Benchmarking free energy computational methods for revealing the key interactions driving PARP1 selective inhibition.

Abstract

e13004 Background: Accurately predicting inhibitor selectivity among closely related protein paralogues remains a central challenge in computational drug discovery. Methods: In this work¹, we systematically compared three computational methods—MM/PBSA, free energy perturbation (FEP), and potential of mean force (PMF) calculations—to assess their ability to predict PARP1 vs PARP2 selectivity across eight clinically relevant PARP inhibitors used in ovarian, breast, prostate, and related cancers 2 . Results: MM/PBSA offered rapid, low-cost qualitative insight but proved highly sensitive to the choice of static binding pose, limiting its reliability when energetic differences between paralogues are small. In contrast, atomistic FEP and PMF simulations performed with explicit solvent show substantially improved agreement with experimental binding affinities. Our atomistic simulations near-quantitatively captured the experimental relative binding preferences, demonstrating that both PMF and FEP calculations using the a99SB- disp 3 and OpenFF 4 force fields reliably reflect selectivity patterns. Based on these findings, we analysed protein–ligand contact frequencies to identify the stabilising interaction network and contact connectivity inducing protein selectivity. The most frequent protein–inhibitor contacts are primarily mediated by tyrosine triads and electrostatic interactions, showing a cooperative complex network of intermolecular contacts which strongly relies on protein multivalency. To dissect the decisive role of individual residues across the binding site, we also performed targeted mutagenesis of the catalytic pocket in complex with saruparib, replacing several active-site amino acids by glycines. Progressively increasing the number of mutations markedly reduces binding stability, with distinct residue combinations exerting two primary effects: destabilization of the final bound state and the emergence of energetic barriers along the ligand association pathway 5 . Conclusions: Together, our results provided a coherent mechanistic framework for understanding PARP1 selectivity and informs the rational design of next-generation inhibitors with improved efficacy and safety. References: 1) Feito et al., bioRxiv , 10.64898/2025.12.29.696816, (2025). 2) Jackson et al., NAR Cancer , 4 (4), zcac042, (2022). 3) Robustelli et al., PNAS 115 (21), E4758-E4766, (2018). 4) Boothroyd et al., JCTC , 19 (11), 3251-3275, (2023). 5) Feito et al., bioRxiv , 10.1101/2025.10.13.681983, (2025).