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 (Scnf). Our work focused on creating a set of 17 descriptors intended to capture variations in Scnf and its comparison to an extended set of 113 structural and energy model features that extend the Rocklin et al. feature set (R). The Scnf 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 Scnf descriptor is described by linear combinations of R features. Despite containing useful information about minipeptide stability, providing Scnf 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.