Assessing State-Specific Accuracy of Cofolding Models for Kinases and GPCRs
AI-driven cofolding models have emerged as powerful tools for predicting protein-ligand complexes, yet whether ligand placement faithfully captures the conformational states of dynamic proteins remains unclear. Here we show that cofolding adaptively remodels binding pockets around bound ligands, but that this local accuracy is frequently decoupled from recovery of the broader conformational state. We benchmark four models, AlphaFold3, RosettaFold3, Boltz-2, and Chai-1, against a set of kinases and class A G protein-coupled receptors (GPCRs), protein families whose pharmacology depends on well-defined structural states. We find that even when ligand root-mean-square deviation (RMSD) is low, critical state markers, including kinase activation-loop geometries and GPCR intracellular arrangements, are frequently mispredicted. Incorporating state-annotated templates and filtered multiple sequence alignments (MSAs) improves conformational recovery in selected cases, yet weakly impacts others. Furthermore, while orthosteric ligand placement is generally reliable, allosteric binders expose a consistent blind spot across all models. These findings establish conformational decoupling as a fundamental limitation of current cofolding approaches, with direct implications for state-selective drug design.