Re-engineering of a carotenoid-binding protein based on NMR structure.

Recently, a number of message passing neural network (MPNN)-based methods have been introduced that, based on backbone atom coordinates, efficiently recover native amino acid sequences of proteins and predict modifications that result in better expressing, more soluble, and stable variants. However, usually, X-ray structures, or artificial structures generated by algorithms trained on X-ray structures, were employed to define target backbone conformations. Here, we show that commonly used algorithms ProteinMPNN and SolubleMPNN display low sequence recovery on structures determined using NMR.