{Reference Type}: Journal Article
{Title}: Unsupervised evolution of protein and antibody complexes with a structure-informed language model.
{Author}: Shanker VR;Bruun TUJ;Hie BL;Kim PS;
{Journal}: Science
{Volume}: 385
{Issue}: 6704
{Year}: 2024 Jul 5
{Factor}: 63.714
{DOI}: 10.1126/science.adk8946
{Abstract}: Large language models trained on sequence information alone can learn high-level principles of protein design. However, beyond sequence, the three-dimensional structures of proteins determine their specific function, activity, and evolvability. Here, we show that a general protein language model augmented with protein structure backbone coordinates can guide evolution for diverse proteins without the need to model individual functional tasks. We also demonstrate that ESM-IF1, which was only trained on single-chain structures, can be extended to engineer protein complexes. Using this approach, we screened about 30 variants of two therapeutic clinical antibodies used to treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We achieved up to 25-fold improvement in neutralization and 37-fold improvement in affinity against antibody-escaped viral variants of concern BQ.1.1 and XBB.1.5, respectively. These findings highlight the advantage of integrating structural information to identify efficient protein evolution trajectories without requiring any task-specific training data.