Computational screening and automatic filtering for the discovery of novel inhibitors of TMPRSS2, a type II transmembrane serine protease
Tongiorgi, L.; Albani, S.; Rigobello, L.; Maccallini, C.; Musiani, F.
Show abstract
Transmembrane Serine Protease 2 (TMPRSS2) is a membrane protein of the type II serine protease family of enzymes implied in epithelial homeostasis. It is involved in several diseases, notably prostate cancer and SARS-CoV-2 infections. Over the years, only a few tested TMPRSS2 inhibitors showed consistent results. This prompted us to select it as target of structure-based virtual screening, to search for novel inhibitors among a library of 475,770 small molecules. Two sets of TMPRSS2 structures were selected, one taken from molecular dynamics simulations, the other from recently solved X-ray crystallographic structures. We designed a workflow to filter docking results in a reproducible way, allowing for a faster and more reliable selection. The program uses four metrics: the pose consistency of the ligand, docking score, number of interactions with key protein residues, and cluster analysis. This led to the selection and visual inspection of two sets of 500 compounds, which yielded 10 reasonable hit candidates.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Elucidation of cryptic and allosteric pockets within the SARS-CoV-2 protease 96%
- Structure-based identification of naphthoquinones and derivatives as novel inhibitors of main protease Mpro and papain-like protease PLpro of SARS-CoV-2 96%
- Druggability Assessment in TRAPP using Machine Learning Approaches 96%
Similar papers in this journal
- CHI3L1-Targeted Small Molecules as Glioblastoma Therapies: Virtual Screening-Based Discovery, Biophysical Validation, Pharmacokinetic Profiling, and Evaluation in Glioblastoma Spheroids 96%
- Development of pyrazolopyrimidine based macrocyclic kinase inhibitors targeting AAK1 95%
- Automated design and optimization of multitarget schizophrenia drug candidates by deep learning 94%
Similar papers in this journal
- HTRF-based identification of small molecules targeting SARS-CoV-2 E protein interaction with ZO-1 PDZ2 96%
- Discovery of Z1362873773: A Novel Fascin Inhibitor from a Large Chemical Library for Colorectal Cancer 96%
- Bacopa monnieri phytochemicals as promising BACE1 inhibitors for Alzheimers Disease Therapy 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.