Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases

The inhibitors of two isoforms of mitogen-activated protein kinase-interacting kinases (i.e., MNK-1 and MNK-2) are implicated in the treatment of a number of diseases including cancer. This work reports, for the first time, a multi-target (or multi-tasking) in silico modeling approach (mt-QSAR) for...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Amit Kumar Halder, M. Natália D. S. Cordeiro
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	MNK-1 and MNK-2 inhibitors mt-QSAR modeling virtual screening Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/4f3b2261f80d47d98c9b3ee756e03750
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:4f3b2261f80d47d98c9b3ee756e03750
record_format	dspace
spelling	oai:doaj.org-article:4f3b2261f80d47d98c9b3ee756e037502021-11-25T16:53:37ZMulti-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases10.3390/biom111116702218-273Xhttps://doaj.org/article/4f3b2261f80d47d98c9b3ee756e037502021-11-01T00:00:00Zhttps://www.mdpi.com/2218-273X/11/11/1670https://doaj.org/toc/2218-273XThe inhibitors of two isoforms of mitogen-activated protein kinase-interacting kinases (i.e., MNK-1 and MNK-2) are implicated in the treatment of a number of diseases including cancer. This work reports, for the first time, a multi-target (or multi-tasking) in silico modeling approach (mt-QSAR) for probing the inhibitory potential of these isoforms against MNKs. Linear and non-linear mt-QSAR classification models were set up from a large dataset of 1892 chemicals tested under a variety of assay conditions, based on the Box–Jenkins moving average approach, along with a range of feature selection algorithms and machine learning tools, out of which the most predictive one (>90% overall accuracy) was used for mechanistic interpretation of the likely inhibition of MNK-1 and MNK-2. Considering that the latter model is suitable for virtual screening of chemical libraries—i.e., commercial, non-commercial and in-house sets, it was made publicly accessible as a ready-to-use FLASK-based application. Additionally, this work employed a focused kinase library for virtual screening using an mt-QSAR model. The virtual hits identified in this process were further filtered by using a similarity search, in silico prediction of drug-likeness, and ADME profiles as well as synthetic accessibility tools. Finally, molecular dynamic simulations were carried out to identify and select the most promising virtual hits. The information gathered from this work can supply important guidelines for the discovery of novel MNK-1/2 inhibitors as potential therapeutic agents.Amit Kumar HalderM. Natália D. S. CordeiroMDPI AGarticleMNK-1 and MNK-2 inhibitorsmt-QSAR modelingvirtual screeningMicrobiologyQR1-502ENBiomolecules, Vol 11, Iss 1670, p 1670 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	MNK-1 and MNK-2 inhibitors mt-QSAR modeling virtual screening Microbiology QR1-502
spellingShingle	MNK-1 and MNK-2 inhibitors mt-QSAR modeling virtual screening Microbiology QR1-502 Amit Kumar Halder M. Natália D. S. Cordeiro Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases
description	The inhibitors of two isoforms of mitogen-activated protein kinase-interacting kinases (i.e., MNK-1 and MNK-2) are implicated in the treatment of a number of diseases including cancer. This work reports, for the first time, a multi-target (or multi-tasking) in silico modeling approach (mt-QSAR) for probing the inhibitory potential of these isoforms against MNKs. Linear and non-linear mt-QSAR classification models were set up from a large dataset of 1892 chemicals tested under a variety of assay conditions, based on the Box–Jenkins moving average approach, along with a range of feature selection algorithms and machine learning tools, out of which the most predictive one (>90% overall accuracy) was used for mechanistic interpretation of the likely inhibition of MNK-1 and MNK-2. Considering that the latter model is suitable for virtual screening of chemical libraries—i.e., commercial, non-commercial and in-house sets, it was made publicly accessible as a ready-to-use FLASK-based application. Additionally, this work employed a focused kinase library for virtual screening using an mt-QSAR model. The virtual hits identified in this process were further filtered by using a similarity search, in silico prediction of drug-likeness, and ADME profiles as well as synthetic accessibility tools. Finally, molecular dynamic simulations were carried out to identify and select the most promising virtual hits. The information gathered from this work can supply important guidelines for the discovery of novel MNK-1/2 inhibitors as potential therapeutic agents.
format	article
author	Amit Kumar Halder M. Natália D. S. Cordeiro
author_facet	Amit Kumar Halder M. Natália D. S. Cordeiro
author_sort	Amit Kumar Halder
title	Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases
title_short	Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases
title_full	Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases
title_fullStr	Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases
title_full_unstemmed	Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases
title_sort	multi-target in silico prediction of inhibitors for mitogen-activated protein kinase-interacting kinases
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4f3b2261f80d47d98c9b3ee756e03750
work_keys_str_mv	AT amitkumarhalder multitargetinsilicopredictionofinhibitorsformitogenactivatedproteinkinaseinteractingkinases AT mnataliadscordeiro multitargetinsilicopredictionofinhibitorsformitogenactivatedproteinkinaseinteractingkinases
_version_	1718412892442722304

Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases

Ejemplares similares