{Reference Type}: Journal Article
{Title}: Validation of de novo designed water-soluble and transmembrane β-barrels by in silico folding and melting.
{Author}: Hermosilla AM;Berner C;Ovchinnikov S;Vorobieva AA;
{Journal}: Protein Sci
{Volume}: 33
{Issue}: 7
{Year}: 2024 Jul
{Factor}: 6.993
{DOI}: 10.1002/pro.5033
{Abstract}: In silico validation of de novo designed proteins with deep learning (DL)-based structure prediction algorithms has become mainstream. However, formal evidence of the relationship between a high-quality predicted model and the chance of experimental success is lacking. We used experimentally characterized de novo water-soluble and transmembrane β-barrel designs to show that AlphaFold2 and ESMFold excel at different tasks. ESMFold can efficiently identify designs generated based on high-quality (designable) backbones. However, only AlphaFold2 can predict which sequences have the best chance of experimentally folding among similar designs. We show that ESMFold can generate high-quality structures from just a few predicted contacts and introduce a new approach based on incremental perturbation of the prediction ("in silico melting"), which can reveal differences in the presence of favorable contacts between designs. This study provides a new insight on DL-based structure prediction models explainability and on how they could be leveraged for the design of increasingly complex proteins; in particular membrane proteins which have historically lacked basic in silico validation tools.