Phosformer-ST
Introduction
This repository contains the code to run Phosformer-ST locally described in the manuscript "Phosformer-ST: explainable machine learning uncovers the kinase-substrate interaction landscape". This readme also provides instructions on all dependencies and packages required to run Phosformer-ST in a local environment.
Quick overview of the dependencies
Included in this repository are the following:
phos-ST_Example_Code.ipynb
: ipynb file with example code to run Phosformer-STmodeling_esm.py
: Python file that has the architecture of Phosformer-STconfiguration_esm.py
: Python file that has configuration/parameters of Phosformer-STtokenization_esm.py
: Python file that contains code for the tokenizer
multitask_MHA_esm2_t30_150M_UR50D_neg_ratio_8+8_shift_30_mask_0.2_2023-03-25_90.txt
: this txt file contains a link to the training weights held on the hugging face or zenodo repository- See section below (Downloading this repository) to be shown how to download this folder and where to put it
phosST.yml
: This file is used to help create an environment for Phosformer-ST to workREADME.md
:LICENSE
: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License
Installing dependencies with version info
From conda:
Python == 3.9.16
From pip:
For torch/PyTorch
Make sure you go to this website https://pytorch.org/get-started/locally/
Follow along with its recommendation
Installing torch can be the most complex part
Downloading this repository
git clone https://huggingface.co./gravelcompbio/Phosformer-ST_with_trainging_weights
cd Phosformer-ST_with_trainging_weights
The Phosformer-ST_with_trainging_weights
folder should have the following files/folder in it
file 1
phos-ST_Example_Code.ipynb
file 2
modeling_esm.py
file 3
configuration_esm.py
file 4
tokenization_esm.py
file 5
multitask_MHA_esm2_t30_150M_UR50D_neg_ratio_8+8_shift_30_mask_0.2_2023-03-25_90.txt
file 6
phosST.yml
file 7
Readme.md
file 8
LICENSE
folder 1
multitask_MHA_esm2_t30_150M_UR50D_neg_ratio_8+8_shift_30_mask_0.2_2023-03-25_90
zipped folder 2
multitask_MHA_esm2_t30_150M_UR50D_neg_ratio_8+8_shift_30_mask_0.2_2023-03-25_90.zip
Once you have a folder with the files/folder above in it you have done all the downloading needed
Installing dependencies with conda
PICK ONE of the options below
Main Option) Utilizing the PhosformerST.yml file
here is a step-by-step guide to set up the environment with the yml file
Just type these lines of code into the terminal after you download this repository (this assumes you have anaconda already installed)
conda env create -f phosST.yml -n PhosST
conda deactivate
conda activate phosST
Alternative option) Creating this environment without yml file
(This is if torch is not working with your version of cuda or any other problem)
Just type these lines of code into the terminal after you download this repository (this assumes you have anaconda already installed)
conda create -n phosST python=3.9
conda deactivate
conda activate phosST
conda install -c conda-forge jupyterlab
pip3 install numpy==1.24.3
pip3 install pandas==2.0.2
pip3 install matplotlib==3.7.1
pip3 install scikit-learn==1.2.2
pip3 install tqdm==4.65.0
pip3 install fair-esm==2.0.0
pip3 install transformers==4.31.0
For torch you will have to download to the torch's specification if you want gpu acceleration from this website https://pytorch.org/get-started/locally/
pip3 install torch torchvision torchaudio
the terminal line above might look different for you
We provided code to test Phosformer-ST (see section below)
Utilizing the Model with our example code
All the following code examples is done inside of the phos-ST_Example_Code.ipynb
file using jupyter lab
Once you have your environment resolved just use jupyter lab to access the example code by typing the command below in your terminal (when you're in the Phosformer-ST
folder)
jupyter lab
Once you open the notebook on your browser, run each cell in the notebook
Testing Phosformer-ST with the example code
There should be a positive control and a negative control example code at the bottom of the phos-ST_Example_Code.ipynb
file which can be used to test the model.
Positive Example
# P17612 KAPCA_HUMAN
kinDomain="FERIKTLGTGSFGRVMLVKHKETGNHYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVKLEFSFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVNDIKNHKWF"
# P53602_S96_LARKRRNSRDGDPLP
substrate="LARKRRNSRDGDPLP"
phosST(kinDomain,substrate).to_csv('PostiveExample.csv')
Negative Example
# P17612 KAPCA_HUMAN
kinDomain="FERIKTLGTGSFGRVMLVKHKETGNHYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVKLEFSFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVNDIKNHKWF"
# Q01831_T169_PVEIEIETPEQAKTR
substrate="PVEIEIETPEQAKTR"
phosST(kinDomain,substrate).to_csv('NegitiveExample.csv')
Both scores should show up in a csv file in the current directory
Inputting your own data for novel predictions
One can simply take the code from above and modify the string variables kinDomain
and substrate
to make predictions on any given kinase substrate pairs
Formatting of the kinDomain
and substrate
for input for Phosformer-ST are as follows:
kinDomain
should be a human Serine/Threonine kinase domain (not the full sequence).substrate
should be a 15mer with the center residue/char being the target Serine or Threonine being phosphorylated
Not following these rules may result in dubious predictions
How to interpret Phosformer-ST's output
This model outputs a prediction score between 1 and 0.
We trained the model to uses a cutoff of 0.5 to distinguish positive and negative predictions
A score of 0.5 or above indicates a positive prediction for peptide substrate phosphorylation by the given kinase
Troubleshooting
If torch is not installing correctly or you do not have a GPU to run Phosformer-ST on, the CPU version of torch is perfectly fine to use
Using the CPU version of torch might increase your run time so for large prediction datasets GPU acceleration is suggested
If you just are here to test if it Phosformer-ST works, the example code should not take too much time to run on the CPU version of torch
Also depending on your GPU the batch_size
argument might need to be adjusted
2024-05-17
- if you get an 'EsmTokenizer' object has no attribute 'all_tokens' error when loading the tokenizer
- Make sure you have version of transformers==4.31.0 installed
The model has been tested on the following computers with the following specifications for trouble shooting proposes
Computer 1
NVIDIA Quadro RTX 5000 (16 GB vRAM)(CUDA Version: 12.1)
Ubuntu 22.04.2 LTS
Intel(R) Xeon(R) Bronze 3204 CPU @ 1.90GHz (6 cores) x (1 thread per core)
64 GB ram
Computer 2
NVIDIA RTX A4000 (16 GB vRAM)(CUDA Version: 12.2)
Ubuntu 20.04.6 LTS
Intel(R) Xeon(R) Bronze 3204 CPU @ 1.90GHz (6 cores) x (1 thread per core)
64 GB ram
Other accessory tools and resources
A webtool for Phosformer-ST can be accessed from: https://phosformer.netlify.app/. A huggingface repository can be downloaded from: https://huggingface.co./gravelcompbio/Phosformer-ST_with_trainging_weights. A huggingface spaces app is available at: https://huggingface.co./spaces/gravelcompbio/Phosformer-ST
The github can be found here https://github.com/gravelCompBio/Phosformer-ST/tree/main