In silico modeling and molecular docking insights of kaempferitrin for colon cancer-related molecular targets

Backgroud: Kinases are proteins that control many biological functions. They are involved in cellular regulation, and many of them are deregulated in cancer proliferation. The evidence of this deregulation in many pathologies served as the origin of kinases as a therapeutic class and constitutes the motive that leads numerous teams to search for inhibitors of these targets. Objective: Based on 3D-QSAR studies and the molecular docking approach, we have developed new potential inhibitors that could be optimized and transformed into colon cancer drugs. Objective: Based on 3D-QSAR studies and the molecular docking approach, we have developed new potential inhibitors that could be optimized and transformed into colon cancer drugs. Method: To design new bioactive molecules and study their interactions with the cyclin-depend kinase type 2 (CDK2) enzyme, we used two virtual screening methods: 3D-QSAR modeling and molecular docking on a series of 28 pyrimidine-based benzothiazole derivatives. Results: To develop models (3D QSAR) we used CoMFA and CoMSIA techniques using SYBYL-X2.0 molecular modeling software. The statistical parameters reveal that the good CoMFA model displays (Q²= 0.587; R²= 0.895) and that of CoMSIA displays (Q²= 0.552; R²= 0.768) which are considered to be very good internal prediction values, while an external validation of a test series of 5 compounds not included in the model development series gives R²test values of 0.56 for CoMFA and R²test values of 0.51 for CoMSIA. The molecular docking approach with AutoDockTools-1.5.6 is introduced in this work to enrich the interpretations extracted from the CoMFA and CoMSIA contour maps, and to provide an in silico research method for the most favorable mode of interaction of an inhibitor within its receptor (CDK2). Conclusion: We have constructed and validated a quantitative 3D model of structure-activity relation-ships of pyrimidine-based benzothiazole derivatives as CDK2 inhibitors. This model allows us to identify the nature and position of the groups that enhance the activity, giving us directions to discover new, more powerful molecules in a limited time.

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Calix[4]pyrrole based Scrupulous probe for track on of tryptophan: Host-guest interaction, In silico modeling and molecular docking insights

Chemical Physics ◽

10.1016/j.chemphys.2021.111426 ◽

2021 ◽

pp. 111426

Author(s):

Ajay L. Desai ◽

Keyur D. Bhatt ◽

Krunal M. Modi ◽

Nihal P. Patel ◽

Manthan Panchal ◽

...

Keyword(s):

Molecular Docking ◽

In Silico ◽

In Silico Modeling

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In Silico Modeling of Spider Toxins: Bioinformatics, Molecular Docking, and Molecular Dynamics

Spider Venoms ◽

10.1007/978-94-007-6389-0_3 ◽

2016 ◽

pp. 205-221

Author(s):

Moacyr Comar Jr ◽

Vanildo Martins Lima Braga ◽

Débora de Oliveira Lopes

Keyword(s):

Molecular Dynamics ◽

Molecular Docking ◽

In Silico ◽

Spider Toxins ◽

In Silico Modeling

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In silico QSAR and molecular docking studies of sulfur containing shikonin oxime derivatives as anti-cancer agent for colon cancer

Radiology of Infectious Diseases ◽

10.1016/j.jrid.2019.10.001 ◽

2019 ◽

Vol 6 (3) ◽

pp. 108-121

Author(s):

Sagir Yusuf Ismail ◽

Adamu Uzairu

Keyword(s):

Colon Cancer ◽

Molecular Docking ◽

In Silico ◽

Docking Studies ◽

Molecular Docking Studies ◽

Anti Cancer ◽

Oxime Derivatives ◽

Cancer Agent ◽

Sulfur Containing

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Antiplasmodial Biodereplication Based on Highly Efficient Methods

10.26434/chemrxiv.11828802 ◽

2020 ◽

Author(s):

Pedro Vásquez-Ocmín ◽

Jean-François Gallard ◽

Anne-Cécile Van Baelen ◽

Karine Leblanc ◽

Sandrine Cojean ◽

...

Keyword(s):

Mass Spectrometry ◽

Data Mining ◽

Molecular Docking ◽

Mechanism Of Action ◽

In Silico ◽

Binding Assay ◽

Antimalarial Drugs ◽

Heme Binding ◽

Molecular Networking ◽

In Silico Modeling

We propose a biodereplication method using mass spectrometry and combining the classical dereplication approach with the predominant mechanism of action of antimalarial drugs. The method encompasses a biomimetic heme binding assay (heme adducts detection by MS), molecular networking for quick data mining, CPC for extract fractionation and compounds isolation and in silico modeling of heme adducts by molecular docking.

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In Silico Modeling of Spider Toxins: Bioinformatics, Molecular Docking, and Molecular Dynamics

Spider Venoms ◽

10.1007/978-94-007-6646-4_3-1 ◽

2015 ◽

pp. 1-14

Author(s):

Moacyr Comar Junior ◽

Vanildo Martins Lima Braga ◽

Débora de Oliveira Lopes

Keyword(s):

Molecular Dynamics ◽

Molecular Docking ◽

In Silico ◽

Spider Toxins ◽

In Silico Modeling

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In silico modeling of izoniazid-steroid conjugates interactions with cytochromes P450 of mycobacteria and their bioconversion in vitro by the cells

Biomeditsinskaya Khimiya ◽

10.18097/pbmc20206605378 ◽

2020 ◽

Vol 66 (5) ◽

pp. 378-385

Author(s):

Y.V. Faletrov ◽

K.A. Gilep ◽

A.S. Falchevskaya ◽

M.S. Horetski ◽

J.V. Panada ◽

...

