Experiment data used in DeepHelicon

Published: 13 April 2020| Version 2 | DOI: 10.17632/k8tfvgftv3.2


The data is for the paper titled "DeepHelicon: accurate prediction of inter-helical residue contacts in transmembrane protein by residual neural networks". It contains four sub-folders as follows: 1. Fasta: the protein sequences in the TRAIN, PREVIOUS, and TEST datasets, respectively. 2. PDB: the protein native structures in the TRAIN, PREVIOUS, and TEST datasets, respectively. 3. Predictions: the contact predictions on the PREVIOUS and TEST datasets, which are predicted by the contact prediction methods mentioned in the DeepHelicon paper. 4. 3D modelling: the 3D models, which are guided by the secondary structures predicted by SCRATCH1D and guided by the residue contacts predicted by DeepHelicon and DeepMetaPSICOV, respectively, are finally generated by CONFOLD2.



Technische Universitat Munchen


Computational Modeling of Protein, Computational Bioinformatics