A combined ligand-based and structure-based in silico molecular modeling approach to pinpoint the key structural attributes of hydroxamate derivatives as promising meprin β inhibitors

Human meprin β is a Zn2+-containing multidomain metalloprotease enzyme that belongs to the astacin family of the metzincin endopeptidase superfamily. Meprin β, with its diverse tissue expression pattern and wide substrate specificity, plays a significant role in various biological processes, including regulation of IL-6R pathways, lung fibrosis, collagen deposition, cellular migration, neurotoxic amyloid β levels, and inflammation. Again, meprin β is involved in Alzheimer's disease, hyperkeratosis, glomerulonephritis, diabetic kidney injury, inflammatory bowel disease, and cancer. Despite a crucial role in diverse disease processes, no such promising inhibitors of meprin β are marketed to date. Thus, it is an unmet requirement to find novel promising meprin β inhibitors that hold promise as potential therapeutics. In this study, a series of arylsulfonamide and tertiary amine-based hydroxamate derivatives as meprin β inhibitors has been analyzed through ligand-based and structure-based in silico approaches to pinpoint their structural and physiochemical requirements crucial for exerting higher inhibitory potential. This study identified different crucial structural features such as arylcarboxylic acid, sulfonamide, and arylsulfonamide moieties, as well as hydrogen bond donor and hydrophobicity, inevitable for exerting higher meprin β inhibition, providing valuable insight for their further future development.Communicated by Ramaswamy H. Sarma.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - year:2024

Enthalten in:

Journal of biomolecular structure & dynamics - (2024) vom: 02. Jan., Seite 1-17

Sprache:

Englisch

Beteiligte Personen:

Jana, Sandeep [VerfasserIn]
Banerjee, Suvankar [VerfasserIn]
Baidya, Sandip Kumar [VerfasserIn]
Ghosh, Balaram [VerfasserIn]
Jha, Tarun [VerfasserIn]
Adhikari, Nilanjan [VerfasserIn]

Links:

Volltext

Themen:

Bayesian model
HQSAR
Journal Article
MD simulation
Meprin β inhibitor
QSAR
Recursive partitioning

Anmerkungen:

Date Revised 02.01.2024

published: Print-Electronic

Citation Status Publisher

doi:

10.1080/07391102.2023.2298394

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM366560875