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Describing the dynamics and conformational landscapes of Intrinsically Disordered Proteins (IDPs) is of paramount importance to understanding their functions. Markov State Models (MSMs) are often used to characterize the dynamics of more structured proteins, but models of IDPs built using conventional MSM modelling protocols can be difficult to interpret due to the inherent nature of IDPs, which exhibit fast transitions between disordered microstates. We propose a new method of determining MSM states from all-atom molecular dynamics simulation data of IDPs by using per-residue secondary structure assignments as input features in a MSM model. Because such secondary structure algorithms use a select set of features for assignment (dihedral angles, contact distances, etc.), they represent a knowledge-based refinement of feature sets used for model-building. This method adds interpretability to IDP conformational landscapes, which are increasingly viewed as composed of transient secondary structure, and allows us to readily use MSM analysis tools in this paradigm. We demonstrate the use of our method with the transcription factor p53 c-terminal domain (p53-CTD), a commonly-studied IDP. We are able to characterize the full secondary structure phase space observed for p53-CTD, and describe characteristics of p53-CTD as a network of transient helical and beta-hairpin structures with different network behaviors in different domains of secondary structure. This analysis provides a novel example of how IDPs can be studied and how researchers might better understand a disordered protein conformational landscape.