This research evaluates existing protein structure tokenization methods and introduces an improved approach for converting complex 3D protein structures into computational representations.

• Benchmarks multiple tokenization methods to identify their strengths and limitations
• Addresses gaps in understanding how different tokenization approaches perform
• Introduces a new tokenization method optimized for biological applications
• Enables more effective application of language modeling techniques to protein structures

This advancement matters for biology by allowing researchers to better analyze protein structures and leverage powerful AI techniques, potentially accelerating drug discovery and understanding protein functions.

Protein Structure Tokenization: Benchmarking and New Recipe