https://github.com/flatironinstitute/deepblast

Neural Networks for Protein Sequence Alignment
https://github.com/flatironinstitute/deepblast

language-modeling neural-networks protein protein-sequences protein-structure sequence-alignment structural-alignments

Last synced: 19 days ago
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Neural Networks for Protein Sequence Alignment

• Host: GitHub
• URL: https://github.com/flatironinstitute/deepblast
• Owner: flatironinstitute
• License: bsd-3-clause
• Created: 2020-06-04T22:28:59.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-11-11T18:12:13.000Z (about 1 month ago)
• Last Synced: 2024-11-11T19:22:49.112Z (about 1 month ago)
• Topics: language-modeling, neural-networks, protein, protein-sequences, protein-structure, sequence-alignment, structural-alignments
• Language: Python
• Homepage:
• Size: 56.7 MB
• Stars: 114
• Watchers: 6
• Forks: 21
• Open Issues: 32
• Metadata Files:
  • Readme: README.md
  • License: COPYING.txt

Awesome Lists containing this project

• awesome-protein-design-software - DeepBLAST - [paper](https://doi.org/10.1101/2020.11.03.365932) (Sequence similarity search and (structural) alignment)

README

        
[![DOI](https://zenodo.org/badge/269478463.svg)](https://zenodo.org/badge/latestdoi/269478463)

# DeepBLAST 

Learning protein structural similarity from sequence alone.  Our preprint can be found [here](https://www.biorxiv.org/content/10.1101/2020.11.03.365932v1)

DeepBLAST is a neural-network based alignment algorithm that can estimate structural alignments. And it can generate structural alignments that are nearly identical to

state-of-the-art structural alignment algorithms.

![Malidup benchmark](/imgs/malidup.png)

# Installation

DeepBLAST can be installed from pip via

```

pip install deepblast

```

To install from the development branch run

```

pip install git+https://github.com/flatironinstitute/deepblast.git

```

# Downloading pretrained models and data

- [DeepBLAST and TMvec checkpoints](https://figshare.com/articles/dataset/TMvec_DeepBLAST_models/25810099)

- [Training data and databases](https://zenodo.org/records/11199459)

See the [Malisam](http://prodata.swmed.edu/malisam/) and [Malidup](http://prodata.swmed.edu/malidup/) websites to download their datasets.

# Getting started

See the [wiki](https://github.com/flatironinstitute/deepblast/wiki) on how to use DeepBLAST and TM-vec for remote homology search and alignment.

If you have questions on how to use DeepBLAST and TM-vec, feel free to raise questions in the [discussions section](https://github.com/flatironinstitute/deepblast/discussions). If you identify any potential bugs, feel free to raise them in the [issuetracker](https://github.com/flatironinstitute/deepblast/issues)

# Citation

If you find our work useful, please cite us at

```

@article{morton2020protein,

  title={Protein Structural Alignments From Sequence},

  author={Morton, Jamie and Strauss, Charlie and Blackwell, Robert and Berenberg, Daniel and Gligorijevic, Vladimir and Bonneau, Richard},

  journal={bioRxiv},

  year={2020},

  publisher={Cold Spring Harbor Laboratory}

}

@article{hamamsy2022tm,

  title={TM-Vec: template modeling vectors for fast homology detection and alignment},

  author={Hamamsy, Tymor and Morton, James T and Berenberg, Daniel and Carriero, Nicholas and Gligorijevic, Vladimir and Blackwell, Robert and Strauss, Charlie EM and Leman, Julia Koehler and Cho, Kyunghyun and Bonneau, Richard},

  journal={bioRxiv},

  pages={2022--07},

  year={2022},

  publisher={Cold Spring Harbor Laboratory}

}

```