From KB to K_B: observing entropy dynamics of complex systems using lossless compression algorithms

Free-energy estimation is key in the characterization of protein structure dynamics and binding prediction for applications such as drug-design. The enthalpy of a simulated protein can be calculated throughout the simulation using an apriori choice of the force field, yet entropy remains a challenge to quantify. Moreover, current entropy estimation techniques, in particular for large protein simulations, suffer from requiring abundant computation resources or the requirement of additional knowledge such as the protein ground-state. Here, I will present our recently developed scheme for universal estimation of entropy using lossless compression algorithm¹. Specifically, we apply our scheme on MD simulations of a protein fluctuating between folded and unfolded states. Our calculated entropy difference between the states successfully yields values comparable to previous estimates. Moreover, by applying our scheme for short windows along the simulation timeline, we demonstrate unmatched knowledge-free detection of the folding events and structure. This demonstrates a novel model-independent approach and opens a new window onto the entropy dynamics of complex systems.
¹ Avinery, Kornreich & Beck (2017) arXiv:1709.10164