Proteins are nature's primary building blocks for the construction of sophisticated molecular
machines and dynamic materials, ranging from protein complexes such as photosystem II …

CASP (critical assessment of structure prediction) assesses the state of the art in modeling
protein structure from amino acid sequence. The most recent experiment (CASP13 held in …

The prediction of interresidue contacts and distances from coevolutionary data using deep
learning has considerably advanced protein structure prediction. Here, we build on these …

The computational de novo protein design is increasingly applied to address a number of
key challenges in biomedicine and biological engineering. Successes in expanding …

The protein design problem is to identify an amino acid sequence that folds to a desired
structure. Given Anfinsen's thermodynamic hypothesis of folding, this can be recast as …

Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and
powerful computational resources, impacting many fields, including protein structural …

Protein design has come of age, but how will it mature? In the 1980s and the 1990s, the
primary motivation for de novo protein design was to test our understanding of the …

Learning the design patterns of proteins from sequences across evolution may have
promise toward generative protein design. However it is unknown whether language …

Inspired by the increasing use of artificial intelligence (AI) to augment humans, researchers
have studied human–AI systems involving different tasks, systems and populations. Despite …

The role of information systems (IS) as representations of real-world systems is changing in
an increasingly digitalized world, suggesting that conceptual modeling is losing its …