Protein design with a machine-learned potential about backbone designability

Proteins are fundamental molecules that mediate diverse biological processes, and protein design can shed light on the molecular mechanisms underlying their biological functions. Huang and colleagues have developed a sequence-independent statistical model for de novo protein design using neural networks (NNs) to learn the distribution of backbone structures with minimal side-chain information. Proteins are fundamental molecules that mediate diverse biological processes, and protein design can shed light on the molecular mechanisms underlying their biological functions. Huang and colleagues have developed a sequence-independent statistical model for de novo protein design using neural networks (NNs) to learn the distribution of backbone structures with minimal side-chain information.