inhibitors ◽  
In Silico ◽  
Drug Target ◽  
Homology Modelling ◽  
Protease Domain ◽  
Lead Compounds ◽  
Alpinia Officinarum ◽  
Natural Product Compounds

SARS-CoV-2 or COVID-19 has caused more than 10,00,000 infections and ~55,000 deaths worldwide spanning over 203 countries, and the numbers are exponentially increasing. Due to urgent need of treating the SARS infection, many approved, pre-clinical, anti-viral, anti-malarial and anti-SARS drugs are being administered to patients. SARS-CoV-2 papain-like protease (PLpro) has a protease domain which cleaves the viral polyproteins a/b, necessary for its survival and replication, and is one of the drug target against SARS-CoV-2. 3D structures of SARS-CoV-2 PLpro were built by homology modelling. Two models having partially open and closed conformations were used in our study. Virtual screening of natural product compounds was performed. We prepared an in house library of compounds found in rhizomes, Alpinia officinarum, ginger and curcuma, and docked them into the solvent accessible S3-S4 pocket of PLpro. Eight compounds from Alpinia officinarum and ginger bind with high in silico affinity to closed PLpro conformer, and hence are potential SARS-CoV-2 PLpro inhibitors. Our study reveal new lead compounds targeting SARS-CoV-2. Further structure based modifications or extract formulations of these compounds can lead to highly potent inhibitors to treat SARS-CoV-2 infections.<br>

scholarly journals Natural Product Compounds in Alpinia officinarum and Ginger are Potent SARS-CoV-2 Papain-like Protease Inhibitors

2020 ◽  
Author(s):  
Dibakar Goswami ◽  
Mukesh Kumar ◽  
Sunil K. Ghosh ◽  
Amit Das
Keyword(s):  
Virtual Screening ◽  
Natural Product ◽  
Protease Inhibitors ◽  
In Silico ◽  
Drug Target ◽  
Homology Modelling ◽  
Protease Domain ◽  
Lead Compounds ◽  
Alpinia Officinarum ◽  
Natural Product Compounds

SARS-CoV-2 or COVID-19 has caused more than 10,00,000 infections and ~55,000 deaths worldwide spanning over 203 countries, and the numbers are exponentially increasing. Due to urgent need of treating the SARS infection, many approved, pre-clinical, anti-viral, anti-malarial and anti-SARS drugs are being administered to patients. SARS-CoV-2 papain-like protease (PLpro) has a protease domain which cleaves the viral polyproteins a/b, necessary for its survival and replication, and is one of the drug target against SARS-CoV-2. 3D structures of SARS-CoV-2 PLpro were built by homology modelling. Two models having partially open and closed conformations were used in our study. Virtual screening of natural product compounds was performed. We prepared an in house library of compounds found in rhizomes, Alpinia officinarum, ginger and curcuma, and docked them into the solvent accessible S3-S4 pocket of PLpro. Eight compounds from Alpinia officinarum and ginger bind with high in silico affinity to closed PLpro conformer, and hence are potential SARS-CoV-2 PLpro inhibitors. Our study reveal new lead compounds targeting SARS-CoV-2. Further structure based modifications or extract formulations of these compounds can lead to highly potent inhibitors to treat SARS-CoV-2 infections.<br>

scholarly journals Drug Repurposing for Candidate SARS-CoV-2 Main Protease Inhibitors by a Novel in Silico Method

2020 ◽  
Author(s):  
Milan Sencanski ◽  
Vladimir Perovic ◽  
Snezana Pajovic ◽  
Miroslav Adzic ◽  
Slobodan Paessler ◽  
...  
Keyword(s):  
Protease Inhibitors ◽  
In Silico ◽  
Drug Target ◽  
Transmission Rate ◽  
Experimental Testing ◽  
Drug Repurposing ◽  
Global Public Health ◽  
Main Protease ◽  
Additional Approach ◽  
Drugbank Database

