Distilling Structural Representations into Protein Sequence Models

Jeffrey Ouyang-Zhang, Chengyue Gong, Yue Zhao, Philipp Kraehenbuehl, Adam Klivans, Daniel Jesus Diaz

Keywords: biology, proteins, sequence, structure, autoencoder, esm

TL;DR: we finetune esm to improve structural representations

Abstract:

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.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Submission Number: 121