Source:

Creator: Christian B. Thygesen, University of Copenhagen. Email: christiank.thygesen '@' di dot ku . dk
Donator: Ola Rønning, University of Copenhagen. Email: ola '@' di . ku dot dk

Data Set Information:

The dataset consists of protein fragments of length nine, called 9mers, derived from 3,733 proteins selected by cullpdb [1]. All proteins have 1) resolution less than 1.6 Ångström, 2) R-factor less than 0.25, 3) sequence identity below 20%. In addition, all proteins with identity above 20% to CASP13 targets are removed. All torsion angle-pairs are in the allowed region of the Ramachandran plot (fragments containing outliers were detected by the Ramalyze function of the crystallography software PHENIX [1] and removed). The dataset has ∼158,000 entries randomly split into train, test, and validation sets with a 60/20/20 split.

References:
[1] Wang, G., & Dunbrack, R. L. (2005). PISCES: recent improvements to a PDB sequence culling server. Nucleic acids research, 33(suppl_2), W94-W98.
[2] Liebschner, D., Afonine, P. V., Baker, M. L., Bunkóczi, G., Chen, V. B., Croll, T. I., ... & Adams, P. D. (2019). Macromolecular structure determination using X-rays, neutrons, and electrons: recent developments in Phenix. Acta Crystallographica Section D: Structural Biology, 75(10), 861-877.

Attribute Information:

secondary_structure: 9 secondary structure labels
angle1: 9 phi torsion angles ([-pi,pi))
angle2: 9 psi torsion angles ([-pi,pi))
amino_acids: 9 amino acid labels

Map from aa to index in aa_to_index.csv.
Map from secondary structure label to index in secondary_structure_to_index.csv.

Relevant Papers:

Christian B. Thygesen, Ahmad Salim Al-Sibahi, Christian S. Steenmanns, Lys S. Moreta, Anders B. Sørensen, Thomas Hamelryck. 'Efficient Generative Modelling of Protein StructureFragments using a Deep Markov Model'. International Conference on Machine Learning 2021 (to appear)

Citation Request:

Christian B. Thygesen, Ahmad Salim Al-Sibahi, Christian S. Steenmanns, Lys S. Moreta, Anders B. Sørensen, Thomas Hamelryck. 'Efficient Generative Modelling of Protein StructureFragments using a Deep Markov Model'. International Conference on Machine Learning 2021 (to appear)

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