Reconstruction of polymer structures from contact maps with Deep Learning

For any polymer, the euclidean distance map (D) is defined as a matrix where D_ij=d_ij² where d_ij is the distance between i and j. This contains all the necessary information to re-create the structure. However certain biological experiments, especially Hi-C or NOESY NMR, are only able to provide us with a contact map(C) containing a list of monomers that are within a certain cut-off distance (r_c). We propose a deep auto-encoder that is able to reconstruct D when only provided with C. We test this network on ensembles of structures generated by MD simulations. We show that a deep auto-encoder is capable of reconstructing polymer structures simply from the contact map information. We propose that this network can be applied to single-cell Hi-C maps to reconstruct chromosome structures in individual cells.