Structural origins of DNA target selection and nucleobase extrusion by a DNA cytosine methyltransferase

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. 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.

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. We demonstrate inverse folding to be an effective unsupervised, structure-based sequence optimization strategy that also generalizes to multimeric complexes by implicitly learning features of binding and amino acid epistasis. Using this approach, we screened ~30 variants of two therapeutic clinical antibodies used to treat SARS-CoV-2 infection and achieved up to 26-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. In addition to substantial overall improvements in protein function, we find inverse folding performs with leading experimental success rates among other reported machine learning-guided directed evolution methods, without requiring any task-specific training data.

Bacterial DNA methyltransferase: A key to the epigenetic world with lessons learned from proteobacteria

Epigenetics modulates expression levels of various important genes in both prokaryotes and eukaryotes. These epigenetic traits are heritable without any change in genetic DNA sequences. DNA methylation is a universal mechanism of epigenetic regulation in all kingdoms of life. In bacteria, DNA methylation is the main form of epigenetic regulation and plays important roles in affecting clinically relevant phenotypes, such as virulence, host colonization, sporulation, biofilm formation et al. In this review, we survey bacterial epigenomic studies and focus on the recent developments in the structure, function, and mechanism of several highly conserved bacterial DNA methylases. These methyltransferases are relatively common in bacteria and participate in the regulation of gene expression and chromosomal DNA replication and repair control. Recent advances in sequencing techniques capable of detecting methylation signals have enabled the characterization of genome-wide epigenetic regulation. With their involvement in critical cellular processes, these highly conserved DNA methyltransferases may emerge as promising targets for developing novel epigenetic inhibitors for biomedical applications.