  Representation learning and \emph{de novo} generation of proteins are pivotal
computational biology tasks. Whilst natural language processing (NLP)
techniques have proven highly effective for protein sequence modelling,
structure modelling presents a complex challenge, primarily due to its
continuous and three-dimensional nature. Motivated by this discrepancy, we
introduce an approach using a vector-quantized autoencoder that effectively
tokenizes protein structures into discrete representations. This method
transforms the continuous, complex space of protein structures into a
manageable, discrete format with a codebook ranging from 4096 to 64000 tokens,
achieving high-fidelity reconstructions with backbone root mean square
deviations (RMSD) of approximately 1-5 \AA. To demonstrate the efficacy of our
learned representations, we show that a simple GPT model trained on our
codebooks can generate novel, diverse, and designable protein structures. Our
approach not only provides representations of protein structure, but also
mitigates the challenges of disparate modal representations and sets a
foundation for seamless, multi-modal integration, enhancing the capabilities of
computational methods in protein design.
