The implementation of the paper Du, Z., Ding, X., Hsu, W., Munir, A., Xu, Y., & Li, Y. (2023). pLM4ACE: A protein language model based predictor for antihypertensive peptide screening. Food Chemistry, 137162.
Web server is available at https://sqzujiduce.us-east-1.awsapprunner.com
The majoy dependencies used in this project are as following:
Python 3.8.16
fair-esm 2.0.0
cleanlab
pandas 1.3.5
numpy 1.21.6
scikit-learn 1.0.2
torch 1.13.0+cu116
More detailed python libraries used in this project are referred to requirements.txt.
All the implementation can be down in Google Colab and all you need is just a browser and a google account.
Install all the above packages by !pip install package_name==2.0.0
Notice: My dataset use 0 and 1 to represent high activity and low/non activity, respectively. Again, 0 is positive and 1 is negative.
Just check the file Pretrained_model_usage_template.ipynb
All you need is to prepare your data for prediction in a xlsx format file and open Pretrained_model_usage_template.ipynb in Google Colab. Then upload your data and train dataset (for the model training). Then you are ready to go.
All you need to do is to prepare your databasets in a xlsx format and two column (first column is sequence and the second column is label). You can just download the xlsx format dataset file from any folder in this repository. Before loading your dataset, please shuffle your datasets and split them as a train dataset and a test datasets as your requirement.
You can also use split dataset in python code with the following codes, and then you can replase the data loading and embeddings section anymore. Just replace that part with the following codes.
UPDATES: I have add a new section in pLM4ACE_template_for_other_bioactivity.ipynb to fit you one xlsx format dataset loading and embeddings (just use it).
import numpy as np
import pandas as pd
# whole dataset loading and dataset splitting
dataset = pd.read_excel('whole_sample_dataset.xlsx',na_filter = False) # take care the NA sequence problem
# generate the peptide embeddings
sequence_list = dataset['sequence']
embeddings_results = pd.DataFrame()
for seq in sequence_list:
format_seq = [seq,seq] # the setting is just following the input format setting in ESM model, [name,sequence]
tuple_sequence = tuple(format_seq)
peptide_sequence_list = []
peptide_sequence_list.append(tuple_sequence) # build a summarize list variable including all the sequence information
# employ ESM model for converting and save the converted data in csv format
one_seq_embeddings = esm_embeddings(peptide_sequence_list)
embeddings_results= pd.concat([embeddings_results,one_seq_embeddings])
embeddings_results.to_csv('whole_sample_dataset_esm2_t6_8M_UR50D_unified_320_dimension.csv')
# loading the y dataset for model development
y = dataset['label']
y = np.array(y) # transformed as np.array for CNN model
# read the peptide embeddings
X_data_name = 'whole_sample_dataset_esm2_t6_8M_UR50D_unified_320_dimension.csv'
X_data = pd.read_csv(X_data_name,header=0, index_col = 0,delimiter=',')
X = np.array(X_data)
# split dataset as training and test dataset as ratio of 8:2
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=123)
After the transoformation, you are all set and good to go. (try all the traditional machine learning methods: SVM, LR, RF, MLP, etc.) Notice: please do check your dataset dimension before running in case of error occring.
# check the dimension of the dataset before model development
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)
Feel free to make your personalized modifications. Just scroll down to the model architecture sections and make revisions to fit your expectation.
We have built a loop for commonly used hyperparameter searching.
Logistic Regression (LR) model performance in test dataset
Sn_collecton 0.9054054054054054
Sp_collecton 0.8769230769230769
MCC_collection 0.7656758452182151
BACC 0.8911642411642411
Multilayer perceptrons (MLP) model performance in test dataset
Sn_collecton 0.8571428571428571
Sp_collecton 0.8818897637795275
MCC_collection 0.7321764677633454
BACC 0.8695163104611923
Support vector machine (SVM) model performance in test dataset
Sn_collecton 0.8461538461538461
Sp_collecton 0.8809523809523809
MCC_collection 0.7221632314801458
BACC 0.8635531135531136