Computational screening campaign reveal natural candidates as potential ASK1 inhibitors: Pharmacophore modeling, molecular docking, MMGBSA calculations, ADMET prediction, and molecular dynamics simulation studies

Apoptosis Signal Regulating Kinases 1 (ASK 1) is an upstream kinase in the MAPK superfamily. It carries out phosphorylation of other members within the family, leading to the activation of the P38 MAPK pathway and C-JUN-N-terminal kinases. The onset of this activation cascade occurs through the phosphorylation of extracellular protein kinases (EPKs) which triggers different stimuli like pro-inflammatory cytokines, environmental factors, and internal stressors. These proteins are also known as stress-activated protein kinases (SAPKs) that are linked to various pathological conditions, yet effective inhibitors for this protein have not been witnessed. This study used structural-based pharmacophore modeling, molecular docking, molecular dynamics simulations, and ADMET predictions to identify the compounds that can potentially inhibit ASK1. Based on the SN3 database, 4160 natural compounds were evaluated for their pharmacophoric characteristics. Subsequently, the distinguished compounds in this step underwent MMGBSA calculations and docking. Compounds SN0030543, SN035314, and SN0330056 exhibited XP higher docking score values (-14.240 kcal/mol, 12.00 kcal/mol, and -11.054 kcal/mol, respectively) than that of the bound ligand (-10.785 kcal/mol). After undergoing 100 nanosecond molecular dynamics studies, these compounds displayed durable interactions with the ASK1 binding site. These substances should be given priority for validation as ASK1 inhibitors in upcoming experiments.

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Publication Details

Journal
Scientific African
Published
2025-03-06
DOI
https://doi.org/10.1016/j.sciaf.2025.e02634
Citations
5
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
7.52
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Computational screening campaign reveal natural candidates as potential ASK1 inhibitors: Pharmacophore modeling, molecular docking, MMGBSA calculations, ADMET prediction, and molecular dynamics simulation studies

Khaled M. Elamin, Abdulrahim A. Alzain, Bayan E. Ainousah, Hanan A. Mohamed et al.
5 citations
Scientific African
Computational Drug Discovery Methods
7.52
article

Computational screening campaign reveal natural candidates as potential ASK1 inhibitors: Pharmacophore modeling, molecular docking, MMGBSA calculations, ADMET prediction, and molecular dynamics simulation studies

Khaled M. Elamin, Abdulrahim A. Alzain, Bayan E. Ainousah, Hanan A. Mohamed, Sabrin R. M. Ibrahim, Mohammed A. Almogaddam, Ahmed Mohammad Alraddadi, Ehda Ahmad Awad, Rayan Yousif
article en
5 citations

Abstract

Apoptosis Signal Regulating Kinases 1 (ASK 1) is an upstream kinase in the MAPK superfamily. It carries out phosphorylation of other members within the family, leading to the activation of the P38 MAPK pathway and C-JUN-N-terminal kinases. The onset of this activation cascade occurs through the phosphorylation of extracellular protein kinases (EPKs) which triggers different stimuli like pro-inflammatory cytokines, environmental factors, and internal stressors. These proteins are also known as stress-activated protein kinases (SAPKs) that are linked to various pathological conditions, yet effective inhibitors for this protein have not been witnessed. This study used structural-based pharmacophore modeling, molecular docking, molecular dynamics simulations, and ADMET predictions to identify the compounds that can potentially inhibit ASK1. Based on the SN3 database, 4160 natural compounds were evaluated for their pharmacophoric characteristics. Subsequently, the distinguished compounds in this step underwent MMGBSA calculations and docking. Compounds SN0030543, SN035314, and SN0330056 exhibited XP higher docking score values (-14.240 kcal/mol, 12.00 kcal/mol, and -11.054 kcal/mol, respectively) than that of the bound ligand (-10.785 kcal/mol). After undergoing 100 nanosecond molecular dynamics studies, these compounds displayed durable interactions with the ASK1 binding site. These substances should be given priority for validation as ASK1 inhibitors in upcoming experiments.

Scientific AfricanVol. 28
Abu Dhabi University (AE), Umm al-Qura University (SA), Prince Mohammed bin Abdulaziz Hospital (SA), Batterjee Medical College, University of Gezira (SD), Alexandria University (EG), Assiut University (EG), Kumamoto University (JP)
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Computational Drug Discovery Methods
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