The accuracy of electrostatic interactions captured by AI protein structure prediction models.

A variant of the U1A protein containing four substitutions to ionizable residues was generated serendipitously due to a miscommunication. Biophysical measurements reveal this variant has twice the helical structure of wild-type U1A and is trimeric, unlike the monomeric wild type. In sharp contrast, structures predicted by deep-learning (AlphaFold2, RoseTTAFold2) and transformer-based tools (OmegaFold, ESMFold) are nearly identical to the wild-type (backbone RMSD < 1 Å). Surprisingly, these models predict ionizable residues buried within the nonpolar core, contradicting established physico-chemical principles. To explore this effect further, we generated sequences containing up to all twelve residues that make up the nonpolar core of U1A. Across thousands of sequences, and depending on the AI model used, the majority of predicted structures contained fully buried ionizable residues while still maintaining the overall U1A fold. We then examined two additional proteins of comparable size, acylphosphatase and the de novo designed TOP7 fold, and observed the same phenomenon: AI models frequently predicted structures with buried ionizable residues that nevertheless retained the parent fold. However, short (50 ns) molecular dynamics simulations with physics-based force fields (CHARMM/AMBER) rapidly relaxed these structures, exposing the ionizable residues. We conclude that while AI-based tools perform exceptionally on natural sequences, they do not reliably encode the physico-chemical principles governing ionizable residue placement. We propose including brief molecular dynamics simulations as a vital validation step for AI-generated structures.