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| title: Phosformer ST | |
| emoji: 🐢 | |
| colorFrom: gray | |
| colorTo: pink | |
| sdk: gradio | |
| sdk_version: 3.38.0 | |
| app_file: app.py | |
| pinned: false | |
| license: cc-by-nc-nd-4.0 | |
| <!-- This github was Made by Nathan Gravel --> | |
| # Phosformer-ST <img src="https://github.com/gravelCompBio/Phosformer-ST/assets/75225868/f375e377-b639-4b8c-9792-6d8e5e9e6c39" width="60"> | |
| ## 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. | |
| </br> | |
| ## Quick overview of the dependencies | |
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| ## 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 | |
| </br> | |
| </br> | |
| ## Installing dependencies with version info | |
| ### From conda: | |
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| Python == 3.9.16 | |
| ### From pip: | |
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| ### 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 | |
| </br> | |
| </br> | |
| ## 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 | |
| </br> | |
| </br> | |
| ##  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) | |
| </br> | |
| </br> | |
| ## 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 | |
| </br> | |
| ### 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** | |
| ```Python | |
| # P17612 KAPCA_HUMAN | |
| kinDomain="FERIKTLGTGSFGRVMLVKHKETGNHYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVKLEFSFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVNDIKNHKWF" | |
| # P53602_S96_LARKRRNSRDGDPLP | |
| substrate="LARKRRNSRDGDPLP" | |
| phosST(kinDomain,substrate).to_csv('PostiveExample.csv') | |
| ``` | |
| **Negative Example** | |
| ```Python | |
| # 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 | |
| </br> | |
| ### 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 | |
| </br> | |
| ### 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 | |
| </br> | |
| ## 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 | |
| </br> | |
| **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 | |
| </br> | |
| **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 | |
| </br> | |
| ## 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 | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |