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

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 Structu...

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Autores principales: Halder, Amit Kumar, Cordeiro, M. Natália D. S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070654/
https://www.ncbi.nlm.nih.gov/pubmed/33920446
http://dx.doi.org/10.3390/ijms22083944
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author Halder, Amit Kumar
Cordeiro, M. Natália D. S.
author_facet Halder, Amit Kumar
Cordeiro, M. Natália D. S.
author_sort Halder, Amit Kumar
collection PubMed
description 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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spelling pubmed-80706542021-04-26 AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery Halder, Amit Kumar Cordeiro, M. Natália D. S. Int J Mol Sci Article 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. MDPI 2021-04-11 /pmc/articles/PMC8070654/ /pubmed/33920446 http://dx.doi.org/10.3390/ijms22083944 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Halder, Amit Kumar
Cordeiro, M. Natália D. S.
AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery
title AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery
title_full AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery
title_fullStr AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery
title_full_unstemmed AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery
title_short AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery
title_sort akt inhibitors: the road ahead to computational modeling-guided discovery
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070654/
https://www.ncbi.nlm.nih.gov/pubmed/33920446
http://dx.doi.org/10.3390/ijms22083944
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