Although many proteins necessitate well-folded structures to properly instigate their biological functions, a large fraction of functioning proteins contain regions-known as intrinsically disordered protein regions-where stable structures are not likely to form. Notable functional roles of intrinsically disordered proteins are in transcriptional regulation, translation, and cellular signal transduction. Moreover, intrinsically disordered protein regions are highly abundant in many proteins associated with various human diseases, therefore these segments have become attractive drug targets for potential therapeutics. Over the past decades, numerous computational methods have been developed to accurately predict disordered regions of proteins. Here we introduce a user-friendly and reliable approach for the prediction of disordered protein regions using the structure prediction software Rosetta. Using 245 proteins from a benchmark data set (16 DisProt database proteins) and a test data set (229 proteins with NMR data), we use Rosetta to predict the global protein structures and then show that there is a statistically significant difference between Rosetta scores in disordered and ordered regions, with scores being less favorable in disordered regions. Furthermore, the difference in scores between ordered and disordered protein regions is sufficient to accurately identify disordered protein regions. As a result, our Rosetta ResidueDisorder method (benchmark data set prediction accuracy of 71.77% and independent test data set prediction accuracy of 65.37%) outperformed other established disorder prediction tools and did not exhibit a biased prediction toward either ordered or disordered regions. To facilitate usage, a Rosetta application has been developed for the Rosetta ResidueDisorder method.