Improved disorder prediction by combination of orthogonal approaches

Disordered proteins are highly abundant in regulatory processes such as transcription and cell-signaling. Different methods have been developed to predict protein disorder often focusing on different types of disordered regions. Here, we present MD, a novel META-Disorder prediction method that molds various sources of information predominantly obtained from orthogonal prediction methods, to significantly improve in performance over its constituents. In sustained cross-validation, MD not only outperforms its origins, but it also compares favorably to other state-of-the-art prediction methods in a variety of tests that we applied.