Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and
structure data have radically transformed computational protein design. New methods …

There has been considerable recent progress in designing new proteins using deep-
learning methods,,,,,,,–. Despite this progress, a general deep-learning framework for protein …

The Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway
serves as a paradigm for signal transduction from the extracellular environment to the …

Predicting the binding structure of a small molecule ligand to a protein--a task known as
molecular docking--is critical to drug design. Recent deep learning methods that treat …

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 …

Construction of a scaffold structure that supports a desired motif, conferring protein function,
shows promise for the design of vaccines and enzymes. But a general solution to this motif …

Designing protein sequences towards desired properties is a fundamental goal of protein
engineering, with applications in drug discovery and enzymatic engineering. Machine …

Many peptide hormones form an α-helix on binding their receptors,,–, and sensitive methods
for their detection could contribute to better clinical management of disease. De novo protein …

Protein inverse folding has attracted increasing attention in recent years. However, we
observe that current methods are usually limited to the CATH dataset and the recovery …

Allosteric modulation of protein function, wherein the binding of an effector to a protein
triggers conformational changes at distant functional sites, plays a central part in the control …