Identification of potent TMPRSS4 inhibitors through structural modeling and molecular dynamics simulations
Abstract
TMPRSS4, a transmembrane serine protease type II, is associated with various pathological illnesses. It has been found to activate SARS-CoV-2, enhance viral infection of human small-intestinal enterocytes and is overexpressed in different types of cancers. Therefore, this study aims to disover potential TMPRSS4 inhibitors that have better binding affinity than the approved inhibitors: 2-hydroxydiarylamide and tyroserleutide. Since no 3D-structure is known for TMPRSS4, structural models for the TMPRSS4 serine protease domain were developed. The modeled structures were validated and subjected to molecular dynamics simulations. FDA-approved, clinical/preclinical drugs and natural products were docked to the pocket of TMPRSS4. Moreover, through a systematic analysis, MD simulations and MM-GBSA binding free energy calculations revealed that the best candidates Ergotamine, S55746, NPC478048, Lifirafenib, and NPC77101 are highly stable drug candidates in complex with TMPRSS4, displaying low RMSD and RMSF values with strong binding stability. Among these compounds, Ergotamine showed the most favorable binding energy (-33.73 kcal/mol). Overall, our in silico results revealed that these compounds could act as potent TMPRSS4 inhibitors and need to be validated by future experimental studies.
Read the paper
Related studies
- Meta-analysis of genome-wide association for migraine in six population-based European cohorts
- New and sex-specific migraine susceptibility loci identified from a multiethnic genome-wide meta-analysis
- microRNA profiling in atherosclerosis, diabetes, and migraine
- Genome‐Wide‐Associated Variants in Migraine Susceptibility: A Replication Study From North I ndia
- Pharmacoinformatics elucidation of potential drug targets against migraine to target ion channel protein KCNK18
- Upregulation of IL-1 Receptor Antagonist in a Mouse Model of Migraine
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.