Evaluation of Artificial Intelligence in Participating Structure-Based Virtual Screening for Identifying Novel Interleukin-1 Receptor Associated Kinase-1 Inhibitors

Che, Jinxin; Feng, Ruiwei; Gao, Jian; Yu, Hongyun; Weng, Qinjie; He, Qiaojun; Dong, Xiaowu; Wu, Jian; Yang, Bo

doi:10.3389/fonc.2020.01769

Cited by 17 publications

(14 citation statements)

References 32 publications

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“…Similarly, many compounds were reported for their interactions with catalytic site residues such as Leu347, Phe290, Phe294, Ala237, Asp358, Lys239, Ile218. [50,51] Besides, high toxicity value (below 10 μM) on MCF7 cells overexpressing IRAK1 was observed in the reported study. In one of the MD simulation studies reported, Lys239 residue was found as more stable due to its contribution to the complex formation.…”

Section: Ligandsmentioning

confidence: 68%

“…Besides, high toxicity value (below 10 μM) on MCF7 cells overexpressing IRAK1 was observed in the reported study. In one of the MD simulation studies reported, Lys239 residue was found as more stable due to its contribution to the complex formation [50] …”

Section: Resultsmentioning

confidence: 98%

“…In one of the MD simulation studies reported, Lys239 residue was found as more stable due to its contribution to the complex formation. [50] As a result, the protein-ligand complexes and binding abilities showed that these nitro compounds may be evaluated as potential candidates against IRAK family members.…”

Section: Ligandsmentioning

confidence: 99%

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Nitro Compounds Inhibit Breast Cancer Cell Proliferation, Migration, and Colony Formation: Molecular Docking, Molecular Dynamics Simulations and Pharmacological Properties

Comert Onder,

Sahin,

Davutlar

et al. 2023

ChemistrySelect

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In this study, the concept of combined in vitro and in silico studies were utilized by using some synthesized nitro bearing compounds. The anticancer activities of the studied compounds were performed by using colony formation analysis, cell cytotoxicity, and migration. Nitro group containing two compounds were analyzed using Hoechst staining to indicate the morphological changes on the nuclei of cancer cells under fluorescence microscopy. Scanning electron microscopy (SEM) analysis was performed with N,N‐dibutyl‐nitro‐substituted compound. Nitro containing anticancer agents were shown the inhibition at 1.5 μM and 2 μM concentrations, and nuclear apoptosis was detected. In addition, cell‐to‐cell interaction on MDA‐MB‐231 cells was broken and observed morphologic changes following the treatment with N,N‐dibutyl‐nitro‐substituted compound at the effective doses. In general, the other nitro compounds showed cell cytotoxicity at 5 μM, 10 μM, and 20 μM. Two hits as anticancer agents were determined as potential interleukin‐1 receptor‐associated kinase 1 (IRAK1) and interleukin‐1 receptor‐associated kinase 4 (IRAK4) inhibitor candidates. Molecular docking and molecular dynamics (MD) simulations studies will provide that the binding patterns with specific residues such as Met265, Tyr284 of the IRAK family members, and these will contribute to further in vitro and in vivo studies for targeted breast cancer therapy.

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Section: Ligandsmentioning

confidence: 68%

Section: Resultsmentioning

confidence: 98%

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Nitro Compounds Inhibit Breast Cancer Cell Proliferation, Migration, and Colony Formation: Molecular Docking, Molecular Dynamics Simulations and Pharmacological Properties

Comert Onder,

Sahin,

Davutlar

et al. 2023

ChemistrySelect

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show abstract

“…To enrich our collection of putative UHM binders, we selected molecules showing similarity to either C5 or C12 using an online similarity search tool ( molport : https://www.molport.com). Seventeen additional chemicals were selected, with a Tanimoto similarity score above 0.8 [30] (C13 to C29, Fig. S1).…”

