Electro-Prot: Learning Protein Representations from Electrostatics

Protein Language Models (PLMs) trained on sequence, as well as structure-aware variants that integrate structural information, have emerged as powerful tools to learn protein representations applicable to a wide range of downstream tasks. However, neither of these frameworks explicitly accounts for the electrostatic interactions that govern protein functions. Electrostatic potentials are the primary determinants of binding, recognition, long-range interactions, and catalysis, and serve as a cornerstone of our understanding of biophysical interactions in proteins. We investigate whether a continuous electrostatic field can serve as a pretraining target to encode general protein representations. We present Electro-Prot, a 6.7 million-parameter encoder-decoder transformer model trained to predict Poisson-Boltzmann electrostatic potentials at arbitrary points around a protein. Coordinate queries cross-attend to atoms to predict the electrostatic potential as a continuous field. Electro-Prot attains $r=0.969$ (MAE = 0.81 kT/e) on a held-out test set. On downstream tasks, our embeddings match or outperform PLMs in interaction-centric tasks such as protein-protein interfacing and mutation induced change in free binding predictions, despite utilizing a dataset that is orders of magnitude smaller. Combining Electro-Prot and PLM embeddings improves results in structure similarity, protein-protein interface, Gene Ontology, and binding site detection. suggesting that Electro-Prot learns a complementary signal not captured in evolutionary statistics. Furthermore, we investigated protein-protein binding free energy predictions, where we outperformed PLMs in mutation-induced changes in binding affinity. Our results suggest that electrostatic pretraining provides a viable, parameter- and data- efficient signal for learning representations relevant to interaction-centric tasks.