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

Abstract

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

Keywords:

• Meprin β inhibitor
• QSAR
• Bayesian model
• HQSAR
• recursive partitioning
• MD simulation

Acknowledgments

The authors are sincerely thankful to the Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India, and the Department of Pharmacy, BITS-Pilani, Hyderabad Campus, India for providing the necessary research facilities. SJ is thankful to the AICTE for providing a scholarship. SB is thankful to the Swami Vivekananda Merit-cum-Means (SVMCM) scholarship, Govt. of West Bengal, India for awarding the fellowship. BG is thankful to the Department of Health Research, India (File No. 11013_33_2021-GIA HR), Govt. of India and Council of Scientific and Industrial Research (CSIR-37(1722)/19/EMR-II), Govt. of India, for providing research grants. NA is thankful to the authority of Jadavpur University for providing the research grant to conduct the research work.

Disclosure statement

The authors declare no conflict of interest.

Additional information

Funding

This study was supported by the All India Council for Technical Education; Council of Scientific and Industrial Research, India.