DeepTAP is a novel recurrent neural network (RNN)-based, using bidirectional gated recurrent unit (BiGRU), developed for the accurate prediction of TAP-binding peptides.The DeepTAP web server is freely accessible to the public at https://pgx.zju.edu.cn/deeptap/.
Contact: zhanzhou@zju.edu.cn
Windows/Linux
Create a virtual environment using conda or miniconda. This is the version of the package that must be installed. see the env.yaml file for detailed installation packages and versions.
| Packages |
|---|
| python==3.10.0 |
| numpy==1.24.2 |
| pandas==1.5.3 |
| pytorch==1.1.3 |
| torchmetrics=0.11.1 |
| pytorch-lightning==1.9.2 |
Download the latest version of DeepTAP from https://github.com/zjupgx/DeepTAP or https://github.com/xuezhang335/DeepTAP
git clone https://github.com/zjupgx/DeepTAP.git
(or git clone https://github.com/xuezhang355/DeepTAP.git)
Go into the directory by using the following command:
cd DeepTAP
DeepTAP takes csv or xlsx files as input with head of "peptide" (requisite). See demo/demo1.csv for an example.
| peptide |
|---|
| KADDDKPGA |
| PTAWRSEMN |
| AEASAAAAY |
| KKTSLEKRM |
| AAASAAYAY |
| RRFGDKLNF |
| ALAKAGAAV |
| AAASAAAAK |
| ALAAAAAAQ |
-t, --taskType, choices=['cla', 'reg'], Select task type: classification, regression
-p, --peptide, Single peptide for prediction
-f, --file, Input file with peptides for prediction: if given, overwrite -p option
-o, --outputDir, Directory to store file with prediction result: if not given, the current directory will be applied
Single peptide:
classification model prediction:
python deeptap.py -t cla -p LNIMNKLNI -o <output directory>
regression model prediction:
python deeptap.py -t reg -p LNIMNKLNI -o <output directory>
List of peptides in a file:
classification model prediction:
python deeptap.py -t cla -f <input file> -o <output directory>
regression model prediction:
python deeptap.py -t reg -f <input file> -o <output directory>
The model prediction results output two files: the original ranking file and the ranking file according to the prediction score from high to low. See demo/demo1_DeepTAP_cla_predresult.csv for an example.
| peptide | pred_score | pred_label |
|---|---|---|
| KADDDKPGA | 0.2964 | 0 |
| PTAWRSEMN | 0.1114 | 0 |
| AEASAAAAY | 0.9795 | 1 |
| KKTSLEKRM | 0.5658 | 1 |
| AAASAAYAY | 0.9955 | 1 |
| RRFGDKLNF | 0.9884 | 1 |
| ALAKAGAAV | 0.3405 | 0 |
| AAASAAAAK | 0.6483 | 1 |
| ALAAAAAAQ | 0.2531 | 0 |
The following are the field descriptions for the result file.
For classification tasks:
pred_score: Combined prediction score, between 0-1, the threshold is 0.5
pred_label: Whether it is a binding peptide, 0 means no binding, 1 means binding
For regression tasks:
pred_affinity: Binding prediction affinity, unit nM, threshold is 10000nM
pred_label: Whether it is a binding peptide, 0 means no binding, 1 means binding
V1.0
Test the suitabilty of different RNN variants (GRU,LSTM,BGRU,BLSTM,att-BGRU and att-BLSTM) and CNN on the binding prediction and select the best one (BGRU) for model construction.