Discovery of a Novel and Potent LCK Inhibitor for Leukemia Treatment via Deep Learning and Molecular Docking

The lymphocyte-specific protein tyrosine kinase (LCK)
plays a crucial
role in both T-cell development and activation. Dysregulation of LCK
signaling has been demonstrated to drive the oncogenesis of T-cell
acute lymphoblastic leukemia (T-ALL), thus providing a therapeutic
target for leukemia treatment. In this study, we introduced a sophisticated
virtual screening strategy combined with biological evaluations to
discover potent LCK inhibitors. Our initial approach involved utilizing
the PLANET algorithm to assess and contrast various scoring methodologies
suitable for LCK inhibitor screening. After effectively evaluating
PLANET, we progressed to devise a virtual screening workflow that
synergistically combines the strengths of PLANET with the capabilities
of Schrödinger’s suite. This integrative strategy led
to the efficient identification of four potential LCK inhibitors.
Among them, compound 1232030-35-1 stood out as the most promising
candidate with an IC50 of 0.43 nM. Further in vitro bioassays revealed that 1232030-35-1 exhibited robust antiproliferative
effects on T-ALL cells, which was attributed to its ability to suppress
the phosphorylations of key molecules in the LCK signaling pathway.
More importantly, 1232030-35-1 treatment demonstrated profound in vivo antileukemia efficacy in a human T-ALL xenograft
model. In addition, complementary molecular dynamics simulations provided
deeper insight into the binding kinetics between 1232030-35-1 and
LCK, highlighting the formation of a hydrogen bond with Met319. Collectively,
our study established a robust and effective screening strategy that
integrates AI-driven and conventional methodologies for the identification
of LCK inhibitors, positioning 1232030-35-1 as a highly promising
and novel drug-like candidate for potential applications in treating
T-ALL.