Fig. 1: Overview of Mac-Diff architecture.
a, Protein backbone geometric representation with L residues as an L × L × 5 tensor with pairwise Cβ distance, dihedral angle ω along two Cβ atoms, dihedral angles θ, and bond angles ϕ (direction of Cβ atom of one residue in a reference frame centred on the other residue), and a padding channel indicating sequence length. b, Mac-Diff workflow. The forward diffusion process iteratively injects noise to geometric tensor, and the backward process performs iterative denoising. The denoising network is a U-Net structure with five downsampling/upsampling stages, each stage with a ResNet block and a TransFormer block (self-attention and LAMA-attention). c, LAMA-attention, allowing each residue to attend only to neighbouring residues with high contact probability, updating residue-pair representations with highly relevant, contextualized sequence features for denoising. repr., denotes representation; seq, protein sequence; Conv, convolutional layer; TF, TransFormer block; RN, ResNet block; Dn, downsampling stage.
