Discovery of hematopoietic progenitor kinase 1 inhibitors using machine learning-based screening and free energy perturbation.
J Biomol Struct Dyn
; : 1-13, 2024 Jan 10.
Article
in En
| MEDLINE
| ID: mdl-38198294
ABSTRACT
Hematopoietic progenitor kinase 1 (HPK1) is a key negative regulator of T-cell receptor (TCR) signaling and a promising target for cancer immunotherapy. The development of novel HPK1 inhibitors is challenging yet promising. In this study, we used a combination of machine learning (ML)-based virtual screening and free energy perturbation (FEP) calculations to identify novel HPK1 inhibitors. ML-based screening yielded 10 potent HPK1 inhibitors (IC50 < 1 µM). The FEP-guided modification of the in-house false-positive hit, DW21302, revealed that a single key atom change could trigger activity cliffs. The resulting DW21302-A was a potent HPK1 inhibitor (IC50 = 2.1 nM) and potently inhibited cellular HPK1 signaling and enhanced T-cell function. Molecular dynamics (MD) simulations and ADME predictions confirmed DW21302-A as candidate compound. This study provides new strategies and chemical scaffolds for HPK1 inhibitor development.Communicated by Ramaswamy H. Sarma.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Diagnostic_studies
/
Screening_studies
Language:
En
Journal:
J Biomol Struct Dyn
Year:
2024
Document type:
Article
Affiliation country:
China