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The best way to run the Glycosylation prediction tool is via the Cyrus Engine API. Given a protein sequence in a fasta file, the API is run like this:
| Code Block |
|---|
cyrus engine submit glyco-predictor input.fasta |
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DNGP architecture parameters were optimized via grid search with three-fold cross-validation on an independent training set. Final DNN parameters selected by Pakhrin et al. are summarized in Table 1. Optimal features for classification were a combination of structural predictions (ASA, RSA, SS, disorder, torsion) via NetSurfP, gapped dipeptide rates, and PSSM data from PSI-BLAST.
Parameter Name | Parameter Used |
Number of layers | 4 |
Number of neuron in three layers | 150 |
Number of neuro in output layer | 2 |
Activation Function | sigmoid |
Activation Function at output layer | softmax |
Optimizer | Adam |
Learning rate | 0.001 |
Objective / loss function | binary_crossentropy |
Model checkpoint | val_accuracy |
Reduce learning rate on plateau | Factor = 0.001 |
Early stopping | patience = 5 |
Dropout | 0.3 |
Batch_size | 256 |
Epochs | 400 |
Table 1. Selected DNN architectural parameters from Pakhrin et al.
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On average, glyco-predictor achieved 72.9% accuracy, 81.1% precision, 71.9% recall, and 83.2% specificity. While there may be potential in enhancing this model, these benchmarks reflect acceptable performance for predicting human N-linked glycosylation.
Benchmark | Accuracy (%) | Precision (%) | Recall (%) | Specificity (%) |
N-GlyDE dataset | 79.4 | 67.0 | 88.6 | 73.9 |
ACE2 (S19) | 70 | 60 | 75 | 66 |
ACE2 (v2.4) | 75 | 100 | 60 | 100 |
GALA | 67.5 | 97.9 | 64 | 93 |
Average | 72.9 | 81.1 | 71.9 | 83.2 |
Table 2. Performance of Cyrus glyco-predictor tool on different benchmarks.
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Output = report of N-linked glycosylation predictions
Links
Pakhrin SC, Aoki-Kinoshita KF, Caragea D, KC DB. DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction. Molecules. 2021; 26(23):7314. https://doi.org/10.3390/molecules26237314
DeepNGlyPred Github Repository