Finetuning RFdiffusion for antibody de novo design

Hi,
I refer to the paper " Atomically accurate de novo design of single-domain antibodies". There is a section "Fine-tuning RFdiffusion for antibody design":

"RFdiffusion uses the AlphaFold214/RF2 frame representation of protein backbones comprising
the Cɑ coordinate and N-Cɑ-C rigid orientation for each residue. During training, a noising
schedule is used that, over a set number of “timesteps” (T), corrupts the protein frames to
distributions indistinguishable from random distributions (Cɑ coordinates are corrupted with
3D Gaussian noise, and residue orientations with Brownian motion on SO3). During training,
a PDB structure and a random timestep (t) are sampled, and t noising steps are applied to the
structure. RFdiffusion predicts the de-noised (pX0) structure at each timestep, and a mean
squared error (m.s.e.) loss is minimized between the true structure (X0) and the prediction. At
inference time, translations are sampled from the 3D Gaussian and uniform rotational
distributions (XT) and RFdiffusion iteratively de-noises these frames to generate a new protein
structure.
To explore the design of antibodies, we fine-tuned RFdiffusion predominantly on antibody
complex structures (Fig. 1; Methods). At each step of training, an antibody complex structure
is sampled, along with a random timestep (t), and this number of noise steps are added to
corrupt the antibody structure (but not the target structure). To permit specification of the
framework structure and sequence at inference time, the framework sequence and structure
are provided to RFdiffusion during training (Fig. 1B). Because it is desirable for the rigid body
position (dock) between antibody and target to be designed by RFdiffusion along with the CDR
loop conformations, the framework structure is provided in a global-frame-invariant manner
during training (Fig. 1C). We utilize the “template track” of RF/RFdiffusion to provide the
framework structure as a 2D matrix of pairwise distances and dihedral angles between each
pair of residues (a representation from which 3D structures can be accurately recapitulated)15,
(Extended Data Fig. 1A). The framework and target templates specify the internal structure of
each protein chain, but not their relative positions in 3D space (in this work we keep the
sequence and structure of the framework region fixed, and focus on the design solely of the
CDRs and the overall rigid body placement of the antibody against the target). In vanilla
RFdiffusion, de novo binders can be targeted to specific epitopes at inference time through
training with an additional one-hot encoded “hotspot” feature, which provides some fraction of
the residues the designed binder should interact with. For antibody design, where we seek
CDR-loop-mediated interactions, we adapt this feature to specify residues on the target protein
with which CDR loops interact (Fig. 1D). "

Is there an example of the RFdiffusion commands for this finetuning? In particular, can we see an example of the framework structure that is a 2D matrix of pairwise distances and dihedral angles between each pair of residues?

No file, test, or entry point is named. Start by locating the RFdiffusion finetuning commands and template-track handling, then compare the available interfaces with the antibody-design workflow described in the issue. Done means documenting a runnable finetuning example and showing an example framework representation with pairwise distances and dihedral angles.