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. 2010 May 4;107(18):8183-8.
doi: 10.1073/pnas.0911107107. Epub 2010 Apr 19.

Net charge per residue modulates conformational ensembles of intrinsically disordered proteins

Affiliations

Net charge per residue modulates conformational ensembles of intrinsically disordered proteins

Albert H Mao et al. Proc Natl Acad Sci U S A. .

Abstract

Intrinsically disordered proteins (IDPs) adopt heterogeneous ensembles of conformations under physiological conditions. Understanding the relationship between amino acid sequence and conformational ensembles of IDPs can help clarify the role of disorder in physiological function. Recent studies revealed that polar IDPs favor collapsed ensembles in water despite the absence of hydrophobic groups--a result that holds for polypeptide backbones as well. By studying highly charged polypeptides, a different archetype of IDPs, we assess how charge content modulates the intrinsic preference of polypeptide backbones for collapsed structures. We characterized conformational ensembles for a set of protamines in aqueous milieus using molecular simulations and fluorescence measurements. Protamines are arginine-rich IDPs involved in the condensation of chromatin during spermatogenesis. Simulations based on the ABSINTH implicit solvation model predict the existence of a globule-to-coil transition, with net charge per residue serving as the discriminating order parameter. The transition is supported by quantitative agreement between simulation and experiment. Local conformational preferences partially explain the observed trends of polymeric properties. Our results lead to the proposal of a schematic protein phase diagram that should enable prediction of polymeric attributes for IDP conformational ensembles using easily calculated physicochemical properties of amino acid sequences. Although sequence composition allows the prediction of polymeric properties, interresidue contact preferences of protamines with similar polymeric attributes suggest that certain details of conformational ensembles depend on the sequence. This provides a plausible mechanism for specificity in the functions of IDPs.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Inventory of protamine sequences. For each protamine, the columns show numeric and graphic identifiers, amino acid sequence, number of residues, UniProtKB accession code, f+, f-, mean Kyte–Doolittle hydropathy score, and the minimum VSL2B disorder prediction score over all residues. Sequences are sorted by their net charge per residue, f+ - f-. The symbols shown here are used throughout the paper (Figs. 2–8) as protamine identifiers. Note that filled shapes (solid diamonds, circles, and squares) denote polyelectrolytes, whereas thin or hollow shapes denote polyampholytes.
Fig. 8.
Fig. 8.
Selected ensemble-average contact maps. Protamines 8 and 9 are shown in the first column, and protamines 11 and 12 are shown in the second column. Each contact map is annotated with a structure taken from the simulated ensemble and is intended to assist in visual interpretation of the contact maps.
Fig. 7.
Fig. 7.
Ensemble-average values for PII propensity plotted against net charge per residue. Error bars denote the SEM.
Fig. 6.
Fig. 6.
Steady-state fluorescence anisotropy r plotted against the net charge per residue. The data convolve contributions from size and shape, and this explains the increased anisotropy of protamine 8, which is a 44-mer, vis-à-vis those with higher net charge per residue.
Fig. 5.
Fig. 5.
Comparison of translational diffusion coefficients obtained from analysis of simulation results (ordinate) and FCS experiments (abscissa). The black line is the result of linear regression. The regression parameters are such that calculated D = 11.2 μm2/s + 0.94 × measured D. The relevant quantities for comparing numbers from simulation and experimental data are the Pearson r value, which is 0.96 and the rmsd between calculated and measured values of D, which is 7.05 μm2/s. Error bars denote the SEM.
Fig. 4.
Fig. 4.
Scaling of the ensemble-average internal distances, 〈Rij〉, between residues i and j plotted against chain separation, |j-i|. Capping groups at the N and C termini were included in all calculations of internal distances. Gray squares and circles show data obtained from reference simulations for atomistic self-avoiding random walks and self-attracting versions of sequences 16 and 7, respectively. For the self-avoiding random walks, all interactions except the nonbonded steric repulsions are turned off; the self-attracting reference also includes the van der Waals dispersions. Gray diamonds denote the internal scaling profile for a reference rod-like chain. The latter data were obtained from a fully extended conformation for a 25-residue polyarginine chain with all backbone and side chain dihedral angles in trans.
Fig. 3.
Fig. 3.
Asphericity δ plotted against net charge per residue. Error bars denote the SEM.
Fig. 2.
Fig. 2.
Normalized 〈Rg〉 plotted against net charge per residue. To enable comparisons between polypeptides of different length, the 〈Rg〉 values obtained using the full ABSINTH Hamiltonian were divided by that of the same protamine simulated as a self-avoiding random walk. Error bars denote the standard error of the mean (SEM).
Fig. 9.
Fig. 9.
Proposed schematic phase diagram for the single chain phase behavior of unbound, single domain proteins. The three-dimensional sequence space is defined by f+, f-, and mean hydropathy. The space is a pyramid instead of a cube because high hydropathy and high fractions of charged residues are mutually exclusive. The boundary separating folded proteins from IDPs is a three-dimensional rendering of the results from Uversky et al. (21). Within the intrinsically disordered region, the boundaries separating disordered globules from swollen coils are extrapolated from the results of the present study.

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