In my lab we are trying to extract spatial features from protein structures. The software we develop makes use of CUDA for all heavy number-lifting, thus we are limited by the GPU's memory (12GB). Using standard voxel-based 3D-representations proved to be too memory hungry, hence we are trying to find a way to reduce the dimensionality to 2D, while preserving as much spatial information as possible. Our absolute goal is to preserve spatial colocalisation of different chains and secondary structures, to understand how important that is during folding. Standard techniques, such as PCA and PCoA on euclidian distances between amino acids, seem to preserve too little information on chain folds. We believe there should be a better way of doing that, but, having little experience in structural biology, we struggle to find any relevant work regarding this issue. Can you recommend any methods or relevant readings?