Exploring the selectivity of PI3Kα and mTOR inhibitors by 3D-QSAR, molecular dynamics simulations and MM/GBSA binding free energy decomposition

Wu, Feng; Hou, Xueyan; Luo, Hao; Zhang, Wenjuan; Ding, Zhenyu; Li, Rui

doi:10.1039/c3md00157a

Cited by 11 publications

(10 citation statements)

References 40 publications

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“…So far, a number of different ligand-based in silico methods and protocols have been employed by different researchers for the discovery of PI3K inhibitors, and these include 2D-quantitative structure–activity relationship (2D-QSAR) modelling, 3D-QSAR, 3D-pharmacophore mapping, etc. [32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49]. However, all these methods suffer from at least one of the following shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool

Halder

Cordeiro

2019

IJMS

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The present work aims at establishing multi-target chemometric models using the recently launched quantitative structure–activity relationship (QSAR)-Co tool for predicting the activity of inhibitor compounds against different isoforms of phosphoinositide 3-kinase (PI3K) under various experimental conditions. The inhibitors of class I phosphoinositide 3-kinase (PI3K) isoforms have emerged as potential therapeutic agents for the treatment of various disorders, especially cancer. The cell-based enzyme inhibition assay results of PI3K inhibitors were curated from the CHEMBL database. Factors such as the nature and mutation of cell lines that may significantly alter the assay outcomes were considered as important experimental elements for mt-QSAR model development. The models, in turn, were developed using two machine learning techniques as implemented in QSAR-Co: linear discriminant analysis (LDA) and random forest (RF). Both techniques led to models with high accuracy (ca. 90%). Several molecular fragments were extracted from the current dataset, and their quantitative contributions to the inhibitory activity against all the proteins and experimental conditions under study were calculated. This case study also demonstrates the utility of QSAR-Co tool in solving multi-factorial and complex chemometric problems. Additionally, the combination of different in silico methods employed in this work can serve as a valuable guideline to speed up early discovery of PI3K inhibitors.

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Section: Introductionmentioning

confidence: 99%

Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool

Halder

Cordeiro

2019

IJMS

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“…QSSR modeling and prediction of ligand selectivity between different protein targets has been limitedly reported previously . Here, we used the QSSR method to characterize inhibitor selectivity between PI3K α and PI3K β and to explore kinase's residue contribution to the selectivity.…”

Section: Resultsmentioning

confidence: 99%

Quantitative structure‐selectivity relationship (QSSR)‐based molecular insight into the cross‐reactivity and specificity of chemotherapeutic inhibitors between PI3Kα and PI3Kβ

Ding

Liu

Zhu

2017

Journal of Chemometrics

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Selective inhibition of phosphoinositide 3-kinase (PI3K) isoforms α and β with small-molecule inhibitors can result in distinct biological effects on anticancer chemotherapy. However, many existing PI3K inhibitors have moderate or high promiscuity and cross-reactivity between the 2 kinase isoforms. Here, a quantitative structure-selectivity relationship-based statistical modeling scheme was used to characterize the relative contribution of independent kinase residues to inhibitor selectivity and to predict the selectivity and specificity for existing PI3K inhibitors. It is found that the residue type and distribution of kinase's active site play an important role in inhibitor selectivity, while rest of the kinase may contribute to the selectivity through long-range chemical interactions and indirect allosteric effect. The selectivity is also determined by the configuration difference between PI3Kα and PI3Kβ kinase domains. Larger inhibitor compounds have lower binding potency to PI3Kβ than PI3Kα and thus possess higher selectivity for PI3Kα over PI3Kβ.KEYWORDS chemotherapeutic agent, inhibitor selectivity, phosphoinositide 3-kinase, quantitative structure-selectivity relationship, statistical modeling

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“…The side chain of Met-2345part of the N-lobe opposite to adenine binding pocket and Ile-2356 part of hydrophobic adenine site were involved in hydrophobic interactions with pyrido-pyrimidine group. AZD2014 was engaged in bonding with Leu-2185 another residue unique to mTOR [24,38]. Additional bond of morpholine moiety with Ile-2163 and Cys-2243 was also observed.…”

Section: Docking Analysis Of Azd 2014mentioning

confidence: 91%

Molecular Docking and Pharmacokinetic of Highly Specific Novel Pan-Mtor Inhibitors against Solid Tumors

Naveed¹

2017

MOJPB

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Mechanistic/mammalian target of rapamycin (mTOR) a serine/threonine kinase belonging to PI3K/Akt/mTOR pathway is involved in different cellular functions cell survival, metabolism, growth, proliferation, apoptosis and autophagy. PanmTOR inhibitors are targeted towards mTOR dysregulation, inhibiting the kinase domain of both mTORC1 and mTORC2. The present study analyzes the binding modes and molecular interactions of highly specific mTOR inhibitors, AZD8055 and its sister compoundAZD2014using computational approach. Both inhibitors proved to be effective against solid tumors in vitro and in vivo. Docking analysis was performed using Auto Dock Vina, conformations were scored based upon their binding energy (kcal/mol) and illustrated using Discovery Studio Visualizer 4.5 version. Inhibitors fit between N-and C-lobes of mTOR kinase domain into the inner hydrophobic core. The results indicated interactions with distinctive mTOR residues Trp-2239, Leu-2185 and newly developed interactions with Asp-2375 for AZD2014 and with Ala-2248, His-2247, Thr-2245 for AZD8055. The binding pattern of both inhibitors was slightly different, responsible for better pharmacokinetic profile of AZD2014 and 5 fold increase in efficacy of AZD8055. The binding energy of best docking score for AZD8055 was -8.0 kcal/mol however AZD20144 showed best binding affinity at -8.2kcal/mol (RMSD l.b. 0.908, RMSD u.b. 1.075). Highly specific, less toxic and potent inhibitors can be designed or optimized based upon the docking results.

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Exploring the selectivity of PI3Kα and mTOR inhibitors by 3D-QSAR, molecular dynamics simulations and MM/GBSA binding free energy decomposition

Cited by 11 publications

References 40 publications

Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool

Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool

Quantitative structure‐selectivity relationship (QSSR)‐based molecular insight into the cross‐reactivity and specificity of chemotherapeutic inhibitors between PI3Kα and PI3Kβ

Molecular Docking and Pharmacokinetic of Highly Specific Novel Pan-Mtor Inhibitors against Solid Tumors

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