NovoB: Bidirectional De Novo Peptide Sequencing Using a Transformer Model

We recommand conda environment for runing NovoB.

• NovoB requires :

  • python
  • tensorflow (tensorflow-gpu for using GPUs) & keras
  • pyteomics

• Our environment (on Linux CentOS 7.9) :

  • python = 3.10
  • tensorflow-gpu = 2.10 (for using GPUs)
  • keras = 2.10
  • pyteomics

• For setting our environment (on Linux CentOS 7.9) :

  • conda env create -f environment.yaml
  • conda activate NovoB

• If you use tensorflow < 2.10, we recommand to use --save_weights (Learning.py) and --load_weights (Prediction.py).

• Ref: https://doi.org/10.1371/journal.pcbi.1011892

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Data Download

• https://doi.org/10.5281/zenodo.10258874

Initail Model

• NovoBInit
  • The model of NovoB which does not learn weights.

Trained Model

• TrainedModel
  • The model of NovoB which learns weights.

leaned Weights

• TrainedModel/<Dataset>/<ModelName>/variable
  • ex) TrainedModel/usingCasanovoDataSet/yeast/variables

Sample Spectra

• MGF/yeast.10k.mgf
• MGF/ricebean.10k.mgf
• Training and validation data need to be provided as annotated MGF files, where the peptide sequence is denoted in the SEQ field.
• In the SEQ field, PTMs("C(+57.02)", "M(+15.99)", "N(+.98)", and "Q(+.98)") are replaced by "C", "m", "n", and "q", respectively.

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Train Model

For learning model, use Learning.py

python Learning.py -h

usage: Learning.py [-h] -m MODEL_PATH [--save_model SAVE_MODEL] [--save_weights SAVE_WEIGHTS] -l LEARNING_FILE.mgf -v VALIDATION_FILE.mgf [-b BATCH_SIZE] [-e EPOCHS] [-g]
                   [-n]
options:
  -h, --help            show this help message and exit
  -m MODEL_PATH, --model_path MODEL_PATH
                        Saved Model Path (folder)
  --save_model SAVE_MODEL
                        Path to save Model (folder), default = model
  --save_weights SAVE_WEIGHTS
                        Path/file_name to save weights (path/file_name)
  -l LEARNING_FILE.mgf, --learning_file LEARNING_FILE.mgf
                        Learning File (.mgf)
  -v VALIDATION_FILE.mgf, --validation_file VALIDATION_FILE.mgf
                        Validation File (.mgf)
  -b BATCH_SIZE, --batch_size BATCH_SIZE
                        Batch size for learning model (on single GPU), default=128
  -e EPOCHS, --epochs EPOCHS
                        the number of epochs, default=30
  -g, --no_multigpu     Do not use multigpu
  -n, --no_nccl         Do not use nccl (when using multigpu)

• Example)
  • Need to download
  • NovoB Model: NovoBInit (do not learn weights)
  • Learning Spectra : MGF/yeast.10k.mgf
  • Validation Specra : MGF/yeast.10k.mgf
  • Output: Model ("model" folder)

python Learning.py -m NovoBInit -l MGF/yeast.10k.mgf -v MGF/yeast.10k.mgf

• If you want to save only weights, use --save_weigths option as follows.
  • Output: Weights (weights file in "model" folder)

python Learning.py -m NovoBInit --save_weights model/weights -l MGF/yeast.10k.mgf -v MGF/yeast.10k.mgf

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Load Model and Weights to predict peptides

For predict peptide, use Prediction.py

• This will write peptide predictions for the given spectra to the specified output file (separator: tab)
• index/charge/pepmass/ForwardSeq/deltamass(F)/probability(F)/ReverseSeq/deltamass(R)/probability(R)

python Prediction.py -h

usage: Prediction.py [-h] -m MODEL_PATH [--load_weights LOAD_WEIGHTS] -i SPECTRUM_FILE [-o OUTPUT_FILE] [-b BATCH_SIZE] [-g] [-n]

options:
  -h, --help            show this help message and exit
  -m MODEL_PATH, --model_path MODEL_PATH
                        Saved Model Path (folder)
  --load_weights LOAD_WEIGHTS
                        Load weights after loading model
  -i SPECTRUM_FILE, --spectrum_file SPECTRUM_FILE
                        spectrum file (.mgf)
  -o OUTPUT_FILE, --output_file OUTPUT_FILE
                        output file (text file), default=result.txt
  -b BATCH_SIZE, --batch_size BATCH_SIZE
                        Batch size to predict peptides (on single GPU), default=256
  -g, --no_multigpu     Do not use multigpu
  -n, --no_nccl         Do not use nccl (when using multigpu)

• Example)
  • Need to download
  • NovoB Model: NovoBInit (do not learn weights)
  • Learned weights : variables in "TrainedModel/usingCasanovoDataSet/yeast/variables/" folder
  • Sample specra : MGF/yeast.10k.mgf

python Prediction.py -m NovoBInit/ --load_weights TrainedModel/usingCasanovoDataSet/yeast/variables/variables -i MGF/yeast.10k.mgf

• If you want to use learned model, don't use --load_weights option as follows.
  • Need to download
  • NovoB Model : TrainedModel/ricebean
  • Recommand to use tensorflow >= 2.10

python Prediction.py -m TrainedModel/usingCasanovoDataSet/ricebean/ -i MGF/ricebean.10k.mgf