Computational identification of JAK2 inhibitors: a combined pharmacophore mapping and molecular docking approach

Sathe, Rohit Y.; Kulkarni, Seema A.; Sella, Raja Natesan; Madhavan, Thirumurthy

doi:10.1007/s00044-014-1223-6

Cited by 5 publications

(5 citation statements)

References 32 publications

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“…The results of selectivity validation are tabulated in Table 4 . The fitness score for JAK1 inhibitors was greater than or equal to 1.5, whereas for other JAK inhibitors, the fitness score was <1.5 for most of the molecules indicating that the pharmacophore models were able to map well with the JAK1 inhibitors ( Sathe et al, 2014 ; Babu et al, 2015 ).…”

Section: Resultsmentioning

confidence: 96%

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Identification of Potent and Selective JAK1 Lead Compounds Through Ligand-Based Drug Design Approaches

et al. 2022

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JAK1 plays a significant role in the intracellular signaling by interacting with cytokine receptors in different types of cells and is linked to the pathogenesis of various cancers and in the pathology of the immune system. In this study, ligand-based pharmacophore modeling combined with virtual screening and molecular docking methods was incorporated to identify the potent and selective lead compounds for JAK1. Initially, the ligand-based pharmacophore models were generated using a set of 52 JAK1 inhibitors named C-2 methyl/hydroxyethyl imidazopyrrolopyridines derivatives. Twenty-seven pharmacophore models with five and six pharmacophore features were generated and validated using potency and selectivity validation methods. During potency validation, the Guner-Henry score was calculated to check the accuracy of the generated models, whereas in selectivity validation, the pharmacophore models that are capable of identifying selective JAK1 inhibitors were evaluated. Based on the validation results, the best pharmacophore models ADHRRR, DDHRRR, DDRRR, DPRRR, DHRRR, ADRRR, DDHRR, and ADPRR were selected and taken for virtual screening against the Maybridge, Asinex, Chemdiv, Enamine, Lifechemicals, and Zinc database to identify the new molecules with novel scaffold that can bind to JAK1. A total of 4,265 hits were identified from screening and checked for acceptable drug-like properties. A total of 2,856 hits were selected after ADME predictions and taken for Glide molecular docking to assess the accurate binding modes of the lead candidates. Ninety molecules were shortlisted based on binding energy and H-bond interactions with the important residues of JAK1. The docking results were authenticated by calculating binding free energy for protein–ligand complexes using the MM-GBSA calculation and induced fit docking methods. Subsequently, the cross-docking approach was carried out to recognize the selective JAK1 lead compounds. Finally, top five lead compounds that were potent and selective against JAK1 were selected and validated using molecular dynamics simulation. Besides, the density functional theory study was also carried out for the selected leads. Through various computational studies, we observed good potency and selectivity of these lead compounds when compared with the drug ruxolitinib. Compounds such as T5923555 and T5923531 were found to be the best and can be further validated using in vitro and in vivo methods.

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Section: Resultsmentioning

confidence: 96%

“…GH score ranges from 0 to 1, which indicates a null model and an ideal model, respectively. GH score >0.6 indicates the acceptable quality of the pharmacophore model and is useful in differentiating the known active molecules from inactives and suitable for retrieving active JAK1 inhibitors ( Sathe et al, 2014 ; Li et al, 2015 ).…”

Section: Methodsmentioning

confidence: 99%

Identification of Potent and Selective JAK1 Lead Compounds Through Ligand-Based Drug Design Approaches

et al. 2022

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“…Hence it is an ideal target for drug design against various diseases. [1][2][3][4][5][6][7] Cancer results from deregulated and aberrant cell division, which invades and expands to different sites in the body. JAK2 upregulation has been proven to be visible in lung cancer, breast cancer, prostate cancer, melanoma, colorectal cancer, cervical cancer, gastric cancer, bladder cancer, and many other cancers.…”

Section: Introductionmentioning

confidence: 99%

“…Mutation in JAK2 has been reported to various disorders like myeloproliferative neoplasms (MPNs), polycythemia vera, essential thrombocythemia, primary myelofibrosis, hypertension, and cardiovascular diseases. Hence it is an ideal target for drug design against various diseases [1–7] . Cancer results from deregulated and aberrant cell division, which invades and expands to different sites in the body.…”

