Interpreting size-exclusion data for highly branched biopolymers by reverse monte carlo simulations

Size-exclusion chromatography with multiple detection provides data on the distributions of various properties in a branched polymer sample, for example, distributions of the number, average mass, mean-squared mass, and branching fraction against hydrodynamic volume. A method is developed that provides a basis to use such data for obtaining structural and biosynthetic information on highly branched polymers, such as amylopectin. We generate by simulation a reference distribution of randomly branched polymers from the experimental distribution of debranched chains of the target polymer. We then select from these simulated chains a set with the same number (or other) distribution as the actual polymer sample, using reverse Monte Carlo simulations. Properties of these model polymers are used to interpret the differences with experiment as due to correlations in branching structure. The same methodology can be applied to data from other separation techniques such as field-flow fractionation and high-performance anionic exchange chromatography.