DeepNGlyPred

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DeepNGlyPred: a Deep Neural Network-based approach for human N-linked Glycosylation site prediction

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# DeepNGlyPred

DeepNGlyPred: a Deep Neural Network-based approach for human N-linked Glycosylation site prediction 
Please cite

Pakhrin, S.C.; Aoki-Kinoshita, K.F.; Caragea, D.; KC, D.B. DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction. Molecules 2021, 26, 7314. https://doi.org/10.3390/molecules26237314

Programs were executed using anaconda version: 2020.07, recommended to install the same 
      
      
  The programs were developed in the following environment. 
      python              : 3.8.3.final.0,
      python-bits         : 64,
      OS                  : Linux,
      OS-release          : 5.8.0-38-generic,
      machine             : x86_64,
      processor           : x86_64,
      pandas              : 1.0.5,
      numpy               : 1.18.5,
      pip                 : 20.1.1,
      scipy               : 1.4.1,
      scikit-learn        : 0.23.1.,
      keras               : 2.4.3,
      tensorflow          : 2.3.1.
      
      
      
 1. Please place the DNN_trained_with_nGlyde_test_with_nglyde.ipynb, 0.605 ANN molecules.ipynb, 447-feature_test.xlsx, All_feature_Independent_dataframe.csv, trained models like 3080_NetSurfP2.0_PSSM_GD8.h5 and
   Glycosylation_model__26__.h5 in the same directory where you will execute the python program.
   
   
2. Run DNN_trained_with_nGlyde_test_with_nglyde.ipynb,  0.605 ANN molecules.ipyn to obtain reported results.

*** For your convenience a sample PSSM file (P48066.PSSM) for protein P48066 generated using PSI-BLAST,  a sample NetsurfP-2.0 file for protein B7Z4J0, B7Z4J0.fasta.csv generated by NetSurfP-2.0, and Ratio_Normalized_NGlyDE.txt file which is generated by Gapped Dipeptide feature extraction program has been uploaded   ***

If you need any futher help please contact Dr. Dukka B. KC at dbkc@mtu.edu.

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