Enzyme engineering is limited by the challenge of rapidly generating and using large datasets of sequence-function relationships for predictive design. To address this challenge, we develop a machine ...

Traditional protein engineering methods, such as directed evolution, while effective, are often slow and labor-intensive. Advances in machine learning and automated biofoundry present new ...

Their overview highlights innovative methods based on B-factor analysis, ancestral sequence reconstruction (ASR), and machine learning (ML), providing tools to design enzymes that withstand high ...

CGSchNet, a fast machine-learned model, simulates proteins with high accuracy, enabling drug discovery and protein engineering for cancer treatment. Operating significantly faster than traditional all ...

A team of researchers has developed a method that could transform the field of protein engineering. The new approach, called AI-informed Constraints for protein Engineering (AiCE), enables rapid and ...

It has long been thought that protein function and stability are highly sensitive to changes in the composition of the internal structures, or protein cores. However, a large-scale experiment probing ...