Graph attention with energy features improves the generalizability of identifying functional sequences at a protein interface

Ash, Jonathan

doi:10.5281/zenodo.15548841

Published May 29, 2025 | Version v1

Data paper Open

Graph attention with energy features improves the generalizability of identifying functional sequences at a protein interface

Ash, Jonathan (Researcher)¹

1. Rutgers, The State University of New Jersey

Rosetta-generated structures of RBD variants from publication. Pre-computed ESM2 averages for all samples are also included for use in graph generation.

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esm_stats_bind.csv

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Name	Size	Download all
decoys_r1.tar.gz md5:afe5123db6164567d129ba932efbedfe	5.5 GB	Download
decoys_r2.tar.gz md5:7bb68946f780f493bde415f669042afd	865.8 MB	Download
esm_stats_bind.csv md5:8d4bf1a0d8af2bdb8c4be11182a377e8	545.3 MB	Preview Download

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Repository URL: https://github.com/JonathanEAsh/GraphAttentionRBD
Programming language: Python
Development Status: Active

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Zenodo

License: Creative Commons Attribution 4.0 International

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Technical metadata

Created: May 29, 2025
Modified: November 3, 2025

Graph attention with energy features improves the generalizability of identifying functional sequences at a protein interface

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Files

esm_stats_bind.csv

Files (6.9 GB)

Additional details

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