J. Gut

Synthetic sequence alignments as programmable probes of learned conformational landscapes in deep learning protein structure predictors

Synthetic MSAs are suggested as a generalizable framework for dissecting the conformational landscapes encoded by deep learning structure predictors, with direct implications for understanding model behavior and accessing biologically relevant hidden states.