Due to their high target specificity and binding affinity, therapeutic antibodies are currently
the largest class of biotherapeutics. The traditional largely empirical antibody development …

Deep learning approaches have produced substantial breakthroughs in fields such as
image classification and natural language processing and are making rapid inroads in the …

Advances in DNA sequencing and machine learning are providing insights into protein
sequences and structures on an enormous scale. However, the energetics driving folding …

Protein structure and function is determined by the arrangement of the linear sequence of
amino acids in 3D space. We show that a deep graph neural network, ProteinSolver, can …

Proteins fold into unique native structures stabilized by thousands of weak interactions that
collectively overcome the entropic cost of folding. Although these forces are “encoded” in the …

De novo protein design holds promise for creating small stable proteins with shapes
customized to bind therapeutic targets. We describe a massively parallel approach for …

The success of protein engineering and design has extensively expanded the protein space,
which presents a promising strategy for creating next-generation proteins of diverse …

Progress in technology and algorithms throughout the past decade has transformed the field
of protein design and engineering. Computational approaches have become well-engrained …

We used the deep learning tool ProteinMPNN to redesign ubiquitin (Ub) as a specific and
functionally stimulating/enhancing binder of the Rsp5 E3 ligase. We generated 20 …

The genome-editing Cas9 protein uses multiple amino-acid residues to bind the target DNA.
Considering only the residues in proximity to the target DNA as potential sites to optimise …