Field-based 3D-QSAR for tyrosine protein kinase JAK-2 inhibitors

Andole, Sowmya; Thumma, Gouthami; Alavala, Rajasekhar Reddy; Gangarapu, Kiran

doi:10.1080/07391102.2023.2226723

Cited by 2 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method of molecular field-based similarity was employed for the purpose of conformational search that designed the pharmacophore template of the ligands. The 3D-QSAR model was generated, which helped in modulating the bioactivity of the molecules . The protein selected was 3EQM taken from the Protein Data Bank.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…A molecular dynamics (MD) simulation was performed to investigate the dynamic behavior of proteins upon inhibitor binding at the atomic level. Using the ff19SB force field and the AMBER22 package, a thorough MDS analysis was performed . The complexes were constructed and solved using the preparation program Tleap.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…The 3D-QSAR model was generated, which helped in modulating the bioactivity of the molecules. 36 The protein selected was 3EQM taken from the Protein Data Bank. The protein selected has a resolution of about 2.90 Å and Homo sapiens organism.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Using the ff19SB force field and the AMBER22 package, a thorough MDS analysis was performed. 36 The complexes were constructed and solved using the preparation program Tleap. In this experiment, a solvated octahedral box was utilized.…”

Section: Materials and Methodsmentioning

confidence: 99%

See 3 more Smart Citations

ExploringCitrus sinensisPhytochemicals as Potential Inhibitors for Breast Cancer Genes BRCA1 and BRCA2 Using Pharmacophore Modeling, Molecular Docking, MD Simulations, and DFT Analysis

Zia,

Parveen,

Shafiq

et al. 2024

ACS Omega

View full text Add to dashboard Cite

Background: Structure−activity relationship (SAR) is considered to be an effective in silico approach when discovering potential antagonists for breast cancer due to gene mutation. Major challenges are faced by conventional SAR in predicting novel antagonists due to the discovery of diverse antagonistic compounds. Methodologyand Results: In predicting breast cancer antagonists, a multistep screening of phytochemicals isolated from the seeds of the Citrus sinensis plant was applied using feasible complementary methodologies. A three-dimensional quantitative structure−activity relationship (3D-QSAR) model was developed through the Flare project, in which conformational analysis, pharmacophore generation, and compound alignment were done. Ten hit compounds were obtained through the development of the 3D-QSAR model. For exploring the mechanism of action of active compounds against cocrystal inhibitors, molecular docking analysis was done through Molegro software (MVD) to identify lead compounds. Three new proteins, namely, 1T15, 3EU7, and 1T29, displayed the best Moldock scores. The quality of the docking study was assessed by a molecular dynamics simulation. Based on binding affinities to the receptor in the docking studies, three lead compounds (stigmasterol P8, epoxybergamottin P28, and nobiletin P29) were obtained, and they passed through absorption, distribution, metabolism, and excretion (ADME) studies via the SwissADME online service, which proved that P28 and P29 were the most active allosteric inhibitors with the lowest toxicity level against breast cancer. Then, density functional theory (DFT) studies were performed to measure the active compound's reactivity, hardness, and softness with the help of Gaussian 09 software. Conclusions: This multistep screening of phytochemicals revealed high-reliability antagonists of breast cancer by 3D-QSAR using flare, docking analysis, and DFT studies. The present study helps in providing a proper guideline for the development of novel inhibitors of BRCA1 and BRCA2.

show abstract

Section: Materials and Methodsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

See 2 more Smart Citations