A Multiscale Simulation Framework for Elucidating Photochemical Structure-Activity Relationships of Photoswitchable...

Predicting the photochemical structure-activity relationships (photo-SAR) of photoswitchable ligands in complex biomolecular environments remains a great challenge due to intricate protein-ligand interactions, strong electron correlation in multiple electronic states, coupled nuclear and electronic dynamics, and protein conformational flexibility. To bridge this gap, here we develop a unified multiscale simulation framework that integrates first-principles nonadiabatic dynamics, excited-state enhanced sampling, and ground-state alchemical free-energy calculations. We applied this approach to photostatins (PSTs), a class of photoswitchable tubulin inhibitors with promising light-regulated anticancer bioactivity, and validated our predictions against extensive experimental data, including ultrafast time-resolved crystallography, absorption spectra, and isomer-dependent bioactivity assays. Our simulations reveal, for the first time, that nonradiative decay rates correlate directly with equilibrium excited-state free-energy surfaces, which are modulated by substituents, protein electrostatics, and steric confinement. Specifically, protein electrostatic fields accelerate excited-state relaxation, whereas steric constraints oppose it. The balance of these factors determines the trend of excited-state dynamics across PST derivatives. Our results further show that the photoisomerization quantum yield depends on (1) the directional alignment of torsional motions with nonadiabatic coupling vectors during nonradiative decay, and (2) the propensity for backward ground-state isomerization, both of which are shaped by protein-ligand interactions. Finally, among the free-energy methods tested, thermodynamic integration most accurately captures subtle substituent effects on the contrast in binding affinities between isomers, a critical metric for minimizing their off-target effects in the dark-adapted state. This work establishes a robust computational platform for accurately predicting photodynamics and light-responsive binding affinities of photoswitchable ligands in biomolecular systems, while also providing novel mechanistic insights that can facilitate their rational design in biological and biomedical applications.