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.

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

An international team led by Einstein Professor Cecilia Clementi in the Department of Physics at Freie Universität Berlin introduces a breakthrough in protein simulation. The study, published in the ...

In a new study published in Nature titled, “Custom CRISPR-Cas9 PAM variants via scalable engineering and machine learning,” researchers from Massachusetts General Hospital (MGH) and Harvard Medical ...

Scientists usually study the molecular machinery that controls gene expression from the perspective of a linear, two-dimensional genome—even though DNA and its bound proteins function in three ...

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 platform to engineer enzymes responsible for the formation of amide bonds as an effort toward developing biocatalysts for green chemistry. Amide bonds — a type of chemical bond formed between a ...

Marc Zimmer does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their ...

AI protein function prediction uses machine learning models trained on sequence and structural data to infer protein roles at ...