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 ...

The search space for protein engineering grows exponentially with complexity. A protein of just 100 amino acids has 20 100 possible variants—more combinations than atoms in the observable universe.

Non-canonical amino acids can expand the scope of proteins available for therapeutics and machine learning platforms can ...

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 ...

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 ...