Intrinsic α helix propensities compact hydrodynamic radii in intrinsically disordered proteins

Abstract

Proteins that lack tertiary stability under normal conditions, known as intrinsically disordered, exhibit a wide range of biological activities. Molecular descriptions for the biology of intrinsically disordered proteins (IDPs) consequently rely on disordered structural models, which in turn require experiments that assess the origins to structural features observed. For example, while hydrodynamic size is mostly insensitive to sequence composition in chemically denatured proteins, IDPs show strong sequence-specific effects in the hydrodynamic radius (Rh) when measured under normal conditions. To investigate sequence-modulation of IDP Rh, disordered ensembles generated by a Hard Sphere Collision model modified with a structure-based parameterization of the solution energetics were used to parse the contributions of net charge, main chain dihedral angle bias, and excluded volume on hydrodynamic size. Ensembles for polypeptides 10 to 35 residues in length were then used to establish power-law scaling relationships for comparison to experimental Rh from 26 IDPs. Results showed the expected outcomes of increased hydrodynamic size from increases in excluded volume and net charge, and compaction from chain-solvent interactions. Chain bias representing intrinsic preferences for α helix and polyproline II (PPII), however, modulated Rh with intricate dependence on the simulated propensities. PPII propensities at levels expected in IDPs correlated with heightened Rh sensitivity to even weak α helix propensities, indicating bias for common (φ, ψ) are important determinants of hydrodynamic size. Moreover, data show that IDP Rh can be predicted from sequence with good accuracy from a small set of physicochemical properties, namely intrinsic conformational propensities and net charge.

Beta turn propensity and a model polymer scaling exponent identify disordered proteins that phase separate

“…ID regions were predicted from the primary sequences using the GeneSilico MetaDisorder service (Kozlowski and Bujnicki, 2012). For the identified ID regions, R h was then predicted using the net charge and intrinsic chain propensity for the polyproline II backbone conformation, both known to promote elongated hydrodynamic dimensions in disordered ensembles (Tomasso et al, 2016; English et al, 2017, 2019). Comparing the IDP set of experimental R h to the set of predicted R h from PS IDPs, two observations are immediately apparent.…”

“…A key difference between the two IDP sets is that the non-phase separating protein data uses experimental R h , while the PS IDP set uses sequence predicted values. The methods developed to predict R h from sequence for IDPs (Marsh and Forman-Kay, 2010; Tomasso et al, 2016; English et al, 2017) were found to be accurate when tested (Perez et al, 2014; English et al, 2019), and the IDPs used here consist primarily of the same IDPs that were used to train the predictions. R h for these IDPs are thus accurately predicted from sequence ( Supplementary Information, Fig.…”

“…Because the structural dimensions of IDPs are strongly correlated to sequence composition (Marsh and Forman-Kay, 2010; Tomasso et al, 2016; English et al, 2017, 2019), the differences in amino acid content of the two IDP sets were evaluated. Figure 1C shows the ratio obtained from the percent composition of each amino acid type in the two protein sets.…”

“…We have previously developed an experimentally based parameterization for predicting the polymer scaling exponent, v , for disordered proteins based simply on sequence composition. Despite its simplicity, this prediction is remarkably accurate for a range of disordered proteins (Langridge et al, 2014; Perez et al, 2014; Tomasso et al, 2016; English et al, 2017; Yarawsky et al, 2017; English et al, 2018, 2019). This parameterization ignores such affects such as the distribution or clustering of charges (Das and Pappu, 2013), hydrophobic residues (Krishnan et al, 2008), or the relative spacing of more adhesive groups (i.e., stickers) and more inert groups (i.e., spacers) (Harmon et al, 2017; Martin et al, 2020).…”

Structural and Energetic Characterization of the Denatured State from the Perspectives of Peptides, the Coil Library, and Intrinsically Disordered Proteins

“…While chemically denatured proteins are known to adopt macromolecular sizes that depend weakly on sequence details other than chain length [ 36 ], IDPs in water exhibit strong sequence-dependent influences on structural size [ 37 ]. Computer simulations show that steric effects on disordered structure cannot account for the hydrodynamic size dependence on sequence observed in IDPs [ 38 ]. Additionally, temperature changes are found to induce large shifts in the hydrodynamic size for disordered proteins in water [ 39 , 40 , 41 ] that can exceed the change in size associated with the heat denaturation of folded proteins of the same chain length [ 42 ].…”