Beyond native sequence recovery: Improved modeling of the sequence-energy landscape of protein structures.

ABSTRACT: Computational protein design using machine learning models has advanced rapidly since the introduction of AlphaFold2. There is now a suite of tools that enable in silico design of proteins with desired structures and properties. Most design workflows require fitting a designed backbone with a sequence that stabilizes it, and many machine learning sequence design models have been proposed. These models are trained to recover the native sequence paired with a known structure, a task known as native sequence recovery (NSR). Here, we demonstrate the limitations of optimizing a sequence design model only for NSR. We show that NSR is often misaligned with more important metrics of model performance: the compatibility of the generated sequence with the desired fold and the ability of the model to

SUBMITTER: Birnbaum F 

PROVIDER: S-EPMC12871252 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature