Intrinsic disorder in cell-signaling and cancer-associated proteins

Abstract

The number of intrinsically disordered proteins known to be involved in cell-signaling and regulation is growing rapidly. To test for a generalized involvement of intrinsic disorder in signaling and cancer, we applied a neural network predictor of natural disordered regions (PONDR VL-XT) to four protein datasets: human cancer-associated proteins (HCAP), signaling proteins (AfCS), eukaryotic proteins from SWISS-PROT (EU_SW) and non-homologous protein segments with well-defined (ordered) 3D structure (O_PDB_S25). PONDR VL-XT predicts >or=30 consecutive disordered residues for 79(+/-5)%, 66(+/-6)%, 47(+/-4)% and 13(+/-4)% of the proteins from HCAP, AfCS, EU_SW, and O_PDB_S25, respectively, indicating significantly more intrinsic disorder in cancer-associated and signaling proteins as compared to the two control sets. The disorder analysis was extended to 11 additional functionally diverse categories of human proteins from SWISS-PROT. The proteins involved in metabolism, biosynthesis, and degradation together with kinases, inhibitors, transport, G-protein coupled receptors, and membrane proteins are predicted to have at least twofold less disorder than regulatory, cancer-associated and cytoskeletal proteins. In contrast to 44.5% of the proteins from representative non-membrane categories, just 17.3% of the cancer-associated proteins had sequence alignments with structures in the Protein Data Bank covering at least 75% of their lengths. This relative lack of structural information correlated with the greater amount of predicted disorder in the HCAP dataset. A comparison of disorder predictions with the experimental structural data for a subset of the HCAP proteins indicated good agreement between prediction and observation. Our data suggest that intrinsically unstructured proteins play key roles in cell-signaling, regulation and cancer, where coupled folding and binding is a common mechanism.