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LEGOLAS 🧝‍♀️🏹

A fast and accurate machine learning model built on PyTorch for predicting protein chemical shifts from PDB structures and molecular dynamics trajectories. This model is designed to be run on either CPUs or GPUs.

Model Architecture

Table of Contents

Installation

  1. Clone the repository using --recursive to include the required submodule:
 git clone --recursive https://github.com/roitberg-group/legolas.git
 cd legolas
  1. Set up a conda environment: Use the provided legolas_env.yaml file to create the conda environment:
conda env create -n legolas -f legolas_env.yaml
conda activate legolas

Versions when installing using conda:

  • python 3.10
  • cuda 11.8
  • pytorch 2.5.1
  1. Install TorchANI:

Within external/internal-legolas-aev:

LEGOLAS is most efficient when run using the torchani compiled cuAEV extension, but it is not required.

You have two options, depending on whether you want to install the torchani compiled extensions. To install torchani with no compiled extensions run:

pip install --no-deps -v .

To install torchani with the cuAEV compiled extension run instead:

# Use 'ext-all-sms' instead of 'ext' if you want to build for all possible GPUs
pip install --config-settings=--global-option=ext --no-build-isolation --no-deps -v .

In both cases you can add the editable, -e, flag after the verbose, -v, flag if you want an editable install (for developers). The -v flag can of course be omitted, but it is sometimes handy to have some extra information about the installation process.

Usage

To run LEGOLAS, use the following command:

# atypes = HA, H, CA, CB, C, N
python legolas.py {coordinates_file(s)} [-b {BATCH_SIZE}] [-atype {INTERESTED_ATYPES}] [-t {TOPOLOGY}] [-o {OUTPUT_FILETYPE}]

Examples:

# All atom types:
python legolas.py data/A001_1KF3A.pdbH

# Specfy atom types:
python legolas.py data/A001_1KF3A.pdbH -atype H,C,N

# Run on molecular dynamics trajectory:
python legolas.py data/{trajectory_file}.nc -t data/{topology_file}.parm7

# Specify output file type ("csv", "parquet", "pdbcs", "all", default=all)
# pdbcs output file type is only available for PDB inputs (not trajectories)
python legolas.py data/A001_1KF3A.pdbH -o csv,pdbcs

Expected .csv Output

Column Name Description
ATOM_TYPE Atom type: N, CA, CB, C, HA, H
SEQ_ID Residue sequence number
RES_TYPE Three-letter code for the 20 standard amino acids
CHEMICAL_SHIFT Predicted chemical shift (average over 5 models)
CHEMICAL_SHIFT_STD Standard deviation across 5 models (lower std = higher confidence)

Contributing

If you find a bug or have some feature request, please feel free to open an issue on GitHub or send us a pull request.

License

This project is licensed under the MIT License.

Citation

Please cite the following paper if you use LEGOLAS:

Mikayla Y. Darrows, Dimuthu Kodituwakku, Jinze Xue, Ignacio Pickering, Nicholas S. Terrel, Adrian E. Roitberg. LEGOLAS: a Machine Learning method for rapid and accurate predictions of protein NMR chemical shifts.

J. Chem. Theory Comput. 2025, 21, 8, 4266–4275

https://doi.org/10.1021/acs.jctc.5c00026