Section: Resultsmentioning

confidence: 99%

Identification of a small molecule splicing inhibitor targeting UHM domains

et al. 2021

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Splicing factor mutations are frequent in myeloid neoplasms, blood cancers, and solid tumors. Cancer cells harboring these mutations present a particular vulnerability to drugs that target splicing factors such as SF3b155 or CAPERα. Still, the arsenal of chemical probes that target the spliceosome is very limited. U2AF homology motifs (UHMs) are common protein interaction domains among splicing factors. They present a hydrophobic pocket ideally suited to anchor small molecules with the aim to inhibit protein-protein interaction. Here, we combined a virtual screening of a small molecules database and an in vitro competition assay and identified a small molecule, we named UHMCP1 that prevents the SF3b155/U2AF 65 interaction. NMR analyses and molecular dynamics simulations confirmed the binding of this molecule in the hydrophobic pocket of the U2AF 65 UHM domain. We further provide evidence that UHMCP1 impacts RNA splicing and cell viability and is therefore an interesting novel compound targeting an UHM domain with potential anticancer properties.

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“…Two potential inhibitors were eventually identified through an in vitro kinase assay [ 10 ]. Che et al significantly improved the performance of molecular docking and pharmacophore-based virtual screening using the AI method, which not only reduced the number of false positive compounds but also successfully identified four active interleukin-1 receptor associated kinase-1 (IRAK1) inhibitors [ 11 ]. However, to our knowledge, there is no relevant report on the discovery of natural products targeting JNK1 by integrating computational strategies.…”

Section: Introductionmentioning

confidence: 99%

Discovery of Novel c-Jun N-Terminal Kinase 1 Inhibitors from Natural Products: Integrating Artificial Intelligence with Structure-Based Virtual Screening and Biological Evaluation

2022

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c-Jun N-terminal kinase 1 (JNK1) is currently considered a critical therapeutic target for type-2 diabetes. In recent years, there has been a great interest in naturopathic molecules, and the discovery of active ingredients from natural products for specific targets has received increasing attention. Based on the above background, this research aims to combine emerging Artificial Intelligence technologies with traditional Computer-Aided Drug Design methods to find natural products with JNK1 inhibitory activity. First, we constructed three machine learning models (Support Vector Machine, Random Forest, and Artificial Neural Network) and performed model fusion based on Voting and Stacking strategies. The integrated models with better performance (AUC of 0.906 and 0.908, respectively) were then employed for the virtual screening of 4112 natural products in the ZINC database. After further drug-likeness filtering, we calculated the binding free energy of 22 screened compounds using molecular docking and performed a consensus analysis of the two methodologies. Subsequently, we identified the three most promising candidates (Lariciresinol, Tricin, and 4′-Demethylepipodophyllotoxin) according to the obtained probability values and relevant reports, while their binding characteristics were preliminarily explored by molecular dynamics simulations. Finally, we performed in vitro biological validation of these three compounds, and the results showed that Tricin exhibited an acceptable inhibitory activity against JNK1 (IC50 = 17.68 μM). This natural product can be used as a template molecule for the design of novel JNK1 inhibitors.

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Evaluation of Artificial Intelligence in Participating Structure-Based Virtual Screening for Identifying Novel Interleukin-1 Receptor Associated Kinase-1 Inhibitors

Cited by 17 publications

References 32 publications

Nitro Compounds Inhibit Breast Cancer Cell Proliferation, Migration, and Colony Formation: Molecular Docking, Molecular Dynamics Simulations and Pharmacological Properties

Nitro Compounds Inhibit Breast Cancer Cell Proliferation, Migration, and Colony Formation: Molecular Docking, Molecular Dynamics Simulations and Pharmacological Properties

Identification of a small molecule splicing inhibitor targeting UHM domains

Discovery of Novel c-Jun N-Terminal Kinase 1 Inhibitors from Natural Products: Integrating Artificial Intelligence with Structure-Based Virtual Screening and Biological Evaluation

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