Section: Introductionmentioning

confidence: 99%

Designing JAK2 Inhibitors Beyond Myeloproliferative Neoplasms ‐ Theoretical and Experimental Analysis for Solid Cancers

Achutha,

Krishna,

Pushpa

et al. 2024

ChemistrySelect

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Somatic mutations in JAK2 have been reported to cause myeloproliferative neoplasms, autoimmune disorders, rheumatoid arthritis, and inflammation. JAK2 protein with PDB id 3FUP was selected for docking analysis via cross‐docking. Molecular docking of JAK2 with ATP and crystal ligand revealed their binding affinities and interactions. 3D QSAR models with R2=0.997 and Q2=0.984 having high stability and predictivity were generated. The biological activity of the hit compound obtained from virtual screening, LAS 26879206, was predicted to be 4.21 using the 3D QSAR model. Modification of LAS 26879206 utilizing 3D QSAR contours for modulating its JAK2 inhibitory potential yielded two novel inhibitors with a 2‐aminopyrimidine core – ACP1 and ACPM1. Molecular docking, molecular dynamics simulations, and ADMET analysis indicated improvements in the efficiency of these compounds compared to the hit compound. Synthesis of these compounds was carried out using a two‐step reaction‐ first Buchwald – Hartwig coupling reaction to yield an intermediate ACB1 which was then coupled with appropriate boronic acids via Suzuki reaction to get ACP1 and ACPM1. The effects of these designed compounds as anticancer agents were analyzed in hCT‐116 (human colorectal carcinoma), HeLa (cervical carcinoma), A549 (human lung adenocarcinoma), and A375 (human melanoma) cell lines. ACPM1 was found to be active in all the cell lines with a pIC50 of 5 which was in concordance with the in silico studies. Western blot analysis validated the JAK2 inhibitory property of ACPM1 which can be further ensured by in vivo studies.

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“…Several QSAR models were developed in the hope to drive the novel compounds with better properties against kinase [15,16,17,18,19,20,21,22]. To understand the origin and bioactivities of JAK inhibitors, models were developed with the hope to identify important pharmacophores and substructures using pharmacophores and 3D QSAR [23,24,25,26,27,28,29,30,31,32,33,34,35]. Due to the polypharmacological nature of compounds, multi-target QSAR models have been also developed to handle the interaction of multiple targets of JAK inhibitors.…”

Section: Introductionmentioning

confidence: 99%

Construction of Quantitative Structure Activity Relationship (QSAR) Models to Predict Potency of Structurally Diversed Janus Kinase 2 Inhibitors

Simeon

Jongkon

2019

Molecules

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Janus kinase 2 (JAK2) inhibitors represent a promising therapeutic class of anticancer agents against many myeloproliferative disorders. Bioactivity data on pIC50 of 2229 JAK2 inhibitors were employed in the construction of quantitative structure-activity relationship (QSAR) models. The models were built from 100 data splits using decision tree (DT), support vector machine (SVM), deep neural network (DNN) and random forest (RF). The predictive power of RF models were assessed via 10-fold cross validation, which afforded excellent predictive performance with R2 and RMSE of 0.74 ± 0.05 and 0.63 ± 0.05, respectively. Moreover, test set has excellent performance of R2 (0.75 ± 0.03) and RMSE (0.62 ± 0.04). In addition, Y-scrambling was utilized to evaluate the possibility of chance correlation of the predictive model. A thorough analysis of the substructure fingerprint count was conducted to provide insights on the inhibitory properties of JAK2 inhibitors. Molecular cluster analysis revealed that pyrazine scaffolds have nanomolar potency against JAK2.

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Computational identification of JAK2 inhibitors: a combined pharmacophore mapping and molecular docking approach

Cited by 5 publications

References 32 publications

Identification of Potent and Selective JAK1 Lead Compounds Through Ligand-Based Drug Design Approaches

Identification of Potent and Selective JAK1 Lead Compounds Through Ligand-Based Drug Design Approaches

Designing JAK2 Inhibitors Beyond Myeloproliferative Neoplasms ‐ Theoretical and Experimental Analysis for Solid Cancers

Construction of Quantitative Structure Activity Relationship (QSAR) Models to Predict Potency of Structurally Diversed Janus Kinase 2 Inhibitors

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