The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-
quality and freely accessible set of protein sequences annotated with functional information …

Breakthrough methods in machine learning (ML), protein structure prediction, and novel
ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models …

Simple Summary The rapidly advancing field of deep learning, specifically transformer-
based architectures and attention mechanisms, has found substantial applicability in …

The prediction of molecular phenotypes from DNA sequences remains a longstanding
challenge in genomics, often driven by limited annotated data and the inability to transfer …

Motivation To provide high quality, computationally tractable annotation of binding sites for
biologically relevant (cognate) ligands in UniProtKB using the chemical ontology ChEBI …

Adapting language models to protein sequences spawned the development of powerful
protein language models (pLMs). Concurrently, AlphaFold2 broke through in protein …

Sequence-based prediction of drug–target interactions has the potential to accelerate drug
discovery by complementing experimental screens. Such computational prediction needs to …

Prediction methods inputting embeddings from protein language models have reached or
even surpassed state-of-the-art performance on many protein prediction tasks. In natural …

The emergence of SARS-CoV-2 variants stressed the demand for tools allowing to interpret
the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational …

Experimental structures are leveraged through multiple sequence alignments, or more
generally through homology-based inference (HBI), facilitating the transfer of information …