DispHred: A Server to Predict pH-Dependent Order–Disorder Transitions in Intrinsically Disordered Proteins

Abstract

The natively unfolded nature of intrinsically disordered proteins (IDPs) relies on several physicochemical principles, of which the balance between a low sequence hydrophobicity and a high net charge appears to be critical. Under this premise, it is well-known that disordered proteins populate a defined region of the charge–hydropathy (C–H) space and that a linear boundary condition is sufficient to distinguish between folded and disordered proteins, an approach widely applied for the prediction of protein disorder. Nevertheless, it is evident that the C–H relation of a protein is not unalterable but can be modulated by factors extrinsic to its sequence. Here, we applied a C–H-based analysis to develop a computational approach that evaluates sequence disorder as a function of pH, assuming that both protein net charge and hydrophobicity are dependent on pH solution. On that basis, we developed DispHred, the first pH-dependent predictor of protein disorder. Despite its simplicity, DispHred displays very high accuracy in identifying pH-induced order/disorder protein transitions. DispHred might be useful for diverse applications, from the analysis of conditionally disordered segments to the synthetic design of disorder tags for biotechnological applications. Importantly, since many disorder predictors use hydrophobicity as an input, the here developed framework can be implemented in other state-of-the-art algorithms.

Order from Disorder with Intrinsically Disordered Peptide Amphiphiles

“…The turbidity of both variants changes abruptly at pH∼ 6 and 4.5, for the 4 × 7 and 2 × 12, respectively. Notably, the pH-dependent CD measurements ( Supporting Information Figure S2 ) confirm the disorder–order prediction 48 that these peptides do not undergo any structural reorganization around pH ∼ 6.…”

Prediction of the effect of pH on the aggregation and conditional folding of intrinsically disordered proteins with SolupHred and DispHred

“…In previous works, we rationalized the influence of pH on IDPs' disordered states and aggregation propensities and modeled their dependence on this parameter in such reactions [15][16][17]. Our models succeeded in identifying pH-dependent aggregation and order-disorder transitions, as reported in the literature.…”

“…To do so, we exploited a recent study where Zamora and coworkers [18] developed a pH-dependent amino acid lipophilicity scale by computing the n-octanol/water partition for amino acids in protein-like environments using continuum solvation calculations. Even if lipophilicity and hydrophobicity are not strictly equivalent terms, the aforementioned scale showed a good correlation with hydrophobicity-based scales widely used in applications of disorder prediction [17,[19][20][21] or aggregation [15,22]. Therefore, it will serve as a legit proxy for the calculus of protein hydrophobicity.…”

DispHScan: A Multi-Sequence Web Tool for Predicting Protein Disorder as a Function of pH

“…Disorder calculation : Hydrophobicity and NCPR are combined in the linear boundary condition equation described by Santos and their co-workers to discriminate the folding state for each pH in range (DispH score) [ 7 ]. The algorithm classifies proteins with positive DispH scores as folded and negatives as unfolded.…”

“…In a recent study, we modeled protein disorder as a function of pH and developed a disorder predictor able to anticipate disorder-to-order transitions in IDPs named DispHred [ 7 ]. The DispHred algorithm was based on the realisation of Uversky and his coworkers that protein disorder could be anticipated by analyzing two simple biophysical properties: protein net charge and hydrophobicity [ 3 ].…”