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

Abstract

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.

In silico characterization of aryl benzoyl hydrazide derivatives as potential inhibitors of RdRp enzyme of H5N1 influenza virus

“…Furthermore, the molecular mechanics generalized born surface area (MM-GBSA) ( Srinivasan et al, 1998 ) binding free energies of the protein-ligand complexes were calculated using the MM-PBSA.py tool of Amber and by employing 100 snapshots taken from the last 10 ns of the MD trajectory. A detailed and in-depth description and discussion of the MM-GBSA analysis applied here can be found in our recent work ( Halder and Cordeiro, 2021b ). However, the entropic contribution (T∆S) is not calculated since it is computationally expensive for large protein complexes and its accuracy may not always be ascertained ( Berishvili et al, 2020 ; Halder and Cordeiro, 2021b ).…”

“…A detailed and in-depth description and discussion of the MM-GBSA analysis applied here can be found in our recent work ( Halder and Cordeiro, 2021b ). However, the entropic contribution (T∆S) is not calculated since it is computationally expensive for large protein complexes and its accuracy may not always be ascertained ( Berishvili et al, 2020 ; Halder and Cordeiro, 2021b ).…”

Computer aided designing of novel pyrrolopyridine derivatives as JAK1 inhibitors

“…The g-mmpbsa method implemented by Kumari et al 25 does not consider the entropy term hence, the methods can only give the relative BE. Though there are several methods to calculate the entropy term 26 , 27 , these methods are time-consuming and the magnitude of the standard error is high. Hence, the binding energy results presented in the study are considered as relative binding energy values.…”

PI3K/AKT/mTOR-Targeted Therapy for Breast Cancer

“…There are different kinds of AKT inhibitors. Pan-AKT inhibitors bind to the ATP pocket of AKT1/2/3 and suppress their activity [ 97 ] ( Table 3 ). Another kind of AKT inhibitor is allosteric inhibitor [ 98 ].…”