Computer-Aided Drug Design for AMP-Activated Protein Kinase Activators

Wang, Zhanli; Huo, Jianxin; Sun, Lidan; Wang, Yongfu; Jin, Hongwei; Yu, Hui; Zhang, Liangren; Zhou, Lishe

doi:10.2174/157340911796504323

Cited by 7 publications

(4 citation statements)

References 118 publications

(137 reference statements)

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“…In the literature (PubMed search with terms "AMPK" and "QSAR"), several QSAR models for prediction of AMPK activation have been published so far [24][25][26][27][28][29][30]. However, all of these models used pharmacophore docking analyses or homology models or structure-, ligand-, or fragment-based design and focused exclusively on direct AMPK activators.…”

Section: Discussionmentioning

confidence: 99%

Modeling Structure–Activity Relationship of AMPK Activation

Drewe

Küsters²,

Hammann

et al. 2021

Molecules

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The adenosine monophosphate activated protein kinase (AMPK) is critical in the regulation of important cellular functions such as lipid, glucose, and protein metabolism; mitochondrial biogenesis and autophagy; and cellular growth. In many diseases—such as metabolic syndrome, obesity, diabetes, and also cancer—activation of AMPK is beneficial. Therefore, there is growing interest in AMPK activators that act either by direct action on the enzyme itself or by indirect activation of upstream regulators. Many natural compounds have been described that activate AMPK indirectly. These compounds are usually contained in mixtures with a variety of structurally different other compounds, which in turn can also alter the activity of AMPK via one or more pathways. For these compounds, experiments are complicated, since the required pure substances are often not yet isolated and/or therefore not sufficiently available. Therefore, our goal was to develop a screening tool that could handle the profound heterogeneity in activation pathways of the AMPK. Since machine learning algorithms can model complex (unknown) relationships and patterns, some of these methods (random forest, support vector machines, stochastic gradient boosting, logistic regression, and deep neural network) were applied and validated using a database, comprising of 904 activating and 799 neutral or inhibiting compounds identified by extensive PubMed literature search and PubChem Bioassay database. All models showed unexpectedly high classification accuracy in training, but more importantly in predicting the unseen test data. These models are therefore suitable tools for rapid in silico screening of established substances or multicomponent mixtures and can be used to identify compounds of interest for further testing.

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Section: Discussionmentioning

confidence: 99%

Modeling Structure–Activity Relationship of AMPK Activation

Drewe

Küsters²,

Hammann

et al. 2021

Molecules

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“…Most importantly, computer aided drug design (CADD) successfully implemented in diverse situation. Computer‐aided drug design produces numerous experimental advantages: (i) The administered doses able to optimized for simple and slight 24 ; (ii) reducing of drug interactions and better safety and efficacy 25 ; (iii) successful prevention of resistance in target proteins 26 ; (iv) using structure‐based drug design through computational studies have inferior expenditure of money and time 27,28 . These CADD strategies have shown great promise in identifying bioactive molecules for anticancer and antiviral activities 29,30 .…”

Section: Introductionmentioning

confidence: 99%

“…Representatives of benzimidazole derivatives containing triazole moiety (I-III) as antibacterial agents money and time. 27,28 These CADD strategies have shown great promise in identifying bioactive molecules for anticancer and antiviral activities. 29,30 Recently, we have identified the chemical constituents of Tinospora Cordifolia as possible inhibitors of COVID-19 targets via computational approach.…”

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confidence: 99%

Exploration of potential inhibitors for tuberculosis via structure‐based drug design, molecular docking, and molecular dynamics simulation studies

