| Name | rf2t-micro JSON |
| Version |
0.0.1.post1
JSON |
| download |
| home_page | None |
| Summary | Stripped-down RosettaFold-2track. |
| upload_time | 2024-09-22 17:25:33 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2021 RosettaCommons Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
science
biology
systems biology
biochemistry
machine learning
analysis
deep learning
rosettafold
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# 🌹 RoseTTAFold-2track micro
Predict protein-protein interaction interfaces using deep learning.
**`rf2t-micro`** uses an implementation of [RoseTTAFold](https://github.com/RosettaCommons/RoseTTAFold)-2track with streamlined installation, a command-line interface, a Python API, and resilience to out-of-memory error. It takes (paired) multiple-sequence alignments in A3M format (as generated by tools like [`hhblits`](https://github.com/soedinglab/hh-suite)) as input, and outputs a matrix of inter-residue contacts.
## Installation
Obtaining and setting up `rf2t-micro` is easy.
```bash
$ pip install git+https://github.com/scbirlab/rf2t-micro
```
Using the embedded model requires using the [RoseTTAFold](https://github.com/RosettaCommons/RoseTTAFold)-2track weights. These are automatically downloaded, but by using `rf2t-micro` you agree that the trained weights for RoseTTAFold are made available for non-commercial use only under the terms of the [Rosetta-DL Software license](https://files.ipd.uw.edu/pub/RoseTTAFold/Rosetta-DL_LICENSE.txt).
## Usage
You can always get more help by running
```bash
$ rf2t-micro run --help
```
Once you have your (paired) MSA file in A3M format, you can run
```bash
$ rf2t-micro run msa-file.a3m --chain-a-length 224
```
It is required to specify the length of the first protein of the protein pair using the `--chain-a-length` option. If you haven't run the model before, the weights will be downloaded automatically which may take a few minutes.
By default, `rf2t-micro` tries to use GPU acceleration where available. To force only CPU use, use the `--cpu` option.
You can ask for visualisations of the residue interaction matrix by specifying `--plot output-directory`. You can use your own RF-2track parameters with the `--params param-file.npz` option.
## ... if you want larger scale and unpaired MSAs
The `rf2t-micro` package was made primarily to be a lightweight dependency of a portable `pip`-installable protein-protein interaction screening tool called [`yunta`](https://github.com/scbirlab/yunta), which in turn could be used in our [`nf-ggi` Nextflow pipeline](https://github.com/scbirlab/nf-ggi) so that HPC clusters could be conveniently used to screen for protein-protein interactions at scale.
[`yunta`](https://github.com/scbirlab/yunta) provides a command-line interface and Python API for predicting protein-protein interactions using GPU-accelerated direct coupling analysis (DCA), RoseTTAFold-2track, and structures usign AlphaFold2 from unpaired MSAs.
## Credit to performer-pytorch and SE(3)-Transformer codes
The code in the network/performer_pytorch.py is strongly based on [this repo](https://github.com/lucidrains/performer-pytorch) which is pytorch implementation of [Performer architecture](https://arxiv.org/abs/2009.14794).
The codes in network/equivariant_attention is from the original SE(3)-Transformer [repo](https://github.com/FabianFuchsML/se3-transformer-public) which accompanies [the paper](https://arxiv.org/abs/2006.10503) 'SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks' by Fabian et al.
## References
M. Baek, et al., Accurate prediction of protein structures and interactions using a three-track neural network, _Science_ (2021). [link](https://www.science.org/doi/10.1126/science.abj8754)
I.R. Humphreys, J. Pei, M. Baek, A. Krishnakumar, et al, Computed structures of core eukaryotic protein complexes, _Science_ (2021). [link](https://www.science.org/doi/10.1126/science.abm4805)
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