lkytal / PepNet

The state of the art Deep CNN neural network for de novo sequencing of tandem mass spectra

PepNet

Code for "Accurate De Novo Peptide Sequencing Using Fully Convolutional Neural Networks"

Link to Accurate De Novo Peptide Sequencing Using Fully Convolutional Neural Networks

The state of the art Deep CNN neural network for de novo sequencing of tandem mass spectra, currently works on unmodified HCD spectra of charges 1+ to 4+.

Free for academic uses. Licensed under LGPL.

Visit https://denovo.predfull.com/ to try online prediction

Update History

• 2023.04.27: 2nd Revised version.
• 2022.11.28: Revised version.
• 2021.12.28: First version.

Method

Based on the structure of the residual convolutional networks. Current precision (bin size): 0.1 Th.

How to use

After clone this project, you should download the pre-trained model (model.h5) from zenodo.org and place it into PepNet's folder.

Important Notes

• Will only output unmodification sequences.
• This model assumes a FIXED carbamidomethyl on C
• The length of output peptides are limited to =< 30

Required Packages

Recommend to install dependency via Anaconda

• Python >= 3.7
• Tensorflow >= 2.5.0
• Pandas >= 0.20
• pyteomics
• numba

Packages Required for traning:

• Tensorflow-addons

Output format

Sample output looks like:

TITLE	DENOVO	Score	PPM Difference	Positional Score
spectra 1	LALYCHQLNLCSK	1.0000	-3.8919184	[1.0, 0.9999956, 1.0, 1.0, 1.0, 1.0, 0.99999976, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
spectra 2	HEELMLGDPCLK	1.0000	4.207922	[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.99999976, 1.0]
spectra 3	AGLVGPEFHEK	1.0000	0.54602236	[1.0, 1.0, 1.0, 1.0, 1.0, 0.99999917, 1.0, 1.0, 1.0, 1.0, 1.0]

Usage

Simply run:

python denovo.py --input example.mgf --model model.h5 --output example_prediction.tsv

The output file is in MGF format

• --input: the input mgf file
• --output: the output file path
• --model: the pretrained model

Typical running speed: sequencing 10,000 spectra in ~59 seconds on a NVIDIA A6000 GPU.

Prediction Examples

We provide sample data on for you to evaluate the sequencing performance. The example.mgf file contains ground truth spectra (randomly sampled from NIST Human Synthetic Peptide Spectral Library), while the example.tsv file contains pre-run predictions.

Also, you can run python evaluation.py --novorst example_prediction.tsv to generate figures presenting the de novo performance.

Train this model

See train.py for sample training codes

Related works

Also, Visit https://www.predfull.com/ to check our previous project on full spectrum prediction