2021

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Drug resistance in tuberculosis is major threat to human population. In the present investigation, we aimed to identify novel and potent benzimidazole molecules to overcome the resistance management. A series of 20 benzimidazole derivatives were examined for its activity as selective antitubercular agents. Initially, AutodockVina algorithm was performed to assess the efficacy of the molecules. The results are further enriched by redocking by means of Glide algorithm. The binding free energies of the compounds were then calculated by MM‐generalized‐born surface area method. Molecular docking studies elucidated that benzimidazole derivatives has revealed formation of hydrogen bond and strong binding affinity in the active site of Mycobacterium tuberculosis protein. Note that ARG308, GLY189, VAL312, LEU403, and LEU190 amino acid residues of Mycobacterium tuberculosis protein PrpR are involved in binding with ligands of benzimidazoles. Interestingly, the ligands exhibited same binding potential to the active site of protein complex PrpR in both the docking programs. In essence, the result portrays that benzimidazole derivatives such as 1p, 1q, and 1 t could be potent and selective antitubercular agents than the standard drug isoniazid. These compounds were then subjected to molecular dynamics simulation to validate the dynamics activity of the compounds against PrpR. Finally, the inhibitory behavior of compounds was predicted using a machine learning algorithm trained on a data collection of 15,000 compounds utilizing graph‐based signatures. Overall, the study concludes that designed benzimidazoles can be employed as antitubercular agents. Indeed, the results are helpful for the experimental biologists to develop safe and non‐toxic drugs against tuberculosis.

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“…In addition to cell metabolism, recent evidence suggests that AMPK plays an intricate role in regulating cancer cell proliferation, survival, and invasive phenotype by targeting multiple oncogenic signaling pathways, including those mediated by Akt, mTOR, and MDM2 . Consequently, the past few years have witnessed the development of a number of novel AMPK activators as discussed in several reviews, including A‐769662, PT‐1, OSU‐53 ( 1 ; Figure ), ( E )‐3‐((3‐[(4‐chlorophenyl)(phenyl)methylene]‐2‐oxoindolin‐1‐yl)methyl)benzoic acid, and 991 . Mechanistically, these direct activators circumvent the dependence on liver kinase B1 (LKB1), an upstream kinase of AMPK under conditions of energy stress, which is often inactivated in cancer cells through mutations in the course of tumorigenesis …”

Section: Introductionmentioning

confidence: 99%

Development of Potent Adenosine Monophosphate Activated Protein Kinase (AMPK) Activators

et al. 2015

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Previously, we reported the identification of a thiazolidinedione-based adenosine monophosphate-activated protein kinase (AMPK) activator, 1 (N-[4-({3-[(1-methylcyclohexyl)methyl]-2,4-dioxothiazolidin-5-ylidene}methyl)phenyl]-4-nitro-3-(trifluoromethyl)benzenesulfonamide), which provided a proof-of-concept to delineate the intricate role of AMPK in regulating oncogenic signaling pathways associated with cell proliferation and epithelial-mesenchymal transition (EMT) in cancer cells. In this study, we used 1 as a scaffold to conduct lead optimization, which generated a series of derivatives. Analysis of the anti-proliferative and AMPK-activating activities of individual derivatives revealed a distinct structure-activity relationship and identified 59 (N-(3-Nitrophenyl)-N'-{4-[(3-{[3,5-bis(trifluoromethyl)phenyl]methyl}-2,4-dioxo-5-thiazolidinylidene)methyl]phenyl}-urea) as the optimal agent. Relative to 1, 59 exhibited multifold higher potency in upregulating AMPK phosphorylation in multiple cell lines irrespective of their liver kinase B1 (LKB1) functional status, accompanied by parallel changes in the phosphorylation/expression levels of p70S6K, Akt, Foxo3a, and EMT-associated markers. Consistent with its predicted activity against tumors with activated Akt status, oral 59 was efficacious in suppressing the growth of phosphatase and tensin homolog (PTEN)-null PC-3 xenograft tumors in nude mice. Together, these findings suggest that 59 has clinical value in therapeutic strategies for PTEN-negative cancer and warrants continued investigation in this regard.

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Computer-Aided Drug Design for AMP-Activated Protein Kinase Activators

Cited by 7 publications

References 118 publications

Modeling Structure–Activity Relationship of AMPK Activation

Modeling Structure–Activity Relationship of AMPK Activation

Exploration of potential inhibitors for tuberculosis via structure‐based drug design, molecular docking, and molecular dynamics simulation studies

Development of Potent Adenosine Monophosphate Activated Protein Kinase (AMPK) Activators

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