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Published October 4, 2024 | Version V1.0
Dataset Open

Supplementary materials for "Improving diffusion-based protein backbone generation with global-geometry-aware latent encoding"

  • 1. ROR icon Tsinghua University
  • 2. ROR icon National Institute of Biological Sciences, Beijing
  • 3. ROR icon University of California, San Diego

Description

Info

This dataset contains the supplementary materials for  "Improving diffusion-based protein backbone generation with global-geometry-aware latent encoding". 

For source code and detailed instructions on usage, please refer to our github .

Supplementary data

weights.zip

The trained model weights used in the paper.

dataset.zip

CATH-60 Dataset used in the paper. In the notebook directory of our github , we provide an example on encoding and visualize it with our trained encoder.

design.zip

The 21 novel mainly-beta designs selected for experiment validation. Along with the generated backbone, we also provide the prediction results from AlphaFold and ESMFold.

benchmark_sample.zip

Sampled backbones used for all benchmark experiment (All methods and variants included).

Files

weights.zip

Files (1.2 GB)

Name Size Download all
md5:5293ae9e5faa29ba01605e8905ec4b70
384.9 MB Preview Download
md5:ef7f6d12244155630b47d2c2b66d2ef6
666.1 MB Preview Download
md5:ebff2aa83163878ab9064b1aa3217815
2.2 MB Preview Download
md5:c6162cf826c4c50b7d366302bf36e323
154.9 MB Preview Download

Additional details

Related works

Is supplement to
Preprint: 10.1101/2024.10.05.616664 (DOI)

Software

Repository URL
https://github.com/meneshail/TopoDiff/tree/main
Programming language
Python , Jupyter Notebook
Development Status
Active