Multi-objective optimization for retinal photoisomerization models with respect to experimental observables.

Area of Science:

• Computational Biophysics
• Physical Chemistry
• Molecular Modeling

Background:

• Traditional physical model fitting often relies on single observables, limiting robustness assessment.
• Handling multiple target observables in model fitting is computationally cumbersome and lacks clear quantification of model performance across all targets.

Purpose of the Study:

• To develop and demonstrate a multi-objective optimization framework for fitting physical models to multiple observables.
• To assess the existence of a single set of model parameters that can simultaneously describe diverse experimental measurements.

Main Methods:

• Constructing a Pareto front to identify optimal trade-offs between different objectives (observables).
• Employing Gaussian process models to approximate prediction errors, reducing computational cost within a Bayesian optimization framework.
• Applying the methodology to refine models for stationary state cis-trans photoisomerization of retinal in rhodopsin.

Main Results:

• Demonstrated the feasibility of jointly optimizing models for multiple experimental observables, including emission spectra, absorption frequencies, and energy storage.
• Successfully improved existing models for retinal photoisomerization by considering a comprehensive set of experimental data.
• Provided insights into the advantages and limitations of previously proposed biophysical models.

Conclusions:

• Multi-objective optimization offers a robust and efficient approach for fitting physical models to multiple experimental observables.
• The Pareto front analysis effectively reveals trade-offs and identifies optimal model parameters for complex biophysical systems like rhodopsin.
• This methodology enhances the predictive power and reliability of molecular simulations in biophysics.