emb2dis: protein disorder prediction tool

This repository contains a deep learning tool for predicting intrinsically disordered regions (IDRs) in protein sequences.

This tool generates embeddings from raw protein sequences using a pre-trained protein language model (pLM) and predicts disorder probabilities using a deep learning model that was trained with the DisProt dataset (2023_12) and tested on the CAID3v3 benchmarks. The output of the tool includes per-residue disorder scores, plots of disorder along the sequence and summary statistics.

Environment setup

1. Clone the repository:

git clone https://github.com/sofiaaduarte/emb2dis.git
cd emb2dis

2. Create a virtual environment:

conda create -n emb2dis python=3.11
conda activate emb2dis

3. Install required packages:

pip install -r requirements.txt

Usage

The main script is predict_disorder.py. You can provide a FASTA file containing one or more protein sequences:

python predict_disorder.py --fasta data/samples.fasta

This script will:

• Read all sequences from the FASTA file.
• Generate embeddings using the specified pLM (ProtT5 by default).
• Predict disorder scores for each residue using a sliding window approach.
• Save results (CSV and plots) to the output directory (./results/ by default).
• Print disorder statistics to the console.

Command-line Arguments

Argument	Short	Description
--fasta	-f	Path to input FASTA file (required).
--model	-m	Protein language model: ProtT5 (by default) or ESM2
--output-dir	-o	Directory to save predictions (.csv) and plots (.png) (./results/ by default).
--device	-d	Device: cpu, cuda (by default), cuda:0, etc.
--verbose	-v	Enable verbose output for detailed progress (False by default).

Examples

1. Specify output directory and verbose mode:

python predict_disorder.py --fasta data/samples.fasta --output-dir my_results/ --verbose

2. Use ESM2 model on CPU:

python predict_disorder.py --fasta data/samples.fasta --model ESM2 --device cpu

3. Use a specific GPU:

python predict_disorder.py --fasta data/samples.fasta --device cuda:1

Models

Supported Protein Language Models

Model	Description	Embedding Size	Reference	Repository
ESM2	ESM-2 (650M parameters)	1280	Lin et al., 2023	facebookresearch/esm
ProtT5	ProtT5-XL (half precision)	1024	Elnaggar et al., 2021	rostlab/ProtTrans

The disorder prediction models are trained specifically for each pLM.

Additional models will be added in future releases.

Additional notes

• Sequence preprocessing: Non-canonical amino acids (U, Z, O, B) are automatically converted to 'X' before generating embeddings.