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A new age in protein design empowered by deep learning

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A new age in protein design empowered by deep learning

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Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences

Assessing and enhancing foldability in designed proteins

Protein language models trained on multiple sequence alignments learn phylogenetic relationships

Fast end-to-end learning on protein surfaces

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

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

Reviewing Challenges of Predicting Protein Melting Temperature Change Upon Mutation Through the Full Analysis of a Highly Detailed Dataset with High-Resolution Structures

Low-energy electron holography imaging of conformational variability of single-antibody molecules from electrospray ion beam deposition

Bottom-up de novo design of functional proteins with complex structural features