Most proteins in cells are composed of multiple folding units (or domains) to perform
complex functions in a cooperative manner. Relative to the rapid progress in single-domain …

The trRosetta (transform-restrained Rosetta) server is a web-based platform for fast and
accurate protein structure prediction, powered by deep learning and Rosetta. With the input …

The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of
proteins and their interactions, enabling a huge range of applications in protein modelling …

Deep-learning methods have revolutionized protein structure prediction and design but are
presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which …

Recent advances in machine learning have leveraged evolutionary information in multiple
sequence alignments to predict protein structure. We demonstrate direct inference of full …

AlphaFold2 revolutionized structural biology with the ability to predict protein structures with
exceptionally high accuracy. Its implementation, however, lacks the code and data required …

Large language models have recently been shown to develop emergent capabilities with
scale, going beyond simple pattern matching to perform higher level reasoning and …

Equilibrium fluctuations and triggered conformational changes often underlie the functional
cycles of membrane proteins. For example, transporters mediate the passage of molecules …

Protein structures can provide invaluable information, both for reasoning about biological
processes and for enabling interventions such as structure-based drug development or …

Attention-based models trained on protein sequences have demonstrated incredible
success at classification and generation tasks relevant for artificial-intelligence-driven …