Maria Littmann

This paper reports on the fourth CAFA challenge, showing improvements in Gene Ontology term prediction but highlighting challenges in new evaluation settings.

The paper uses AlphaFold2 to analyze protein structures from 21 organisms, identifying 26 novel superfamilies and expanding structural diversity insights.

The paper introduces CATHe, a method using protein language model embeddings to detect remote homologues for CATH superfamilies, achieving high accuracy and validating predictions with AlphaFold structures.

LambdaPP provides fast, accessible protein feature predictions including structure and function, using AI models and embeddings from protein Language Models.

The paper introduces bindAdjust, a method that improves binding predictions by incorporating structural information from AlphaFold2, enhancing recall for small molecule binding and expanding predictions to larger binding sites.

This paper introduces ProtTucker, a contrastive learning method using protein embeddings from pLMs to improve recognition of distant homologous relationships in the midnight zone of protein similarity.

The paper introduces bindEmbed21, a deep learning method using protein embeddings to predict binding residues for metal ions, nucleic acids, and small molecules, achieving high accuracy without using MSAs or structural data.

The paper improves the purity of protein functional families by clustering them using sequence embeddings, doubling the number of pure clusters per family.

PredictProtein provides protein sequence analysis tools, offering structure and function predictions with updates including deep learning and DNA/RNA/protein binding predictions.

This paper proposes using deep learning embeddings to transfer GO annotations based on protein sequence proximity rather than homology, achieving performance comparable to existing methods.