ICML 2025

Protein Structure Tokenization: Benchmarking and New Recipe
Xinyu Yuan * 1 2 Zichen Wang † 3 Marcus Collins 3 Huzefa Rangwala † ‡ 3
Abstract
Recent years have witnessed a surge in the devel-
opment of protein structural tokenization meth-
ods, which chunk protein 3D structures into dis-
crete or continuous representations. Structure to-
kenization enables the direct application of pow-
erful techniques like language modeling for pro-
tein structures, and large multimodal models to
integrate structures with protein sequences and
functional texts. Despite the progress, the capa-
bilities and limitations of these methods remain
poorly understood due to the lack of a unified
evaluation framework. We first introduce Struct-
TokenBench, a framework that comprehensively
evaluates the quality and efficiency of structure
tokenizers, focusing on fine-grained local sub-
structures rather than global structures, as typical
in existing benchmarks. Our evaluations reveal
that no single model dominates all benchmarking
perspectives. Observations of codebook under-
utilization led us to develop AminoAseed, a sim-
ple yet effective strategy that enhances codebook
gradient updates and optimally balances codebook
size and dimension for improved tokenizer utiliza-
tion and quality. Compared to the leading VQ-
VAE model ESM3, our method achieves an aver-
age of 6.31% performance improvement across
24 supervised tasks, with sensitivity and utiliza-
tion rates increased by 12.83% and 124.03%,
respectively. Source code and model weights
are
available
at
https://github.com/
KatarinaYuan/StructTokenBench.
*This work was completed while the author was an intern
at Amazon.
†Corresponding author.
‡Huzefa Rangwala is on
LOA as a Professor of Computer Science at George Mason
University.
This paper describes work performed at Ama-
zon.
1Mila - Quebec AI Institute
2University of Montreal
3Amazon.
Emails: Xinyu Yuan <xinyu.yuan@mila.quebec>,
Zichen
Wang
<wangzc921@gmail.com>,
Marcus
Collins
<collmr@...