AMP-SEMiner

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Antimicrobial Peptide Structural Evolution Miner (AMP-SEMiner), an integrated AI framework designed for the simultaneous identification of antimicrobial peptides (AMPs) as small open reading frames (smORFs) and protein fragments.

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# AMP-SEMiner: AntiMicrobial Peptide Structural Evolution Miner

## Introduction

This repository contains the custom code for **AMP-SEMiner**, a robust and comprehensive AI framework designed for residue-level AMP discovery. The framework accurately identifies specific residues within a protein as either AMP or non-AMP.

![Schematic diagram of the AMP-SEMiner framework](https://raw.githubusercontent.com/zjlab-BioGene/AMP-SEMiner/main/Figure_1.png)

## Data Portal

The AMPs identified using AMP-SEMiner are available through our dedicated data portal, [MAG-AMPome](http://mag-ampome.aigene.org.cn) (http://mag-ampome.aigene.org.cn). Visit the portal to access this comprehensive resource of antimicrobial peptides.

## Environment
AMP-SEMiner is run on Python 3.10 and PyTorch 2.1.2. You can build a conda environment for AMP-SEMiner using this [script](https://github.com/zjlab-BioGene/AMP-SEMiner/blob/main/scripts/env_install.sh).

## Run AMP prediction

The model weights of AMP-SEMiner and minimum datasets to run AMP-SEMiner are available in [Zenodo](https://zenodo.org/records/14348290) (DOI: 10.5281/zenodo.14348290). A small example dataset are also provided:

[Tok_CLS.tar.gz](https://zenodo.org/records/14348290/files/Tok_CLS.tar.gz)

[Tok_CLS_LoRA.tar.gz](https://zenodo.org/records/14348290/files/Tok_CLS_LoRA.tar.gz)

[2_steps.tar.gz](https://zenodo.org/records/14348290/files/2_steps.tar.gz)

[example_dataset.tar.gz](https://zenodo.org/records/14348290/files/example_dataset.tar.gz)

<pre>
mkdir model_weights
cd model_weights

## download model weights
wget https://zenodo.org/records/14348290/files/Tok_CLS.tar.gz
tar -zxvf Tok_CLS.tar.gz

wget https://zenodo.org/records/14348290/files/Tok_CLS_LoRA.tar.gz
tar -zxvf Tok_CLS_LoRA.tar.gz

cd -

## download example datasets
wget https://zenodo.org/records/14348290/files/example_dataset.tar.gz
tar -zxvf example_dataset.tar.gz
</pre>

<pre>
mkdir example_out

#conda activate ampseminer
python End_to_end_Tok_CLS/pred_Token.py \
 --input example_dataset/test_dataset.csv \
 --output example_out/test.out_pred.tsv \
 --model_name model_weights/Tok_CLS/epoch15 \
 --batch_size 4 \
 --max_len 300
</pre>

## Run training

Example datasets for AMP model trainining:

[example_data.tar.gz](https://zenodo.org/records/14348290/files/example_data.tar.gz)

[example_labels.tar.gz](https://zenodo.org/records/14348290/files/example_labels.tar.gz)

<pre>
## download example datasets for AMP model training

wget https://zenodo.org/records/14348290/files/example_data.tar.gz
tar -zxvf example_data.tar.gz

wget https://zenodo.org/records/14348290/files/example_labels.tar.gz
tar -zxvf example_labels.tar.gz

#conda activate ampseminer
python End_to_end_Tok_CLS/train_Tokens.py \
 --data_path ./example_data \
 --label_path ./example_labels \
 --model_name facebook/esm2_t30_150M_UR50D \
 --num_classes 2 \
 --ft_mode full \
 --epochs 10 \
 --batch_size 16 \
</pre>

## Custom code for Colab Notebook

Install and run our AMP discovery model [run_pred_AMP_SEMiner.ipynb](https://colab.research.google.com/drive/1-O8U7M6UTtSaMQqm3sX7ZOtkUCVEmmLt?usp=sharing)

## Datasource

## Citation

## Contacts

zhengjinfang1220@gmail.com, liwh@zhejianglab.org, liwh@tongji.edu.cn

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