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About 10 years ago we published our first predictor of intrinsically disordered protein residues in another IEEE journal, the Proceedings of the IEEE International Conference on Neural Networks. Others call such proteins "natively unfolded" and "intrinsically unstructured." Since then, we and others have substantially improved the prediction of intrinsically disordered residues. The prediction of protein intrinsic disorder is similar to the prediction of secondary structure in terms of methodology, but, at the structural level, secondary structure (especially random coil) and intrinsic disorder differ completely in their dynamic motion. First, we will briefly describe the prediction of protein disorder, show the progress from ~ 70 % to ~ 85 % per residue prediction accuracy, and show that intrinsically disordered proteins are common over the three domains of life, but are especially common among the eukaryotes. Next we will discuss our methods for deducing functions that are associated with disordered rather than structured proteins. In brief, structured proteins have advantages for catalysis while disordered proteins and regions have advantages for the reversible, weak binding often observed in signaling, control, and regulation. After that we will discuss how disorder facilitates binding diversity in protein-protein interaction networks, both for single disordered regions binding to many partners and for many disordered regions with different sequences binding to a common site on the surface of one structured protein. Part three presents data indicating that alternative splicing is more prevalent in regions of RNA that code for disorder than those that code for structure, thus providing a means for evolving tissue-specific signaling networks. Finally, we will present a novel approach to drug discovery based on disordered protein.