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Sensitivity Datasets - Leveraging Implicit Knowledge in Neural Networks for Functional Dissection and Engineering of Proteins

<p><strong>Leveraging Implicit Knowledge in Neural Networks for Functional Dissection and Engineering of Proteins</strong></p> <p>The Sensitivity datasets cover more than 800 proteins and are structured as follows. The sensitivity values are the mean of four DeeProtein replicates.</p> <p>It is uploaded as tar.gz. and contains one directory.</p> <p>File names contain the PDB<sup>1</sup> identifier and the respective chain identifier.&nbsp;</p> <p>The sequences and secondary structure information were downloaded from the RCSB Protein Databank and are available here: <a href="https://cdn.rcsb.org/etl/kabschSander/ss_dis.txt.gz">https://cdn.rcsb.org/etl/kabschSander/ss_dis.txt.gz</a> This URL can be found with some explanation at <a href="http://www.rcsb.org/pdb/static.do?p=download/http/index.html">http://www.rcsb.org/pdb/static.do?p=download/http/index.html</a></p> <p>The secondary structure annotation relies on the DSSP Algorithm by Kabsch and Sander<sup>2</sup>.</p> <p>&nbsp;</p> <p><strong>The files are tab-separated and contain the following columns:</strong></p> <ul> <li><strong>Pos</strong>&nbsp;Position in the sequence, starting from zero</li> <li><strong>AA</strong>&nbsp;Amino acid in that position</li> <li><strong>sec</strong> Secondary structure as annotated in the RCSB Protein Databank</li> <li><strong>dis</strong>&nbsp;if a region has not been experimentally observed (sometimes explains mismatches with crystal structures)</li> <li><strong>GO:_______</strong>&nbsp;Sensitivity for the GO term</li> </ul> <p><strong>References</strong></p> <ol> <li>The Protein Data Bank H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, P.E. Bourne (2000) Nucleic Acids Research, 28: 235-242. doi:10.1093/nar/28.1.235</li> <li>Kabsch, W. &amp; Sander, C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22, 2577-2637, doi:10.1002/bip.360221211 (1983).</li> </ol>

ShareScore

44/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
8
Access
16
Reuse readiness
8
Engagement
4

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