Deep DNAshape — PyTorch weights
PyTorch (.pt) conversions of the pretrained Deep DNAshape
TensorFlow checkpoints, for use with pyaptamer.
These are not newly trained models. Every tensor is a bit-exact copy of the
corresponding variable in the upstream TensorFlow checkpoint, with the Conv1D
kernel transposed from TensorFlow's (kernel, in, out) layout to PyTorch's
(out, in, kernel). All credit for the models belongs to the original authors.
Contents
One state_dict per DNA shape feature, 27 in total.
Intra-base pair features (4 input channels, one value per base):
Buckle, EP, MGW, Opening, ProT, Shear, Stagger, Stretch
Inter-base pair features (16 input channels, one value per base step):
HelT, Rise, Roll, Shift, Slide, Tilt
Each feature also has a -FL variant trained with extended flanking regions.
Usage
import torch
from huggingface_hub import hf_hub_download
path = hf_hub_download(repo_id="<this-repo>", filename="MGW.pt")
state_dict = torch.load(path, weights_only=True)
The state_dict targets a 7-layer message-passing architecture with 64 filters.
See the pyaptamer deepdnashape module for the matching model definition.
Credit
Original method, training and weights by Jinsen Li, Tsu-Pei Chiu and Remo Rohs.
- Source repository: https://github.com/JinsenLi/deepDNAshape
- Webserver: https://deepdnashape.usc.edu/
If you use these weights, please cite the original work:
Li, J., Chiu, T.-P. & Rohs, R. Predicting DNA structure using a deep learning method. Nature Communications 15, 1243 (2024). https://doi.org/10.1038/s41467-024-45191-5
License
BSD 3-Clause, inherited from the upstream project.
Copyright (c) 2023, JinsenLi. The full license text is in LICENSE.