pLM4ACE: A protein language model-based deep learning predictor for screening peptide with high antihypertensive activity

The webserver is 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."

Quick output version: 1. Choose a model  → 2. Input a peptide sequence

Choose a model: Logistic_Regression SVM Multilayer_Perceptron Run

Large-scale output version: 1. Prepare your files (xls, xlsx, fasta, or txt) and click “Choose File” for uploading → 2. Choose a model for classification  → 3. Download the results.

Choose a model: Logistic_Regression SVM Multilayer_Perceptron
Choose a file to upload Run

A recommended server integrates 22 prediction models for different biactivities and properties (e.g., cell penetrating activity, toxicity, allergens, etc.); give a try at https://nepc2pvmzy.us-east-1.awsapprunner.com/

Usage of the webserver:

Example for “Quick output version” :

1. Select Logistic_Regression model for antihypertensive activity prediction.  →   →  →  2. Insert a peptide or protein sequence, “VPP” →   →  →  3. Click “Run”→   →  → 4. The result will be returned in seconds below the “Run” button

Notice: it also support multiple sequence at the same time. Just input as “VPP,IPP,CCL,AGR” (sequences are separated by comma, no space)

Example for “Large-scale output version:” :

1. Prepare your xls, xlsx, txt or fasta files → → → 2. Upload the file through “Choose File” botton → → → 3. Click “Run” → → → 4. It will automatically download your results.

Notice: File preparation should follow the examples under this repository https://github.com/dzjxzyd/pLM4ACE/tree/main/Example%20uploading%20files

Model performance in test dataset

Model	Balanced ACC	Sensitivity	Specificity	Matthews correlation coefficient
Logistic regression	0.883 ± 0.017	0.845 ± 0.041	0.92 ± 0.025	0.77 ± 0.032
SVM	0.867 ± 0.02	0.825 ± 0.041	0.91 ± 0.027	0.74 ± 0.038
Multilayer perceptron	0.855 ± 0.024	0.815 ± 0.036	0.895 ± 0.037	0.711 ± 0.054