<p>The SARS-CoV-2 outbreak caused an unprecedented global public health threat, having a high transmission rate with currently no drugs or vaccines approved. An alternative powerful additional approach to counteract COVID-19 is <em>in silico</em> drug repurposing. The SARS-CoV-2 main protease is essential for viral replication and an attractive drug target. In this study, we used the virtual screening (VS) protocol with both long-range and short-range interactions to select candidate SARS-CoV-2 main protease inhibitors. First, the ISM applied for Small Molecules was used for searching the Drugbank database and further followed by molecular docking. After <em>in silico</em> screening of drug space, we identified 57 drugs as potential SARS-CoV-2 main protease inhibitors that we propose for further experimental testing.</p>

scholarly journals A Program for Automated Data Mining of PubChem to Screen a Billion Compounds and Generate by Machine Learning Based AutoQSAR Algorithm Anti-Corona Viral Drug Leads (Replicase Polyprotein 1ab Inhibitors) and in Silico Study of the Top Drug Lead Compounds

2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Akhil Sanker ◽  
Host Antony Davidd ◽  
Judith Gracia
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
In Silico ◽  
Drug Target ◽  
Data Set ◽  
Lead Compounds ◽  
Pubchem Database ◽  
Drug Lead ◽  
Lead Generation ◽  
Drug Leads

Our work is composed of a python program for automatic data mining of PubChem database to collect data associated with the corona virus drug target replicase polyprotein 1ab (UniProt identifier : POC6X7 ) of data set involving active compounds, their activity value (IC50) and their chemical/molecular descriptors to run a machine learning based AutoQSAR algorithm on the data set to generate anti-corona viral drug leads. The machine learning based AutoQSAR algorithm involves feature selection, QSAR modelling, validation and prediction. The drug leads generated each time the program is run is reflective of the constantly growing PubChem database is an important dynamic feature of the program which facilitates fast and dynamic anti-corona viral drug lead generation reflective of the constantly growing PubChem database. The program prints out the top anti-corona viral drug leads after screening PubChem library which is over a billion compounds. The interaction of top drug lead compounds generated by the program and two corona viral drug target proteins, 3-Cystiene like Protease (3CLPro) and Papain like protease (PLpro) was studied and analysed using molecular docking tools. The compounds generated as drug leads by the program showed favourable interaction with the drug target proteins and thus we recommend the program for use in anti-corona viral compound drug lead generation as it helps reduce the complexity of virtual screening and ushers in an age of automatic ease in drug lead generation. The leads generated by the program can further be tested for drug potential through further In Silico, In Vitro and In Vivo testing <div><br></div><div><div>The program is hosted, maintained and supported at the GitHub repository link given below</div><div><br></div><div>https://github.com/bengeof/Drug-Discovery-P0C6X7</div></div><div><br></div>

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.

scholarly journals In Silico Characterization and Virtual Screening of GntR/HutC Family Transcriptional Regulator MoyR: A Potential Monooxygenase Regulator in Mycobacterium tuberculosis

Biology ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1241
Author(s):  
Thanusha Dhananji Abeywickrama ◽  
Inoka Chinthana Perera
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Virtual Screening ◽  
In Silico ◽  
Streptomyces Coelicolor ◽  
Homology Modelling ◽  
Transcriptional Regulators ◽  
Physicochemical Analysis ◽  
Structure Stability ◽  
In Silico Characterization ◽  
Drug Leads

Mycobacterium tuberculosis is a well-known pathogen due to the emergence of drug resistance associated with it, where transcriptional regulators play a key role in infection, colonization and persistence. The genome of M. tuberculosis encodes many transcriptional regulators, and here we report an in-depth in silico characterization of a GntR regulator: MoyR, a possible monooxygenase regulator. Homology modelling provided a reliable structure for MoyR, showing homology with a HutC regulator DasR from Streptomyces coelicolor. In silico physicochemical analysis revealed that MoyR is a cytoplasmic protein with higher thermal stability and higher pI. Four highly probable binding pockets were determined in MoyR and the druggability was higher in the orthosteric binding site consisting of three conserved critical residues: TYR179, ARG223 and GLU234. Two highly conserved leucine residues were identified in the effector-binding region of MoyR and other HutC homologues, suggesting that these two residues can be crucial for structure stability and oligomerization. Virtual screening of drug leads resulted in four drug-like compounds with greater affinity to MoyR with potential inhibitory effects for MoyR. Our findings support that this regulator protein can be valuable as a therapeutic target that can be used for developing drug leads.

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.

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