In a recent study published in the journal Nature Machine Intelligence, researchers developed "DeepGO-SE," a method to predict gene ontology (GO) functions from protein sequences using a large, ...

AI protein function prediction is reshaping how laboratories approach protein annotation, hypothesis generation, and translational research. As sequencing technologies continue to outpace experimental ...

A novel bioinformatics approach for classifying proteins according to similarity of function, rather than of sequence, is described in the April 12 PNAS. Albert Y. Lau and Daniel I. Chasman of ...

This fully updated volume explores a wide array of new and state-of-the-art tools and resources for protein function prediction. Beginning with in-depth overviews of essential underlying computational ...

In an ambitious collaboration, researchers added 1.8 million high-confidence protein complex structure predictions to the ...

Researchers from the University of Illinois at Urbana-Champaign and the University of California-Davis (UC Davis) are combining in vivo experimentation with computation for highly accurate prediction ...

A new artificial intelligence model can predict how different proteins may bind to DNA. A new artificial intelligence model developed by USC researchers and published in Nature Methods can predict how ...

Proteins sourced from microorganisms are attracting attention for their potential in biomanufacturing a variety of products, including pharmaceuticals, industrial enzymes, and diagnostic antibodies.

A new LMU study shows how proteins function reliably even without a stable 3D structure—and the crucial importance not only of short sequence motifs, but also of chemical characteristics. Many ...

The limitations imposed by evolution have implications for AI tools trained on existing sequences and de novo protein design ...