Unsupervised evolution of protein and antibody complexes with a structure-informed language model.

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.

Inverse folding of protein complexes with a structure-informed language model enables unsupervised antibody evolution.

Large language models trained on sequence information alone are capable of learning 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 and trained on the inverse folding problem can guide evolution for diverse proteins without needing to explicitly model individual functional tasks.

Efficient evolution of human antibodies from general protein language models.

Natural evolution must explore a vast landscape of possible sequences for desirable yet rare mutations, suggesting that learning from natural evolutionary strategies could guide artificial evolution. Here we report that general protein language models can efficiently evolve human antibodies by suggesting mutations that are evolutionarily plausible, despite providing the model with no information about the target antigen, binding specificity or protein structure. We performed language-model-guided affinity maturation of seven antibodies, screening 20 or fewer variants of each antibody across only two rounds of laboratory evolution, and improved the binding affinities of four clinically relevant, highly mature antibodies up to sevenfold and three unmatured antibodies up to 160-fold, with many designs also demonstrating favorable thermostability and viral neutralization activity against Ebola and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pseudoviruses.

Enhancing proteasomal processing improves survival for a peptide vaccine used to treat glioblastoma.

Despite its essential role in antigen presentation, enhancing proteasomal processing is an unexploited strategy for improving vaccines. pepVIII, an anticancer vaccine targeting EGFRvIII, has been tested in several trials for glioblastoma. We examined 20 peptides in silico and experimentally, which showed that a tyrosine substitution (Y6-pepVIII) maximizes proteasome cleavage and survival in a subcutaneous tumor model in mice.