Designing a model of retinal isomerization in rhodopsin, the first step in vision, that accounts for both experimental transient and stationary state observables is challenging. Here, multiobjective Bayesian optimization is employed to refine the parameters of a minimal two-state-two-mode (TM) model describing the photoisomerization of retinal in rhodopsin. The optimized retinal model predicts excitation wavelength-dependent fluorescence spectra that closely align with experimentally observed non-Kasha behavior in the nonequilibrium steady state. Further, adjustments to the potential energy surface within the TM model reduce the discrepancies across the time domain. Overall, agreement with experimental data is excellent.

中文翻译：

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模型视网膜异构化中非 Kasha 行为和瞬态动力学的机器学习优化


设计视紫红质中视网膜异构化的模型是视觉的第一步，它同时考虑了实验瞬态和静止状态可观察物，这很有挑战性。在这里，采用多目标贝叶斯优化来优化描述视紫红质中视黄醛光异构化的最小两态双模式 （TM） 模型的参数。优化的视网膜模型预测了激发波长依赖性荧光光谱，该光谱与实验观察到的非 Kasha 在非平衡稳态下的行为密切相关。此外，对 TM 模型中势能表面的调整减少了整个时域的差异。总体而言，与实验数据的一致性非常好。