Decoding PARP1 Selectivity: Atomistic Insights for Next-Generation Cancer Inhibitors

Selective inhibition of PARP1 has emerged as a promising strategy to improve the therapeutic index of PARP inhibitors, a class of anticancer agents that exploit defects in DNA repair pathways. While PARP inhibitors have shown remarkable clinical benefit, particularly in BRCA-mutated tumors, the lack of discrimination between PARP1 and its close homolog PARP2 often leads to hematological toxicity and limits treatment efficacy. Therefore, achieving molecular selectivity for PARP1 remains a central challenge in the rational design of safer and more potent inhibitors. Here, we perform atomistic molecular dynamics simulations using potential of mean force (PMF) calculations, energetic decomposition, and targeted in silico mutagenesis to dissect the molecular determinants underlying PARP1 versus PARP2 selectivity across four representative approved compounds. Our simulations of the catalytic domain were used to quantify residue–ligand contact frequencies, ligand-induced modulation of protein flexibility, and the free-energy landscapes governing binding and unbinding processes. Contact analysis identified key residues mediating ligand recognition, while energetic decomposition characterized the dominant stabilizing forces inducing ligand-protein interactions. Importantly, our PMF profiles revealed distinct energetic and molecular features of association pathways responsible for ligand selectivity. Our results demonstrate that selective inhibitors stabilize unique PARP1 interactions and follow favorable low-barrier association routes, whereas the same ligands encounter higher barriers in PARP2. Together, these findings provide a unified mechanistic framework for selectivity and inform the design of next-generation PARP1 inhibitors with improved efficacy and safety.