The framework predicts how proteins will function with several interacting mutations and finds combinations that work well ...

Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.

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.

Fusion oncoproteins arise when a gene fuses with another gene and acquires new abilities. Such abilities can include the formation of biomolecular condensates, "droplets" of concentrated proteins, DNA ...

The search space for protein engineering grows exponentially with complexity. A protein of just 100 amino acids has 20100 ...

Researchers in the Nanoscience Center at the University of Jyväskylä, Finland, have developed a pioneering computational model that could expedite the use of nanomaterials in biomedical applications.

Industrial yeasts are a powerhouse of protein production, used to manufacture vaccines, biopharmaceuticals, and other useful compounds.

For decades, scientists have relied on structure to understand protein function. Tools like AlphaFold have revolutionized how researchers predict and design folded proteins, allowing for new ...

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 generalizable ML framework predicts protein interactions with ligand-stabilized gold nanoclusters, supporting faster design of bioimaging, sensing and drug delivery materials. (Nanowerk News) The ...