d-script
A structure-aware interpretable deep learning model for sequence-based prediction of protein-protein interactions
Science Score: 67.0%
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Repository
A structure-aware interpretable deep learning model for sequence-based prediction of protein-protein interactions
Basic Info
- Host: GitHub
- Owner: samsledje
- License: mit
- Language: Python
- Default Branch: main
- Homepage: http://dscript.csail.mit.edu
- Size: 156 MB
Statistics
- Stars: 100
- Watchers: 3
- Forks: 21
- Open Issues: 2
- Releases: 20
Metadata Files
README.md
D-SCRIPT
D-SCRIPT is a deep learning method for predicting a physical interaction between two proteins given just their sequences. It generalizes well to new species and is robust to limitations in training data size. Its design reflects the intuition that for two proteins to physically interact, a subset of amino acids from each protein should be in contact with the other. The intermediate stages of D-SCRIPT directly implement this intuition, with the penultimate stage in D-SCRIPT being a rough estimate of the inter-protein contact map of the protein dimer. This structurally-motivated design enhances the interpretability of the results and, since structure is more conserved evolutionarily than sequence, improves generalizability across species.
You can now make predictions with D-SCRIPT via the interface on HuggingFace!
Installation
bash
pip install dscript
Usage
Protein sequences need to first be embedded using the Bepler+Berger protein language model; this requires a .fasta file as input. Everything before the first space will be used as the key.
bash
dscript embed --seqs [sequences] --outfile [embedding file]
Candidate pairs should be in tab-separated (.tsv) format with no header, and columns for [protein key 1], [protein key 2]. Optionally, a third column with [label] can be provided, so predictions can be made using training or test data files (but the label will not affect the predictions only the first two columns will be read).
While pre-trained model files can be downloaded directly, we recommend instead passing the name of a pre-trained model that will be automatically downloaded from HuggingFace. Available models include:
- samsl/dscripthumanv1
- samsl/topsyturvyhuman_v1 (recommended)
- samsl/tt3dhumanv1
bash
dscript predict --pairs [input data] --embeddings [embedding file] --model [model file] --outfile [predictions file]
References
- The original D-SCRIPT model is described in the paper “Sequence-based prediction of protein-protein interactions: a structure-aware interpretable deep learning model”.
- We have updated D-SCRIPT to incorporate network information (Topsy Turvy) and structure information (TT3D)
Owner
- Name: Samuel Sledzieski
- Login: samsledje
- Kind: user
- Location: Cambridge, MA
- Company: Massachusetts Institute of Technology
- Website: samsl.io
- Twitter: samsledzieski
- Repositories: 4
- Profile: https://github.com/samsledje
PhD student @ MIT. Studying computational biology and bioinformatics.
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Sledzieski"
given-names: "Samuel"
orcid: "https://orcid.org/0000-0002-0170-3029"
- family-names: "Singh"
given-names: "Rohit"
orcid: "https://orcid.org/0000-0002-4084-7340"
- family-name: "Devkota"
given-names: "Kapil"
orcid: "https://orcid.org/0000-0002-6093-6260"
title: "D-SCRIPT"
version: 0.2.0
doi: 10.1016/j.cels.2021.08.010
date-released: 2022-06-26
url: "https://github.com/samsledje/D-SCRIPT"
preferred-citation:
type: article
authors:
- family-names: "Sledzieski"
given-names: "Samuel"
orcid: "https://orcid.org/0000-0002-0170-3029"
- family-names: "Singh"
given-names: "Rohit"
orcid: "https://orcid.org/0000-0002-4084-7340"
- family-names: "Cowen"
given-names: "Lenore"
orcid: "https://orcid.org/0000-0001-6698-6413"
- family-names: "Berger"
given-names: "Bonnie"
orcid: "https://orcid.org/0000-0002-2724-7228"
doi: "10.1016/j.cels.2021.08.010"
journal: "Cell Systems"
publisher: "Elsevier"
volume: 12
issue: 10
start: 969
end: 982
title: "D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions"
year: 2021
GitHub Events
Total
- Create event: 7
- Release event: 2
- Issues event: 11
- Watch event: 16
- Delete event: 1
- Issue comment event: 17
- Push event: 16
- Pull request review event: 5
- Pull request review comment event: 5
- Pull request event: 5
- Fork event: 3
Last Year
- Create event: 7
- Release event: 2
- Issues event: 11
- Watch event: 16
- Delete event: 1
- Issue comment event: 17
- Push event: 16
- Pull request review event: 5
- Pull request review comment event: 5
- Pull request event: 5
- Fork event: 3
Dependencies
- actions/checkout v3 composite
- actions/setup-python v3 composite
- actions/checkout v3 composite
- actions/setup-python v3 composite
- pypa/gh-action-pypi-publish 27b31702a0e7fc50959f5ad993c78deac1bdfc29 composite
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- numpy
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- pip 20.0.*
- python 3.7.*
- pytorch 1.11.*
- scikit-learn
- scipy
- seaborn
- setuptools
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- Sphinx ==3.3.1
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- jinja2 <3.1
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- sphinx-rtd-theme ==1.0.0
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- sphinxcontrib-jsmath ==1.0.1
- sphinxcontrib-qthelp ==1.0.3
- sphinxcontrib-serializinghtml ==1.1.4
- torch ==1.5.0
- tqdm *
- biopython *
- h5py *
- matplotlib *
- numpy *
- pandas *
- scikit-learn *
- scipy *
- seaborn *
- setuptools *
- torch ==1.11
- tqdm *
- biopython *
- h5py *
- matplotlib *
- numpy *
- pandas *
- scikit-learn *
- scipy *
- seaborn *
- torch >=1.11
- tqdm *