A Implementation details

A.1 Encoder network architecture We adopt a similar encoder network (Figure S1) as RDE to transform the structural context of mutations in the interface to a conditional vector used by the generative process of side-chain conformations. We define the structural context as the 128 residues in closest proximity to the mutation sites. The input features can be grouped into single node features and pair edge features. The node features include amino acid types, backbone torsion angles, and local atom coordinates for each amino acid, while the edge features include pair distance and relative sequence position between two amino acids. The input features are first fed into MLP layers (denoted as Transition layer in Figure S1) and then combined with the spatial backbone frames to pass through the Invariant Point Attention Module (IPA), an SE(3)-invariant network proposed in AlphaFold2 [Jumper et al., 2021].