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| Main Authors: | , , , |
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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.18890480 |
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Table of Contents:
- <p>Data repository — Computational pipeline for in silico identification of novel secretory peptides interacting with SecA in Escherichia coli</p> <p>GitHub repository: https://github.com/malegria01/IdentificationSecretoryPeptides<br> <br>Description: </p> <p>This dataset contains large files associated with the computational pipeline described in Cisneros Mandujano et al. (2026). Due to size constraints, these files are deposited here as a supplement to the GitHub repository.</p> <p> Included files:</p> <p><br> - 1.3_signalP_output.json — Full SignalP 6.0 prediction output (JSON format) for toxin protein sequences from gram-negative bacteria.<br> - 2_AlphaFold_Control_SPs_PDBs.zip — AlphaFold structural models (PDB format) for 146 control signal peptides.<br> - 2_AlphaFold_Toxin_SPs_PDBs.zip — AlphaFold structural models (PDB format) for 917 toxin-derived signal peptide candidates.<br> - 2.1_STRIDE_SecondaryStructure_results.zip — STRIDE secondary structure assignments for all 917 toxin SP models.<br> - 4_5_Input_clusters_PyDockEneRes_MAPIYA.zip — HADDOCK cluster PDB files used as input for MAPIYA and PyDockEneRes interface analysis (7 representative<br> SecA/SP complexes + PDB:2VDA reference).</p>