Pre-Structural Embeddings for Protein-RNA Binding Affinity Prediction

Biomolecular foundation models such as AlphaFold 3 and Boltz-2 are typically used through their decoded structural outputs. However, many biologically important molecules, including RNA and intrinsically disordered proteins, populate conformational ensembles that cannot be fully represented by a single static structure. We propose that intermediate representations from these models contain rich pre-structural embeddings useful for downstream tasks involving conformationally flexible biomolecules. Including predicted-structure information is expensive and provides little value in this context. We validate this representation strategy on protein-RNA binding affinity prediction, a task where both partners are structurally heterogeneous and structure-based methods generalize poorly outside related examples. We introduce ZeroFold, a transformer-based model that integrates protein and RNA pre-structural embeddings from Boltz-2 through cross-modal attention to predict binding affinity directly from sequence. To ensure rigorous evaluation, we construct PRADB, a curated benchmark of 2,582 unique protein-RNA pairs with experimentally measured affinities, split at a strict 40% sequence identity threshold for both modalities. On the held-out test set, ZeroFold achieves a Spearman correlation of 0.64, approaching the empirical ceiling imposed by inter-replicate experimental variability. Under progressively stricter evaluations that remove training-set overlap with competing methods, ZeroFold performs consistently better than the leading structure-based predictor (CoPRA) and sequence-based predictor (DeePNAP), with the advantage widening as overlap is reduced. Ablations replacing pre-structural embeddings with language-model features or Boltz-2 distogram-derived structural features substantially degrade performance. These results support pre-structural embeddings as an optimal representation strategy for learning the interactions of flexible biomolecules.