Intrinsically disordered proteins (IDPs) are typically low in nonpolar/hydrophobic but relatively high in polar, charged, and aromatic amino acid compositions. Some IDPs undergo liquid-liquid phase separation in the aqueous milieu of the living cell. The resulting phase with enhanced IDP concentration can function as a major component of membraneless organelles that, by creating their own IDP-rich microenvironments, stimulate critical biological functions. IDP phase behaviors are governed by their amino acid sequences. To make progress in understanding this sequence-phase relationship, we report further advances in a recently introduced application of random-phase-approximation (RPA) heteropolymer theory to account for sequence-specific electrostatics in IDP phase separation. Here we examine computed variations in phase behavior with respect to block length and charge density of model polyampholytes of alternating equal-length charge blocks to gain insight into trends observed in IDP phase separation. As a real-life example, the theory is applied to rationalize/predict binodal and spinodal phase behaviors of the 236-residue N-terminal disordered region of RNA helicase Ddx4 and its charge-scrambled mutant for which experimental data are available. Fundamental differences are noted between the phase diagrams predicted by RPA and those predicted by mean-field Flory-Huggins and Overbeek-Voorn/Debye-Hückel theories. In the RPA context, a physically plausible dependence of relative permittivity on protein concentration can produce a cooperative effect in favor of IDP-IDP attraction and thus a significant increased tendency to phase separate. Ramifications of these findings for future development of IDP phase separation theory are discussed.

FT-IR, in combination with residual amino group determination using a fluorescence technique, has been used to investigate the chemical functional groups involved in the cross-linking reaction between glutaraldehyde and gelatin molecules. The results suggest that, at high pH values (i.e., close to the pK(a) of lysine), the cross-linking reaction is mainly governed by the well-known Schiff base formation, whereas at low pH (i.e., when the amino groups of lysine are protonated), the reaction may also involve the -OH groups of hydroxyproline and hydroxylysine, leading to the formation of hemiacetals.

Single-molecule Förster resonance energy transfer (smFRET) is an important tool for studying disordered proteins. It is commonly utilized to infer structural properties of conformational ensembles by matching experimental average energy transfer ⟨E⟩exp with simulated ⟨E⟩sim computed from the distribution of end-to-end distances in polymer models. Toward delineating the physical basis of such interpretative approaches, we conduct extensive sampling of coarse-grained protein chains with excluded volume to determine the distribution of end-to-end distances conditioned upon given values of radius of gyration Rg and asphericity A. Accordingly, we infer the most probable Rg and A of a protein disordered state by seeking the best fit between ⟨E⟩exp and ⟨E⟩sim among various (Rg,A) subensembles. Application of our method to residues 1-90 of the intrinsically disordered cyclin-dependent kinase (Cdk) inhibitor Sic1 results in inferred ensembles with more compact conformations than those inferred by conventional procedures that presume either a Gaussian chain model or the mean-field Sanchez polymer theory. The Sic1 compactness we infer is in good agreement with small-angle X-ray scattering data for Rg and NMR measurement of hydrodynamic radius Rh. In contrast, owing to neglect or underappreciation of excluded volume, conventional procedures can significantly overestimate the probabilities of short end-to-end distances, leading to unphysically large smFRET-inferred Rg at high [GdmCl]. It follows that smFRET Sic1 data are incompatible with the presumed homogeneously expanded or contracted conformational ensembles in conventional procedures but are consistent with heterogeneous ensembles allowed by our subensemble method of inference. General ramifications of these findings for smFRET data interpretation are discussed.