Intrinsically semi-disordered state and its role in induced folding and protein aggregation.

Intrinsically disordered proteins (IDPs) refer to those proteins without fixed three-dimensional structures under physiological conditions. Although experiments suggest that the conformations of IDPs can vary from random coils, semi-compact globules, to compact globules with different contents of secondary structures, computational efforts to separate IDPs into different states are not yet successful. Recently, we developed a neural-network-based disorder prediction technique SPINE-D that was ranked as one of the top performing techniques for disorder prediction in the biannual meeting of critical assessment of structure prediction techniques (CASP 9, 2010). Here, we further analyze the results from SPINE-D prediction by defining a semi-disordered state that has about 50% predicted probability to be disordered or ordered. This semi-disordered state is partially collapsed with intermediate levels of predicted solvent accessibility and secondary structure content. The relative difference in compositions between semi-disordered and fully disordered regions is highly correlated with amyloid aggregation propensity (a correlation coefficient of 0.86 if excluding four charged residues and proline, 0.73 if not). In addition, we observed that some semi-disordered regions participate in induced folding, and others play key roles in protein aggregation. More specifically, a semi-disordered region is amyloidogenic in fully unstructured proteins (such as alpha-synuclein and Sup35) but prone to local unfolding that exposes the hydrophobic core to aggregation in structured globular proteins (such as SOD1 and lysozyme). A transition from full disorder to semi-disorder at about 30-40 Qs is observed in the poly-Q (poly-glutamine) tract of huntingtin. The accuracy of using semi-disorder to predict binding-induced folding and aggregation is compared with several methods trained for the purpose. These results indicate the usefulness of three-state classification (order, semi-disorder, and full-disorder) in distinguishing nonfolding from induced-folding and aggregation-resistant from aggregation-prone IDPs and in locating weakly stable, locally unfolding, and potentially aggregation regions in structured proteins. A comparison with five representative disorder-prediction methods showed that SPINE-D is the only method with a clear separation of semi-disorder from ordered and fully disordered states.