Intrinsically Disordered Proteins (IDPs) play key functional roles facilitated by their inherent plasticity. In most of the cases, IDPs recognize their partners through partially-structured elements inserted in fully-disordered chains. The identification and characterization of these elements is fundamental to understand the functional mechanisms of IDPs. Although several computational methods have been developed to identify order within disordered chains, most of the current secondary structure predictors are focused on globular proteins and are not necessarily appropriate for IDPs. Here, we present a comprehen-sible method, called Local Structural Propensity Predictor (LS2P), to predict secondary structure elements from IDP sequences. LS2P performs statistical analyses from a database of three-residue fragments extracted from coil regions of high-resolution protein structures. In addition to identifying scarcely populated helical and extended regions, the method pinpoints short stretches triggering β-turn formation or promoting α-helices. The simplicity of the method enables a direct connection between experimental observations and structural features encoded in IDP sequences.