We propose an AutoEncoder-based framework for reconstructing protein shapes from time-resolved SWAXS data. The model is trained on physically plausible protein deformations generated using AFsample2 and represented as 3D voxel models of proteins. By learning the deformation space, the trained network can be incorporated into a framework capable of generating candidate structures guided by fitting to time-resolved SWAXS profiles, enabling shape reconstruction directly from experimental measurements.