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Protein design as a challenge for peptide chemists

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Protein design as a challenge for peptide chemists

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A new age in protein design empowered by deep learning

De novo protein design by inversion of the AlphaFold structure prediction network

Indication of 310-Helix Structure in Gas-Phase Neutral Pentaalanine

Scaffolding protein functional sites using deep learning

Towards automating de novo protein design for novel functionalities: controlling protein folds and protein-protein interactions

Assessing and enhancing foldability in designed proteins

SPServer: split-statistical potentials for the analysis of protein structures and protein-protein interactions

On the use of direct-coupling analysis with a reduced alphabet of amino acids combined with super-secondary structure motifs for protein fold prediction

Assessment of transferable forcefields for protein simulations attests improved description of disordered states and secondary structure propensities, and hints at multi-protein systems as the next challenge for optimization

On the exploration of novel methodological approaches for immunogen design: case studies in influenza and hepatitis C