Keyword(s):

Molecular Docking ◽

Binding Energy ◽

In Silico ◽

Cytochromes P450 ◽

In Silico Study ◽

In Silico Modeling ◽

Specificity Of Binding ◽

Steroid Conjugates ◽

Bilayer Permeability

Molecular docking of four hydrazones of isoniazid with steroids (dehydroepiandrosterone, pregnenolone, 16α,17α-epoxypregnenolone, cholestenone) — IDHEA, IPRE, IEP5, ICHN, to mycobacterial cytochromes P450 was performed. The in silico study has shown than these hydrazones can be effectively bound to CYP121, CYP124, CYP125, CYP126A1, CYP130, and CYP51 with binding energy ranged from -9 kcal/mol to -12 kcal/mol. Calculations also demonstrated enhancement of passive lipid bilayer permeability with respect to isoniazid. In vitro IDHEA, IPRE, IEPR were found to undergo bioconversion into their 3-keto-4-en derivatives. This suggests their ability to penetrate into M. tuberculosis H37Rv cells. The results of this study are important in the context of understanding of specificity of binding of synthetic steroid derivatives to mycobacterial CYPs and indicate the possibility of using the steroid compounds studied by us as new ligands for these enzymes.

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Antiplasmodial Biodereplication Based on Highly Efficient Methods

10.26434/chemrxiv.11828802.v1 ◽

2020 ◽

Cited By ~ 2

Author(s):

Pedro Vásquez-Ocmín ◽

Jean-François Gallard ◽

Anne-Cécile Van Baelen ◽

Karine Leblanc ◽

Sandrine Cojean ◽

...

Keyword(s):

Mass Spectrometry ◽

Data Mining ◽

Molecular Docking ◽

Mechanism Of Action ◽

In Silico ◽

Binding Assay ◽

Antimalarial Drugs ◽

Heme Binding ◽

Molecular Networking ◽

In Silico Modeling

We propose a biodereplication method using mass spectrometry and combining the classical dereplication approach with the predominant mechanism of action of antimalarial drugs. The method encompasses a biomimetic heme binding assay (heme adducts detection by MS), molecular networking for quick data mining, CPC for extract fractionation and compounds isolation and in silico modeling of heme adducts by molecular docking.

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EVALUATION OF IN-SILICO ANTICANCER POTENTIAL OF PYRETHROIDS: A COMPARATIVE MOLECULAR DOCKING STUDY

10.3390/ecsoc-19-e012 ◽

2015 ◽

Author(s):

Manik Ghosh ◽

Kamal Kant ◽

Anoop Kumar ◽

Padma Behera ◽

Naresh Rangra ◽

...

Keyword(s):

Molecular Docking ◽

In Silico ◽

Docking Study ◽

Molecular Docking Study

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In Silico Molecular Docking and Molecular Dynamic Simulation of Potential Inhibitors of 3C-like Main Proteinase (3CLpro) from Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) Using Selected African Medicinal Plants

10.26434/chemrxiv.12480434.v1 ◽

2020 ◽

Author(s):

Sahar Qazi ◽

Mustafa Alhaji Isa ◽

Adam Mustapha ◽

Khalid Raza ◽

Ibrahim Alkali Allamin ◽

...

Keyword(s):

Molecular Docking ◽

Severe Acute Respiratory Syndrome ◽

In Silico ◽

Md Simulation ◽

Binding Energies ◽

Anacardium Occidentale ◽

Upper Respiratory Tract ◽

Ligand Complex ◽

In Silico Docking ◽

Main Protease

The Severe Acute Respiratory Syndrome 2 (SARS-CoV-2) is an infectious virus that causes mild to severe life-threatening upper respiratory tract infection. The virus emerged in Wuhan, China in 2019, and later spread across the globe. Its genome has been completely sequenced and based on the genomic information, the virus possessed 3C-Like Main Protease (3CLpro), an essential multifunctional enzyme that plays a vital role in the replication and transcription of the virus by cleaving polyprotein at eleven various sites to produce different non-structural proteins. This makes the protein an important target for drug design and discovery. Herein, we analyzed the interaction between the 3CLpro and potential inhibitory compounds identified from the extracts of Zingiber offinale and Anacardium occidentale using in silico docking and Molecular Dynamics (MD) Simulation. The crystal structure of SARS-CoV-2 main protease in complex with 02J (5-Methylisoxazole-3-carboxylic acid) and PEJ (composite ligand) (PDB Code: 6LU7,2.16Å) retrieved from Protein Data Bank (PDB) and subject to structure optimization and energy minimization. A total of twenty-nine compounds were obtained from the extracts of Zingiber offinale and the leaves of Anacardium occidentale. These compounds were screened for physicochemical (Lipinski rule of five, Veber rule, and Egan filter), Pan-Assay Interference Structure (PAINS), and pharmacokinetic properties to determine the Pharmaceutical Active Ingredients (PAIs). Of the 29 compounds, only nineteen (19) possessed drug-likeness properties with efficient oral bioavailability and less toxicity. These compounds subjected to molecular docking analysis to determine their binding energies with the 3CLpro. The result of the analysis indicated that the free binding energies of the compounds ranged between ˗5.08 and -10.24kcal/mol, better than the binding energies of 02j (-4.10kcal/mol) and PJE (-5.07kcal.mol). Six compounds (CID_99615 = -10.24kcal/mol, CID_3981360 = 9.75kcal/mol, CID_9910474 = -9.14kcal/mol, CID_11697907 = -9.10kcal/mol, CID_10503282 = -9.09kcal/mol and CID_620012 = -8.53kcal/mol) with good binding energies further selected and subjected to MD Simulation to determine the stability of the protein-ligand complex. The results of the analysis indicated that all the ligands form stable complexes with the protein, although, CID_9910474 and CID_10503282 had a better stability when compared to other selected phytochemicals (CID_99615, CID_3981360, CID_620012, and CID_11697907).

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