In-depth analysis of subclass-specific conformational preferences of IgG antibodies

Abstract

IgG subclass-specific differences in biological function and in vitro stability are often referred to variations in the conformational flexibility, while this flexibility has rarely been characterized. Here, small-angle X-ray scattering data from IgG1, IgG2 and IgG4 antibodies, which were designed with identical variable regions, were thoroughly analysed by the ensemble optimization method. The extended analysis of the optimized ensembles through shape clustering reveals distinct subclass-specific conformational preferences, which provide new insights for understanding the variations in physical/chemical stability and biological function of therapeutic antibodies. Importantly, the way that specific differences in the linker region correlate with the solution structure of intact antibodies is revealed, thereby visualizing future potential for the rational design of antibodies with designated physicochemical properties and tailored effector functions. In addition, this advanced computational approach is applicable to other flexible multi-domain systems and extends the potential for investigating flexibility in solutions of macromolecules by small-angle X-ray scattering.

Keywords: IgG antibody; shape clustering; small-angle X-ray scattering (SAXS); solution conformation; structure modelling.

Figure 1

Solution scattering data. (a) A comparison of the primary sequence of hinge regions in the three IgG subclasses. The locations of the small rigid bodies are highlighted within the small blue box. (b) Scattering intensity plot and (c) Kratky plot of SAXS data collected on the IgGs in 50 mM Na phosphate, 100 mM NaCl, pH 7.4. IgG1 (blue), IgG2 (red) and IgG4 (green). (d) The pair distance distribution functions from the indirect Fourier transformation of the scattering intensity.

Figure 2

Results of ensemble optimization. (a)–(c) Fits between the calculated scattering curve from the best ensemble (coloured, selected by EOM) and the experimental data (black). (d)–(f) D _max distributions of the conformers in the pool (black) and the optimized ensembles (coloured). (g) Comparison of the D _max and (h) of the R _g distributions in the optimized ensembles of the three IgG subclasses. (i) A representative structural model of IgG1 generated by RANCH.

Figure 3

Superimposed structures from the conformational subsets. The clusters for (a) IgG1, (b) IgG2 and (c) IgG4 are shown in decreasing order of overall occurrence, which is noted in the lower right-hand corner of each plot. The domains pointing downwards are the regions mostly occupied by the Fc domain.

Figure 4

Putative conformational equilibria of (a) IgG1, (b) IgG2 and (c) IgG4. Representative structures are selected from the corresponding clusters. The number of the cluster is noted in the lower right-hand corner in red. The structure types and their overall percentage in the optimized structural pool are noted above each group.