cryoligate 1.0.0

Enhancing the resolvability of cryo-EM maps in protein-ligand complexes using deep learning.

Introduction

The full potential of cryo-EM in drug discovery remains limited by poor density resolvability at ligand-binding interfaces. Although recent advances in deep learning have transformed cryo- EM map enhancement, existing approaches largely focus on protein regions and often neglect ligand-containing sites. Here, we present CryoLigate, an AI framework specifically designed to enhance the density resolvability of protein–ligand interfaces. We trained and evaluated CryoLigate across a structurally diverse dataset including pharmaceutical drugs, lipids, steroids, and carbohydrates.

CryoLigate features a streamlined, single-command interface. It automatically isolates the target sub-volume using a preliminary atomic model as a spatial reference, requiring no manual box curation. The pipeline is computationally efficient, with localized refinement completed in seconds on standard desktop-grade GPU hardware.

Installation

Note: We strongly recommend installing CryoLigate in a fresh Python or Conda environment to avoid dependency conflicts.

Option A: Install CryoLigate via PyPI (Recommended):

1. Create a clean environment with Python

conda create -n CryoLigate python=3.10 -y
conda activate CryoLigate

2. Install the package

pip install CryoLigate -U

Option B: Install directly from GitHub for the latest development updates:

1. Clone the repository and step into it

git clone https://github.com/nandanhaloi123/CryoLigate.git
cd CryoLigate

2. Create the environment using the local file

conda env create -f environment.yml
conda activate CryoLigate

3. Link your local directory in editable mode

pip install -e .

If you are installing on CPU-only or non-CUDA GPU hardware, the pipeline will automatically fall back to CPU processing. Note that the CPU version is significantly slower than the GPU version for 3D volumetric refinement.

Inference

Before running inference or fine-tuning, download the pre-trained weights:

mkdir weights
wget -O weights/cryoligate_v1.0.0.pth https://github.com/nandanhaloi123/CryoLigate/releases/download/v1.0.0/cryoligate_v1.0.0.pth```

Download the example data used in the inference command:

mkdir -p example/8ioe
wget -O 8ioe.zip https://github.com/nandanhaloi123/CryoLigate/releases/download/v1.0.0/8ioe.zip
unzip 8ioe.zip -d example/8ioe/
rm 8ioe.zip

Run inference using CryoLigate:

CryoLigate-infer --weights weights/cryoligate_v1.0.0.pth --map example/8ioe/emd_35617.map --pdb example/8ioe/8ioe.cif --resname TPP --chain A --resid 801