Protein function prediction and annotation represent critical challenges in the post‐genomic era. As high‐throughput sequencing continues to generate vast amounts of protein data, computational ...

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 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, ...

University of Missouri researchers have released the world's largest collection of protein models with quality assessment—a ...

A new artificial intelligence (AI) tool that draws logical inferences about the function of unknown proteins promises to help scientists unravel the inner workings of the cell. A new artificial ...

Three scientists were named winners of the 2024 Nobel Prize in Chemistry for their innovations in the fields of computational protein design and structure prediction. One half of the prize was awarded ...

Artificial intelligence has solved one of biology’s most stubborn mysteries: how proteins fold into their intricate three-dimensional shapes. But as the field shifts from prediction to application, a ...

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.

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

Yusuf Roohani, PhD, machine learning group lead at the Arc Institute, is among a team of researchers training artificial intelligence (AI) models with transcriptome data to predict how cell gene ...