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Title: Distilling Structural Representations into Protein Sequence Models
Abstract Protein language models, like the popular ESM2, are widely used tools for extracting evolution-based protein representations and have achieved significant success on downstream biological tasks. Representations based on sequence and structure models, however, show significant performance differences depending on the downstream task. A major open problem is to obtain representations that best capture both the evolutionary and structural properties of proteins in general. Here we introduceImplicitStructureModel(ISM), 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. We have madeISM’s structure-enriched weights easily available: integrating ISM into any application using ESM2 requires changing only a single line of code. Our code is available athttps://github.com/jozhang97/ISM.  more » « less
Award ID(s):
2505865
PAR ID:
10631086
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
bioRxiv
Date Published:
Format(s):
Medium: X
Institution:
bioRxiv
Sponsoring Org:
National Science Foundation
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