Artificial intelligence-driven advances in protein structure prediction in recent years have
raised the question: has the protein structure-prediction problem been solved? Here, with a …

Since its public release in 2021, AlphaFold2 (AF2) has made investigating biological
questions, by using predicted protein structures of single monomers or full complexes, a …

Deep learning generative approaches provide an opportunity to broadly explore protein
structure space beyond the sequences and structures of natural proteins. Here, we use deep …

The greatly improved prediction of protein 3D structure from sequence achieved by the
second version of AlphaFold in 2020 has already had a huge impact on biological research …

A crucial component in structure-based drug discovery is the availability of high-quality three-
dimensional structures of the protein target. Whenever experimental structures were not …

In the early 2000s, Tawfik presented his 'New View'on enzyme evolution, highlighting the
role of conformational plasticity in expanding the functional diversity of limited repertoires of …

Self-supervised pretraining on protein sequences has led to state-of-the art performance on
protein function and fitness prediction. However, sequence-only methods ignore the rich …

De novo protein design enhances our understanding of the principles that govern protein
folding and interactions, and has the potential to revolutionize biotechnology through the …

De novo protein design is a longstanding fundamental goal of synthetic biology, but has
been hindered by the difficulty in reliable prediction of accurate high-resolution protein …

The recent success of AlphaFold2 (AF2) and other deep learning (DL) tools in accurately
predicting the folded three-dimensional (3D) structure of proteins and enzymes has …