Computational Design and Experimental Validation of Photoactive PARP1 Inhibitors

TL;DR

This study combines computational methods and experimental validation to design photoactive PARP1 inhibitors that can be controlled by visible light, demonstrating a promising approach for targeted cancer therapy.

Contribution

It introduces a comprehensive computational workflow integrating atomistic simulation, machine learning, and free energy calculations to identify photoactive PARP1 inhibitors with experimental validation.

Findings

Identified a compound with 15-fold increased PARP1 inhibition upon green-light irradiation.

Validated the computational screening approach for red-shifted photoinhibitors.

Highlighted limitations like rapid thermal relaxation affecting efficacy.

Abstract

Light-activated drugs are a promising way to treat localized diseases for which existing treatments have severe side effects. However, their development is complicated by the set of photophysical and biological properties that must be simultaneously optimized. Here we used computational techniques to find a set of promising candidates for the photoactive inhibition of the poly(ADP-ribose) polymerase 1 (PARP1) cancer target. Using our recently developed methods based on atomistic simulation and machine learning (ML), we screened a set of 5 million hypothetical photoactive ligands. Our workflow used protein-ligand docking to identify candidates with differential PARP1 binding under light and dark conditions; ML force fields and quantum chemistry calculations to predict p$K_\mathrm{a}$, absorption spectra, and thermal half-lives; graph-based surrogate models to screen additional compounds; excited-state nonadiabatic dynamics with ML force fields to estimate quantum yields; and free energy perturbation (FEP) to refine binding predictions. From these predictions, we prioritized a small set of synthetically feasible candidates expected to have red-shifted absorption spectra, thermal half-lives on the order of seconds to minutes, and isomer-dependent PARP1 binding under visible-light control. We synthesized 10 candidates and experimentally characterized their photobehavior and PARP1 inhibition constants. Among the validated compounds, \textbf{1} showed a 15-fold increase in inhibition of PARP1 upon green-light irradiation at 519 nm (208.8 $\pm$ 28.3 $\mu$M vs 14.4 $\pm$ 1.9 $\mu$M). These results validate the computation-guided screening strategy for identifying red-shifted PARP1 photoinhibitors, while also underscoring current limitations such as rapid thermal relaxation in aqueous media.