Evidence map›Paper›PMID 34590967›Full record

ArticleJournal of biomolecular structure & dynamics2022

Identification of potent inhibitors against transmembrane serine protease 2 for developing therapeutics against SARS-CoV-2.

Abdulla Al Mamun, Farjana Akter, Maksud Khan, Sayeda Samina Ahmed, Md Giash Uddin, Nabila Tabassum Tasfia, Faiyaz Md Efaz, Md Ackas Ali, Mossammad Umme Chand Sultana, Mohammad A Halim

Open access · greenAbstract read
In one paragraph

Article in Journal of biomolecular structure & dynamics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.6field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 5 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 4 countries.

Abdulla Al MamunDivision of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Farjana AkterDivision of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Maksud KhanDivision of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Sayeda Samina AhmedDivision of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Md Giash UddinDivision of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Nabila Tabassum TasfiaDivision of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Faiyaz Md EfazDivision of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Md Ackas AliDivision of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Mossammad Umme Chand SultanaDivision of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh.
Mohammad A HalimDepartment of Chemistry and Biochemistry, Kennesaw State University, Kennesaw, GA, USA.
Centre for Drug Research and Development · CAKennesaw State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In viral binding and entry, the Spike(S) protein of SARS-CoV-2 uses transmembrane serine protease 2 (TMPRSS2) for priming to cleavage themselves. In this study, we have screened 'drug-like' 7476 ligands and found that over thirty ligands can effectively inhibit the TMPRSS-2 better than the control ligand. Finally, the three best drug agents L1, L2, and L6 were selected according to their average binding affinities and fitting score. These ligands interact with Asp435, Cys437, Ser436, Trp461, and Cys465 amino acid residues. The three best candidates and a reported drug Nafamostat mesylate (NAM) were selected to run 250 ns molecular dynamics (MD) simulations. Various properties of ligand-protein interactions obtained from MD simulation such as bonds, angle, dihedral, planarity, coulomb, and van der Waals (VdW) were used for principal component analysis (PCA) calculation. PCA discloses the evidence of the structural similarities to the corresponding complexes of L1, L2, and L6 with the complex of TMPRSS2(TM) and Nafamostat mesylate (TM-NAM). Moreover, Quantitative structure-activity relationship (QSAR) pattern recognition was generated using PCA for the investigation of structural similarities among the selected ligands. Multiple Linear Regression (MLR) model was built to predict the binding energy compared to the binding energy obtained from molecular docking. The MLR regression model reveals an accuracy of 80% for the prediction of the binding energy of ligands. ADMET analysis demonstrates that these drug agents are appeared to be safer inhibitors. These three ligands can be used as potential inhibitors against the TMPRSS2.Communicated by Ramaswamy H. Sarma.

Indexed as

COVID-19SARS-CoV-2BenzamidinesGuanidinesHumansLigandsMolecular Docking SimulationMolecular Dynamics SimulationProtease InhibitorsBenzamidinesGuanidinesLigandsnafamostatProtease Inhibitorsmolecular dynamicsQSARSARS-CoV-2TMPRSS2virtual screening

Identifiers

PMID34590967
PMCPMC8500310
OpenAlexW3204431869

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.