A strategy to validate protein function predictions in vitro - accompanying data

<p>This repository contains the ProteinCartography analyses that accompany the pub "<a href="https://doi.org/10.57844/arcadia-cae9-96c4">A strategy to validate protein function predictions&nbsp;<em>in vitro</em></a><a href="https://doi.org/10.57844/arcadia-cae9-96c4">."</a> These proteins were selected from a list of the 200 most studied human proteins in the Protein Data Bank (PDB) which was published in <a href="https://onlinelibrary.wiley.com/doi/10.1002/pro.4038">Liu and Buck, 2021</a>.&nbsp;</p> <p>Each analysis was done using <a href="https://github.com/Arcadia-Science/ProteinCartography/releases/tag/v0.5.0">v0.5.0</a> of ProteinCartography using the standard configuration parameters. Each analysis is in its own zipped file corresponding to the protein name. The zipped files contain inputs, configuration files, all the structures, and all ProteinCartography results. Additionally, for a handful of the proteins, we ran a scaled-up version of ProteinCartography asking for 10,000 hit proteins instead of the standard. These are also included with the file prefix "Scaled-up_".&nbsp;</p> <p>Finally, we've decided to move forward with 2 protein families for validation of our tool, ProteinCartography. These protein families are <a href="https://doi.org/10.57844/arcadia-1e5d-e272">deoxycytidine kinase</a> and <a href="https://doi.org/10.57844/arcadia-74ad-345f">Ras GTPase</a>. For more about these two protein families, visit the mentioned pubs!&nbsp;</p>