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, ...

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 ...

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.

An innovative machine learning approach has been shown to rapidly predict multiple protein configurations. A new paper presents the method that predicts the relative populations of protein ...

In 2020, news headlines repeated John Moult’s words at the end of a stunning competition: Artificial intelligence had “solved” a long-standing grand challenge in biology, protein structure prediction.