https://github.com/ur-whitelab/peptide-dashboard

Web cards/apps describing peptides
https://github.com/ur-whitelab/peptide-dashboard

Last synced: about 1 month ago
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Web cards/apps describing peptides

• Host: GitHub
• URL: https://github.com/ur-whitelab/peptide-dashboard
• Owner: ur-whitelab
• License: gpl-3.0
• Created: 2018-04-03T19:52:40.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2023-04-26T03:42:28.000Z (over 1 year ago)
• Last Synced: 2024-04-15T15:11:13.639Z (9 months ago)
• Language: Jupyter Notebook
• Homepage: https://peptide.bio
• Size: 65.3 MB
• Stars: 21
• Watchers: 3
• Forks: 2
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
Peptide Dashboard

=====

![concept](https://user-images.githubusercontent.com/51170839/231787783-91f143fe-2035-4e89-bf09-bd9ffda0260d.png)

We present three deep learning sequence-based prediction models for peptide properties including hemolysis, solubility, and resistance to nonspecific interactions that achieve comparable results to the state-of-the-art models. Our sequence-based solubility predictor, MahLooL, outperforms the current state-of-the-art methods for short peptides. These models are implemented as a static website without the use of a dedicated server or cloud computing. Web-based models like this allow for accessible and effective reproducibility. Most existing approaches rely on third-party servers that typically require upkeep and maintenance. Our predictive models do not require servers, require no installation of dependencies, and work across a range of devices. The specific architecture is bidirectional recurrent neural networks. This serverless approach is a demonstration of edge machine learning that removes the dependence on cloud providers. 

Web-app: [peptide.bio](https://peptide.bio)

## CLI Implementation

Check out [this notebook](https://github.com/ur-whitelab/peptide-dashboard/blob/master/examples/Quick_start.ipynb) for the CLI implementation of our trained models.

## Citation

[See paper](https://pubs.acs.org/doi/10.1021/acs.jcim.2c01317) and the citation:

```bibtex

@article{Ansari2023,

  doi = {10.1021/acs.jcim.2c01317},

  url = {https://doi.org/10.1021/acs.jcim.2c01317},

  year = {2023},

  month = apr,

  publisher = {American Chemical Society ({ACS})},

  volume = {63},

  number = {8},

  pages = {2546--2553},

  author = {Mehrad Ansari and Andrew D. White},

  title = {Serverless Prediction of Peptide Properties with Recurrent Neural Networks},

  journal = {Journal of Chemical Information and Modeling}

}

```