SumoPred-PLM: human SUMOylation and SUMO2/3 sites Prediction using Pre-trained Protein Language Model

To guarantee proper use of our tool, please follow all the steps in the presented order.

Note

All the programs in this repository are properly tested and accurate for its functionality!

How it Works

SumoPred-PLM is a multi-layer perceptron-based approach that leverages contextualized embeddings generated from the ProT5-XL-UniRef50 Protein Language Model (Referred to here as ProT5 PLM) to predict Human SUMOylation and SUMO2/3 sites ("K").

Environment Details

SumoPred-PLM was developed in the following environment:

• Python version: 3.8.3.final.0
• Python bits: 64
• Operating System: Linux
  • OS release: 5.8.0-38-generic
• Machine architecture: x86_64
• Processor architecture: x86_64

Python Packages and Dependencies

The model relies on the following Python packages and their specific versions:

• Pandas: 1.0.5
• NumPy: 1.18.5
• Pip: 20.1.1
• SciPy: 1.4.1
• scikit-learn: 0.23.1
• TensorFlow: 2.3.1

Please ensure you have a compatible Python environment with these dependencies to use the model effectively.

Create a Virtual Environment

To install the required dependencies for this project, you can create a virtual environment and use the provided requirements.txt file. Make sure your Python environment matches the specified versions above to ensure compatibility.

python -m venv myenv  # Create a virtual environment
source myenv/bin/activate  # Activate the virtual environment
pip install -r requirements.txt  # Install the project dependencies

Download Protein Sequences From Uniprot.org

You can use our model with all human protein sequences provided by UniPort.

• We have included a file named P10275.fasta as an example in this repository

Once you have downloaded the protein sequence you can send the sequence as input to the ProT5 PLM.

Generate Contextualized Embeddings Using ProT5 PLM

ProT5 PLM Installation

In order to run the ProT5 PLM, you need to install the following modules:

!pip install torch
!pip install transformers
!pip install sentencepiece

For more information, refer to this "ProtTrans" Repository.

Once the model is installed you can write your own code to generate the contextualized embeddings OR to make this process easier for you, we have also provided a Generate_ProtT5_Contextualized_Embeddings_of_P10275_Protein.ipynb file.

Here's how you can use the provided file for a single protein sequence:

You need to make changes to only two lines of the code:

basedir = "/Generate_ProtT5_File/after_cd_hit_files"       # here you will paste the location where your .fasta file is located
name = "P10275.fasta"                                      # here you will add your protein name

The rest of the lines will stay the same.

Once the code is successfully run, It will generate a file named "P10275_Prot_Trans_.csv".

Here's how the output file will look:

Rows = Length of The Protein Sequence
Columns = 1025 (1 column to represent the protein residue + 1024 embeddings)

Note

Make sure to keep all of the files being used in the same directory

Extracting "K" Sites From Contextualized Embeddings

In order to extract only the "K" sites from the contextualized embeddings you can use the following code:

import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')

df = pd.read_csv("P10275_Prot_Trans_.csv", header = None)                     # replace with your ProtT5 embeddings file
Header = ["Residue"]+[int(i) for i in range(1,1025)]
df.columns = Header
df_K_Residue_Only = df[df["Residue"].isin(["K"])]

K_residue_index = []
All_residue_list = list(df["Residue"])
for residue in range(len(All_residue_list)):
    index_no = residue+1
    amino_acid = All_residue_list[residue]
    if amino_acid == "K":
        K_residue_index.append(index_no)

df_K_Residue_Only["K_index_starting_from_one"] = K_residue_index

df_K_Residue_Only.to_csv("P10275_K_Sites.csv")                 # saves the embeddings of only K residues

df_K_Residue_Only = pd.read_csv("P10275_K_Sites.csv")

df_K_Residue_Only

Once the process is complete, you will have a .csv file containing the embeddings of "K" sites.

Here's an example output:

Sending Sites into SumoPred-PLM For Prediction

Now send the P10275_K_Sites.csv from the previous step as an input to the SumoPred-PLM.

We have provided a file named SumoPred-PLM.ipynb and our modelSubash_Chandra_Pakhrin5775457372.h5 which should be downloaded and kept in the same directory to avoid any issues.

Any Questions?

If you need any help don't hesitate to get in touch with Dr. Subash Chandra Pakhrin (pakhrins@uhd.edu)

This is for Peer Review Purposes

Programs were executed using anaconda version: 2020.07, recommended to install the same

The programs were developed in the following environment. python : 3.8.3.final.0, python-bits : 64, OS : Linux, OS-release : 5.8.0-38-generic, machine : x86_64, processor : x86_64, pandas : 1.0.5, numpy : 1.18.5, pip : 20.1.1, scipy : 1.4.1, scikit-learn : 0.23.1., keras : 2.4.3, tensorflow : 2.3.1.

Please place all the following files in the same directory SUMOylation Independent Testing.ipynb, Final_independent_test_dataset_of_SUMOylation_PTM.csv (In the publicly shared google drive), Subash_Chandra_Pakhrin5775457372.h5, and execute the SUMOylation Independent Testing.ipynb program to see the reported result.

Please place all the following files in the same directory SUMO_2_3_Independent_Test_Set_Testing.ipynb, Independent_Test_Set_of_Hendriks_et_al_SUMO_2_3.csv (In the publicly shared google drive), SUMO2_330482327.h5, and execute the SUMO_2_3_Independent_Test_Set_Testing.ipynb program to see the reported result.

Please place all the following files in the same directory GPS SUMO Independent Testing.ipynb, GPS_Testing_Prot_T5_feature.txt (In the publicly shared google drive), GPS_SUMO_Independent_test82423____13.h5, and execute the GPS SUMO Independent Testing.ipynb program to see the reported result.

If you want to test if the sites of a protein is SUMOylated or not: Please place all the following files in the same directory SUMOylation_P10275_Test.ipynb, Subash_Chandra_Pakhrin5775457372.h5, P10275_Prot_Trans_.csv (Change this feature file with your interest, generate it using the analyze_Cell_Mem_ER_Extrac_Protein.py program) and execute the SUMOylation_P10275_Test.ipynb you will see the result which K are SUMOylated.

If you want to test if the sites of a protein is SUMO2/3 SUMOylated or not: Please place all the following files in the same directory SUMO_2_3_P10275_Test.ipynb, SUMO2_330482327.h5, P10275_Prot_Trans_.csv (Change this feature file with your interest, generate it using the analyze_Cell_Mem_ER_Extrac_Protein.py program) and execute the SUMO_2_3_P10275_Test.ipynb you will see the result which K are SUMO2/3 SUMOylated.

*** For your convenience we have uploaded the ProtT5 feature extraction program (analyze_Cell_Mem_ER_Extrac_Protein.py) for the protein sequence from ProtT5 as well as corresponding 1024 feature vector extraction program (Feature Extraction Program from the generated files.ipynb) from the ProtT5 file. ***

All the training and independent test data are uploaded at the following google drive link: https://drive.google.com/drive/folders/1GsTRSQc2vwWH-tzBbkrXwrec6LpWUKhC

If you need any help please contact Dr. Subash Chandra Pakhrin (pakhrins@uhd.edu)