Predicting protein stability is a challenge due to the many competing thermodynamic effects. Through de novo protein design, one begins with a target structure and searches for a sequence that will fold into it. Previous work by Rocklin et al. introduced a data set of more than 16,000 miniproteins spanning four structural topologies with information on stability. These structures were characterized with a set of 46 structural descriptors, with no explicit inclusion of configurational entropy (S cnf). Our work focused on creating a set of 17 descriptors intended to capture variations in S cnf and its comparison to an extended set of 113 structural and energy model features that extend the Rocklin et al. feature set (R). The S cnf descriptors statistically discriminate between stable and unstable distributions within topologies and best describe EEHEE topology stability (where E = β sheet and H = α helix). Between 50 and 80% of the variation in each S cnf descriptor is described by linear combinations of R features. Despite containing useful information about minipeptide stability, providing S cnf features as inputs to machine learning models does not improve overall performance when predicting protein stability, as the R features sufficiently capture the implicit variations.