Interfaces with strong hallucination-stage i_pTM consistently fail to survive MPNN resequencing on a hard, non-aromatic epitope: expected behavior or tuning issue?

Hello,
thank you for developing and sharing BindCraft. I have encountered some issues in my campaign and I would like to ask for your advice.

Environment: Google Colab
Target protein model: AF3 prediction (PTM: 0.56)

Parameters:

• default_4stage_multimer_hardtarget.json (under default AF2 prediction protocol the most confident trajectories were the ones where the target itself deformed most, hardtarget solved this problem)

• default_filters.json

• template_protocol: Default (we would like to use the binder to stabilize the target in a closed conformation).

Target description: protein of 236 residues formed by two domains (roughly 120 + 100 aa) joined by a short flexible linker, cross-validated against XL-MS crosslinks. AF3 alone gives inconsistent inter-domain orientation across seeds, the target model was selected based on conformation and hotspots (see below). The protein contains no Trp, no Met, all 5 Tyr are buried and the vast majority of exposed residues are charged or polar.

Goal: a bidentate binder that bridges both domains across their ~24 Å interdomain gap.

Hotspots: selected via SASA + Cα distance-to-opposite-domain + pLDDT filtering, split across both domains. It is difficult to select hotspots with the suggested target residues.

Problem observed: every hallucination trajectory that reports high pLDDT/i_pTM/i_pAE at the trajectory stage fails after MPNN resequencing. failure_csv.csv shows i_pTM/i_pAE as the overwhelming dominant rejection category (e.g., 180/180 and 344/354 in two successive batches), with pLDDT/pTM playing a much smaller role, and 0 MPNN designs ever reaching Rosetta-stage scoring. This holds even for trajectories with i_pTM up to 0.85 at the hallucination stage.

Questions: before committing to a full campaign of 1000 to 10000 trajectoriesIs, we ask for an opinion on these considerations:

• Is the pattern "confident hallucination but MPNN-fragile interface" a known/expected failure mode for low-signal (non-aromatic, charged-dominated) epitopes, or does it suggest something is misconfigured on our end?
• Would raising weights_con_inter relative to weights_iptm/weights_pae_inter be a sensible way to push toward more MPNN-robust interfaces?
• Would switching from MPNNsol to MPNN help in any way, since the target exposes negatively charged residues?

I thank you in advance.

No source file or test is identified. Start by reviewing default_4stage_multimer_hardtarget.json and default_filters.json, then inspect failure_csv.csv alongside the MPNNsol/MPNN and Rosetta-stage configuration entry points. Done means determining whether the reported post-resequencing rejection is expected, misconfigured, or addressable through the proposed weight or model changes.