In silico validation of de novo designed proteins withdeep learning (DL)-based structure prediction algorithms has becomemainstream. However, formal evidence that high-confidence predictions lead to higher chances of experimental success is lacking. We used experimentally characterized \emph{de novo} designs to show that AlphaFold2 and ESMFold excel at different tasks. ESMFold can identify designs generated based on high-quality (designable) backbones. However, only AlphaFold2 can predict which sequences are more likely to folding among similar designs. We show that ESMFold can predict high-quality structures from just a few 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 still lack many basic in silico design and validation tools.