getwilds/starling repository overview

⁠STARLING

This directory contains Docker images for STARLING⁠, a latent-space probabilistic denoising diffusion model for predicting coarse-grained ensembles of intrinsically disordered protein regions from amino acid sequences.

⁠Available Versions

• latest ( Dockerfile⁠ | Vulnerability Report⁠ )
• 2.0.0a3 ( Dockerfile⁠ | Vulnerability Report⁠ )

⁠Image Details

These Docker images are built from the ubuntu:24.04 base image with GPU support and include:

• STARLING v2.0.0a3: Coarse-grained ensemble prediction for intrinsically disordered proteins
• PyTorch 2.6.0 (CUDA 12.6): Deep learning framework used by STARLING's diffusion model
• MDTraj: Molecular dynamics trajectory analysis
• metapredict: Disorder prediction used internally by STARLING

CUDA 12.6 libraries are bundled with the PyTorch cu126 wheels, so GPU-accelerated inference works without an NVIDIA CUDA base image. To use GPU acceleration, run with --gpus all (Docker) or --nv (Apptainer).

⁠Citation

If you use STARLING in your research, please cite the original authors:

Novak B, Lotthammer JM, Emenecker RJ, Holehouse AS.
Accurate predictions of conformational ensembles of disordered proteins with STARLING.
bioRxiv 2025.02.14.638373 (2025).
DOI: 10.1101/2025.02.14.638373

Tool homepage: https://github.com/idptools/starling⁠

Publication: https://doi.org/10.1101/2025.02.14.638373⁠

⁠Usage

⁠Docker

# Pull the latest version
docker pull getwilds/starling:latest

# Or pull a specific version
docker pull getwilds/starling:2.0.0a3

# Alternatively, pull from GitHub Container Registry
docker pull ghcr.io/getwilds/starling:latest

⁠Singularity/Apptainer

# Pull the latest version
apptainer pull docker://getwilds/starling:latest

# Or pull a specific version
apptainer pull docker://getwilds/starling:2.0.0a3

# Alternatively, pull from GitHub Container Registry
apptainer pull docker://ghcr.io/getwilds/starling:latest

⁠Example Commands

# Generate an ensemble with GPU acceleration
docker run --rm --gpus all -v /path/to/data:/data getwilds/starling:latest \
  starling --sequence MKVIFLAVLGLGIVVTTVLY --output /data/ensemble.starling

# Generate an ensemble from a FASTA file (GPU)
docker run --rm --gpus all -v /path/to/data:/data getwilds/starling:latest \
  starling --fasta /data/sequences.fasta --output /data/ensemble.starling

# CPU-only mode (omit --gpus flag)
docker run --rm -v /path/to/data:/data getwilds/starling:latest \
  starling --sequence MKVIFLAVLGLGIVVTTVLY --output /data/ensemble.starling

# Convert a STARLING output file to PDB + XTC trajectory
docker run --rm -v /path/to/data:/data getwilds/starling:latest \
  starling2xtc --input /data/ensemble.starling --output /data/ensemble

# Query information about a .starling file
docker run --rm -v /path/to/data:/data getwilds/starling:latest \
  starling2info --input /data/ensemble.starling

# Using Apptainer with GPU support
apptainer run --nv --bind /path/to/data:/data docker://getwilds/starling:latest \
  starling --sequence MKVIFLAVLGLGIVVTTVLY --output /data/ensemble.starling

⁠Dockerfile Structure

The Dockerfile follows these main steps:

1. Uses ubuntu:24.04 as the base image; CUDA 12.6 libraries come bundled with the PyTorch cu126 wheels (see below), and the host NVIDIA driver is injected at runtime
2. Adds metadata labels for documentation and attribution
3. Installs Python 3, python3-dev, and build dependencies (gcc, g++, make) needed for Cython compilation
4. Installs PyTorch 2.6.0 with CUDA 12.6 support
5. Installs STARLING v2.0.0a3 and remaining Python dependencies via pip
6. Removes build dependencies to minimize image size
7. Performs a smoke test to verify the installation
8. Cleans up apt caches to minimize image size

⁠Security Scanning and CVEs

These images are regularly scanned for vulnerabilities using Docker Scout. However, due to the nature of bioinformatics software and their dependencies, some Docker images may contain components with known vulnerabilities (CVEs).

Use at your own risk: While we strive to minimize security issues, these images are primarily designed for research and analytical workflows in controlled environments.

For the latest security information about this image, please check the CVEs_*.md files in this directory⁠, which are automatically updated through our GitHub Actions workflow. If a particular vulnerability is of concern, please file an issue⁠ in the GitHub repo citing which CVE you would like to be addressed.

⁠Source Repository

These Dockerfiles are maintained in the WILDS Docker Library⁠ repository.