Identification of Novel Potent NSD2-PWWP1 Ligands Using Structure-Based Design and Computational Approaches

Area of Science:

• Biochemistry
• Molecular Biology
• Computational Chemistry

Background:

• Dysregulation of histone methyl transferase nuclear receptor-binding SET domain 2 (NSD2) is linked to various cancers.
• NSD2, a multidomain protein, possesses both histone writing and reading functions.
• Developing potent and selective NSD2 inhibitors targeting its enzymatic activity is challenging.

Purpose of the Study:

• To explore small molecules targeting the NSD2-PWWP1 domain as an alternative inhibition strategy.
• To identify high-affinity binders for the NSD2-PWWP1 domain using computational chemistry.
• To provide novel chemical tools for investigating NSD2 function in human cancers.

Main Methods:

• Employed advanced computational chemistry techniques, including free energy perturbation coupled to machine learning (FEP/ML) models.
• Utilized virtual screening (VS) activities to identify potential NSD2-PWWP1 binders.
• Synthesized and characterized identified compounds for binding affinity.

Main Results:

• Identified novel high-affinity binders for the NSD2-PWWP1 domain.
• Discovered compound 34, the most potent NSD2-PWWP1 binder reported to date (pIC50 = 8.2).
• Validated the efficacy of computational approaches in identifying selective enzyme inhibitors.

Conclusions:

• The identified compounds serve as valuable tools for studying the role of PWWP1 domains in NSD2 inhibition.
• This work demonstrates the potential of FEP/ML and VS in drug discovery for challenging targets like NSD2.
• The findings pave the way for developing novel therapeutic strategies against NSD2-driven malignancies.