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-ST
- modeling_esm.py: Python file that has the architecture of Phosformer-ST
- configuration_esm.py: Python file that has configuration/parameters of Phosformer-ST
- tokenization_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 work
README.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

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