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.
- Clone the repository:
git clone https://github.com/roitberg-group/legolas.git
cd legolas- Set up a conda environment using the provided legolas_env.yaml:
# The environment provides, among other dependencies:
# - torchani
# - python 3.11
# - cudatoolkit 12.9
# - pytorch 2.8
conda env create -f legolas_env.yaml
conda activate legolas- (optional) Install the TorchANI 2.0 compiled extensions. LEGOLAS is most efficient with them.
# torchani provides the "ani" CLI command
ani build-extensions# atypes = HA, H, CA, CB, C, N
python legolas.py <COORDINATES_FILE(S)> [-b BATCH_SIZE] [-atype REQUESTED_ATYPES] [-t TOPOLOGY] [-o OUTPUT_FILETYPE]# 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| 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) |
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.
This project is licensed under the MIT License.
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