Of fishes and birthdays: Efficient estimation of polymer configurational entropies

We present an algorithm to estimate the configurational entropy S of a polymer. The algorithm uses the statistics of coincidences among random samples of configurations and is related to the catch-tag-release method for estimation of population sizes, and to the classic “birthday paradox”. Bias in the entropy estimation is decreased by grouping configurations in nearly equiprobable partitions based on their energies, and estimating entropies separately within each partition. Whereas most entropy estimation algorithms require N ∼ 2 samples to achieve small bias, our approach typically needs only N ∼ √ 2S . Thus the algorithm can be applied to estimate protein free energies with increased accuracy and decreased computational cost.