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A computational framework for the design of optimal protein synthesis

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A computational framework for the design of optimal protein synthesis

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Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

Generative power of a protein language model trained on multiple sequence alignments

A new age in protein design empowered by deep learning

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

Scaffolding protein functional sites using deep learning

Ribo-DT: An automated pipeline for inferring codon dwell times from ribosome profiling data

A Rosetta-based protein design protocol converging to natural sequences

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

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