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 ...

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

A machine-learning algorithm to study the behavior of proteins within cells and to predict their ability to trigger neurodegenerative diseases such as Amyotrophic Lateral Sclerosis (ALS), Parkinson's, ...

The machine-learning algorithm studies the structure of a protein, analyzing its amino acidic sequences and considering its affinity for RNA. Thanks to this analysis, researchers can determine if the ...

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 ...

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.