Elucidation of binding mode and three dimensional quantitative structure–activity relationship studies of a novel series of protein kinase B/Akt inhibitors

Muddassar, Muhammad; Pasha, Farhan Ahmad; Neaz, M. M.; Saleem, Yasar; Cho, S. J.

doi:10.1007/s00894-008-0416-7

Cited by 10 publications

(4 citation statements)

References 30 publications

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“…By changing the substituent at main scaffold, the activity of the compounds predicted by the developed models, was affected. The quality of the models is affected by multiple factors like the conformation of the molecules and their assigned partial charges ( Muddassar et al, 2009 ; Wang et al, 2015 ). Therefore, different conformations of dataset molecules using ligand and structure-based approaches were generated along with different charge models i.e., GH, GM, MMFF.…”

Section: Resultsmentioning

confidence: 99%

Combined 3D-QSAR, molecular docking and dynamics simulations studies to model and design TTK inhibitors

Ashraf

Asari²,

Yousaf³

et al. 2022

Front. Chem.

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Tyrosine threonine kinase (TTK) is the key component of the spindle assembly checkpoint (SAC) that ensures correct attachment of chromosomes to the mitotic spindle and thereby their precise segregation into daughter cells by phosphorylating specific substrate proteins. The overexpression of TTK has been associated with various human malignancies, including breast, colorectal and thyroid carcinomas. TTK has been validated as a target for drug development, and several TTK inhibitors have been discovered. In this study, ligand and structure-based alignment as well as various partial charge models were used to perform 3D-QSAR modelling on 1H-Pyrrolo[3,2-c] pyridine core containing reported inhibitors of TTK protein using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches to design better active compounds. Different statistical methods i.e., correlation coefficient of non-cross validation (r2), correlation coefficient of leave-one-out cross-validation (q2), Fisher’s test (F) and bootstrapping were used to validate the developed models. Out of several charge models and alignment-based approaches, Merck Molecular Force Field (MMFF94) charges using structure-based alignment yielded highly predictive CoMFA (q2 = 0.583, Predr2 = 0.751) and CoMSIA (q2 = 0.690, Predr2 = 0.767) models. The models exhibited that electrostatic, steric, HBA, HBD, and hydrophobic fields play a key role in structure activity relationship of these compounds. Using the contour maps information of the best predictive model, new compounds were designed and docked at the TTK active site to predict their plausible binding modes. The structural stability of the TTK complexes with new compounds was confirmed using MD simulations. The simulation studies revealed that all compounds formed stable complexes. Similarly, MM/PBSA method based free energy calculations showed that these compounds bind with reasonably good affinity to the TTK protein. Overall molecular modelling results suggest that newly designed compounds can act as lead compounds for the optimization of TTK inhibitors.

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Section: Resultsmentioning

confidence: 99%

Combined 3D-QSAR, molecular docking and dynamics simulations studies to model and design TTK inhibitors

Ashraf

Asari²,

Yousaf³

et al. 2022

Front. Chem.

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“…Different CoMFA- and CoMSIA-based 3D-QSAR models were generated using partial least square method (PLS) by correlating biological activities of BRD4 inhibitors in a training dataset with their field descriptors. There are several factors that affect the quality of the developed CoMFA and CoMSIA models [ 18 ]. However, the alignment of the dataset molecule and the charges assigned to them are the two major factors that affect the predictability of the generated models [ 19 ].…”

Section: Resultsmentioning

confidence: 99%

Investigations of Structural Requirements for BRD4 Inhibitors through Ligand- and Structure-Based 3D QSAR Approaches

et al. 2018

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The bromodomain containing protein 4 (BRD4) recognizes acetylated histone proteins and plays numerous roles in the progression of a wide range of cancers, due to which it is under intense investigation as a novel anti-cancer drug target. In the present study, we performed three-dimensional quantitative structure activity relationship (3D-QSAR) molecular modeling on a series of 60 inhibitors of BRD4 protein using ligand- and structure-based alignment and different partial charges assignment methods by employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches. The developed models were validated using various statistical methods, including non-cross validated correlation coefficient (r2), leave-one-out (LOO) cross validated correlation coefficient (q2), bootstrapping, and Fisher’s randomization test. The highly reliable and predictive CoMFA (q2 = 0.569, r2 = 0.979) and CoMSIA (q2 = 0.500, r2 = 0.982) models were obtained from a structure-based 3D-QSAR approach using Merck molecular force field (MMFF94). The best models demonstrate that electrostatic and steric fields play an important role in the biological activities of these compounds. Hence, based on the contour maps information, new compounds were designed, and their binding modes were elucidated in BRD4 protein’s active site. Further, the activities and physicochemical properties of the designed molecules were also predicted using the best 3D-QSAR models. We believe that predicted models will help us to understand the structural requirements of BRD4 protein inhibitors that belong to quinolinone and quinazolinone classes for the designing of better active compounds.

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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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Elucidation of binding mode and three dimensional quantitative structure–activity relationship studies of a novel series of protein kinase B/Akt inhibitors

Cited by 10 publications

References 30 publications

Combined 3D-QSAR, molecular docking and dynamics simulations studies to model and design TTK inhibitors

Combined 3D-QSAR, molecular docking and dynamics simulations studies to model and design TTK inhibitors

Investigations of Structural Requirements for BRD4 Inhibitors through Ligand- and Structure-Based 3D QSAR Approaches

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

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