Optimizing Molecular Motors Fueled By Chiral Transitions

Many models in statistical physics involve high-dimensional Markov dynamics that depend on numerous parameters, yet estimating the sensitivity of a dynamical statistic to those parameters is often prohibitively expensive, especially when the dynamics involve rare events. We present a practical pipeline for efficiently computing such sensitivities in systems with large state spaces and rare transitions. To demonstrate a use case for our approach, we introduce an explicit particle-based molecular dynamics model of a molecular motor driven by a racemization reaction. A chiral fuel molecule in one of two enantiomeric states (R and S) interacts with the motor, depositing blocking groups with a kinetic asymmetry such that a motor initialized with pure R fuel can be made to do work during the racemization. Our approach to computing sensitivities is able to adjust the parameters in the force field to optimize the rotation rate of the molecular motor model when the concentration of R or S is enriched by grand canonical Monte Carlo. We furthermore explicitly show that forced rotation of the motor can pump the fuel from a racemic mixture to one enriched in R.