ESFold: Adapting Biomolecular Structure Models with Experimental Signals

Protein–RNA interactions are fundamental to gene regulation and RNA function, yet their structural modeling is limited by scarce experimentally resolved complexes. In contrast, enhanced crosslinking and immunoprecipitation (eCLIP) assays provide abundant interaction evidence but lack atomic resolution. Here we introduce ESFold, a two-stage post-training framework built on Protenix that integrates eCLIP signals into protein–RNA structure modeling. In the first stage, supervised post-training combines structural and eCLIP data to learn binding-aware representations. In the second stage, we adapt Diffusion Negative-aware Fine-Tuning (DiffusionNFT) to molecular structure generation, using eCLIP agreement and structural plausibility as feedback to optimize the distribution of generated conformations. We construct 265,070 candidate protein–RNA pairs spanning 53,014 RNA windows and 84 proteins. On a protein–RNA interface structure benchmark, ESFold improves the prediction success rate from 61.43% with Protenix to 74.29% and reduces interface RMSD from 7.61 Å to 5.02 Å. On a held-out test set of 190 experimental binding conditions, peak F1 averaged across RNA-binding proteins (RBPs) improves from 0.332 to 0.459. These gains are achieved while broadly maintaining Protenix’s performance on structural prediction tasks beyond protein–RNA complexes. Together, the findings highlight experimental-signal post-training as an approach to integrating functional genomics into three-dimensional biomolecular modeling.