BindEnergyCraft: Casting Protein Structure Predictors as Energy-Based Models for Binder Design
Journal:
arXiv
Published Date:
May 27, 2025
Abstract
Protein binder design has been transformed by hallucination-based methods
that optimize structure prediction confidence metrics, such as the interface
predicted TM-score (ipTM), via backpropagation. However, these metrics do not
reflect the statistical likelihood of a binder-target complex under the learned
distribution and yield sparse gradients for optimization. In this work, we
propose a method to extract such likelihoods from structure predictors by
reinterpreting their confidence outputs as an energy-based model (EBM). By
leveraging the Joint Energy-based Modeling (JEM) framework, we introduce
pTMEnergy, a statistical energy function derived from predicted inter-residue
error distributions. We incorporate pTMEnergy into BindEnergyCraft (BECraft), a
design pipeline that maintains the same optimization framework as BindCraft but
replaces ipTM with our energy-based objective. BECraft outperforms BindCraft,
RFDiffusion, and ESM3 across multiple challenging targets, achieving higher in
silico binder success rates while reducing structural clashes. Furthermore,
pTMEnergy establishes a new state-of-the-art in structure-based virtual
screening tasks for miniprotein and RNA aptamer binders.
