Ligand-based modeling of Akt3 lead to potent dual Akt1/Akt3 inhibitor

Al-Sha’er, Mahmoud A.; Taha, Mutasem O.

doi:10.1016/j.jmgm.2018.02.001

Cited by 10 publications

(5 citation statements)

References 63 publications

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“…This discrepancy is explainable only by advantageous entropic binding contributions that enhance the affinity of the potent AC member (i. e., C19 ). Incidentally, calculating such entropic discrepancies is rather complicated because the gross protein conformational changes that accompany entropy‐driven ligand binding [25a,34a–e] require timescales of tens of microseconds of molecular dynamics simulation [34f] …”

Section: Resultsmentioning

confidence: 99%

“…This protocol defines ligand‐receptor binding interactions and translates them into pharmacophore models [21] . The resulting pharmacophores were fitted against the collected list compounds ( 1 – 109 , Table S1), clustered and allowed to compete within QSAR context [15j,25a,29a,39] . Genetic function algorithm (GFA) was used to select different combinations of pharmacophores and molecular descriptors calculated for the collected compounds.…”

Section: Resultsmentioning

confidence: 99%

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Pharmacophore Modeling of Targets Infested with Activity CliffsviaMolecular Dynamics Simulation Coupled with QSAR and Comparison with other Pharmacophore Generation Methods: KDR as Case Study

Abudayah

Daoud

Al-Sha’er

et al. 2022

Molecular Informatics

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Activity cliffs (ACs) are defined as pairs of structurally similar compounds with large difference in their potencies against certain biotarget. We recently proposed that potent AC members induce significant entropically‐driven conformational modifications of the target that unveil additional binding interactions, while their weakly‐potent counterparts are enthalpically‐driven binders with little influence on the protein target. We herein propose to extract pharmacophores for ACs‐infested target(s) from molecular dynamics (MD) frames of purely “enthalpic” potent binder(s) complexed within the particular target. Genetic function algorithm/machine learning (GFA/ML) can then be employed to search for the best possible combination of MD pharmacophore(s) capable of explaining bioactivity variations within a list of inhibitors. We compared the performance of this approach with established ligand‐based and structure‐based methods. Kinase inserts domain receptor (KDR) was used as a case study. KDR plays a crucial role in angiogenic signalling and its inhibitors have been approved in cancer treatment. Interestingly, GFA/ML selected, MD‐based, pharmacophores were of comparable performances to ligand‐based and structure‐based pharmacophores. The resulting pharmacophores and QSAR models were used to capture hits from the national cancer institute list of compounds. The most active hit showed anti‐KDR IC50 of 2.76 μM.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Pharmacophore Modeling of Targets Infested with Activity CliffsviaMolecular Dynamics Simulation Coupled with QSAR and Comparison with other Pharmacophore Generation Methods: KDR as Case Study

Abudayah

Daoud

Al-Sha’er

et al. 2022

Molecular Informatics

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“…The pharmacophoric space of LSD-1 inhibitors was extensively explored using supervised ligand-based pharmacophore modelling. 63,73,75,112 For this purpose, 8 training subsets were carefully selected from collected LSD-1 inhibitors bioassayed by similar procedures ( 1 – 198 , ESI Table SM1†). Each training subset was selected in such a way to conform with certain envisaged binding mode.…”

Section: Resultsmentioning

confidence: 99%

“…17–20 Despite these efforts, there are still no approved LSD-1 inhibitors in the clinical practice until now. 21 The continued interest in LSD-1 prompted us to combine our innovative pharmacophore modelling methods 39,40,42,51,55,59–63,65,66,69,73,74,76,96,101,105,106,108,109,112 with machine learning methodologies towards the discovery of new LSD-1 inhibitors. The fundamental novelty of the current project lies in allowing numerous pharmacophore models (of ligand- and structure-based origins) and physicochemical descriptors to compete within genetic algorithm (GA)/machine learning (ML) context for the selection of optimal combination of pharmacophore(s) and physicochemical descriptors within self-consistent and predictive ML model(s).…”

Section: Introductionmentioning

confidence: 99%

Discovery of new potent lysine specific histone demythelase-1 inhibitors (LSD-1) using structure based and ligand based molecular modelling and machine learning

2022

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“…The best predictive linear and non-linear models were then used to screen a focused kinase library to obtain the most potential virtual hits that were further investigated by structure-based methods, such as pharmacophore-based prediction, docking and molecular dynamics (MD) simulation techniques. Even though several computational modeling works targeting AKT inhibitors have been reported so far, these were always focused only on one subtype of AKT pertaining to one experimental assay condition [27][28][29][30][31][32][33]. To the best of our knowledge, the current work is the first one to report multi-target computational modeling-guided discovery of inhibitors for all three AKT isoforms assayed under multiple experimental assay conditions.…”

Section: Introductionmentioning

confidence: 99%

AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery

Halder

Cordeiro

2021

IJMS

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AKT, is a serine/threonine protein kinase comprising three isoforms—namely: AKT1, AKT2 and AKT3, whose inhibitors have been recognized as promising therapeutic targets for various human disorders, especially cancer. In this work, we report a systematic evaluation of multi-target Quantitative Structure-Activity Relationship (mt-QSAR) models to probe AKT’ inhibitory activity, based on different feature selection algorithms and machine learning tools. The best predictive linear and non-linear mt-QSAR models were found by the genetic algorithm-based linear discriminant analysis (GA-LDA) and gradient boosting (Xgboost) techniques, respectively, using a dataset containing 5523 inhibitors of the AKT isoforms assayed under various experimental conditions. The linear model highlighted the key structural attributes responsible for higher inhibitory activity whereas the non-linear model displayed an overall accuracy higher than 90%. Both these predictive models, generated through internal and external validation methods, were then used for screening the Asinex kinase inhibitor library to identify the most potential virtual hits as pan-AKT inhibitors. The virtual hits identified were then filtered by stepwise analyses based on reverse pharmacophore-mapping based prediction. Finally, results of molecular dynamics simulations were used to estimate the theoretical binding affinity of the selected virtual hits towards the three isoforms of enzyme AKT. Our computational findings thus provide important guidelines to facilitate the discovery of novel AKT inhibitors.

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Ligand-based modeling of Akt3 lead to potent dual Akt1/Akt3 inhibitor

Cited by 10 publications

References 63 publications

Pharmacophore Modeling of Targets Infested with Activity CliffsviaMolecular Dynamics Simulation Coupled with QSAR and Comparison with other Pharmacophore Generation Methods: KDR as Case Study

Pharmacophore Modeling of Targets Infested with Activity CliffsviaMolecular Dynamics Simulation Coupled with QSAR and Comparison with other Pharmacophore Generation Methods: KDR as Case Study

Discovery of new potent lysine specific histone demythelase-1 inhibitors (LSD-1) using structure based and ligand based molecular modelling and machine learning

AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery

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