Distilling Structural Representations into Protein Sequence Models
Jeffrey Ouyang-Zhang Chengyue Gong Yue Zhao Philipp Kraehenbuehl Adam Klivans Daniel Jesus Diaz
Protein language (or sequence) models, like the popular ESM2, are now widely used tools for extracting evolution-based protein representations and have achieved significant success on core downstream biological tasks.
A major open problem is how to obtain representations that best capture both the sequence evolutionary history and the atomic structural properties of proteins in general. 
We introduce **I**mplicit **S**equence **M**odel, a sequence-only input model with structurally-enriched representations that outperforms state-of-the-art sequence models on several well-studied benchmarks including mutation stability assessment and structure prediction. 
Our key innovations are a microenvironment-based Autoencoder for generating structure tokens and a self-supervised training objective that distills these tokens into ESM2's pre-trained model. 
Notably, we make ISM's structure-enriched weights easily accessible for any application using the ESM2 framework.
biology, proteins, sequence, structure, autoencoder, esm
we finetune esm to improve structural representations