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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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Opportunities and challenges in design and optimization of protein function

Predicting protein interactions using geometric deep learning on protein surfaces

Investigating the intra-molecular and inter-molecular effects of post-translational modifications on intrinsically disordered protein regions and structured protein regions

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

Engineering novel protein interactions with therapeutic potential using deep learning-guided surface design

Development of tools and methods for protein identification: From single molecules to in vivo applications.

Prolamins' 3D structure: A new insight into protein modeling using the language of numbers and shapes

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

A Geometric Transformer for Structural Biology: Development and Applications of the Protein Structure Transformer

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