Exploring zero-shot structure-based protein fitness prediction
<p>This repository contains data used in Exploring zero-shot structure-based protein fitness<br>prediction.</p> <p>Directions to use this data can be found on <a href="https://github.com/gitter-lab/benchmarking-structure-based-models">our GitHub repository</a>.</p> <ol> <li><code>experimental_struct_artifacts</code> contains the experimentally determined structures for ProteinGym assays used in our analysis along with the reference file needed to generate ESM inverse folding predictions for these structures in ProteinGym.</li> <li><code>results</code> contains the prediction results obtained by running SSEmb on the 216 ProteinGym assays being considered in this study.</li> <li><code>test.tar.gz</code> contains all the structures from ProteinGym as well as MSAs generated using mmseqs2. To use this directory: <ul> <li>Setup SSEmb as directed in its <a href="https://github.com/KULL-Centre/_2023_Blaabjerg_SSEmb">repository</a></li> <li>Download this file and extract it in the data folder.</li> </ul> </li> </ol> <p> </p> <p> </p>
ShareScore
32/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 0
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