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--- | ||
output: github_document | ||
--- | ||
|
||
<!-- README.md is generated from README.Rmd. Please edit that file --> | ||
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||
```{r, include = FALSE} | ||
knitr::opts_chunk$set( | ||
collapse = TRUE, | ||
comment = "#>", | ||
fig.path = "man/figures/README-", | ||
out.width = "50%" | ||
) | ||
``` | ||
|
||
# deepdirect | ||
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<!-- badges: start --> | ||
<!-- badges: end --> | ||
|
||
Deepdirect is an in silico approach to generate mutations for protein complexes towards a specified direction (increase/decrease) in binding affinity. | ||
|
||
## System requirements and dependencies | ||
### Hardware Requirements | ||
`deepdirect` model is able to be trained and perform its operations on a standard computer. | ||
|
||
### OS Requirements | ||
The `deepdirect` model should be compatible with Windows, Mac, and Linux operating systems. The package has been tested on the following systems: | ||
|
||
* Linux 3.10.0 | ||
* Windows 10 | ||
|
||
### Dependencies | ||
`deepdirect` framework is built and trained on the `Tensorflow 2.4.0` and `Keras 2.4.0`. | ||
|
||
## Framework construction | ||
|
||
The python file including all required deepdirect framework built function is able to be downloaded from GitHub: `deepdirect_framework/model_function.py` | ||
|
||
|
||
## Data structure | ||
* `data` folder contains the original datasets used for building the training datasets. | ||
* `deepdirect_framework` folder contains the trained model weights and the model constructing functions. | ||
* `deepdirect_paper` folder contains codes for building and training models, and performing analysis in the deepdirect manuscript. The file `ab_bind_data_extract.py` and `skempi_data_extract.py` contains code for constructing training datasets for Deepdirect framework. `train_step_1.py` contains code for training step 1 for the mutation mutator. `final_model.py` contains code for training step 2 (final) for the mutation mutator. `model_function.py` contains code for constructing the Deepdirect framework. `model_evaluation_application.py` contains code for model evaluation, and teh application on Novavax-vaccine. `evolution_analysis.py` contains code for performing evolution analysis. | ||
|
||
## File source | ||
For files that are required as input in the code but not generated from other codes, please refer to the data availability section in the original paper. | ||
|
||
|
||
## Issues and bug reports | ||
Please use <https://github.com/tianlt/deepdirect/issues> to submit issues, bug reports, and comments. | ||
|
||
|
||
|
||
## License | ||
deepdirect is distributed under the [GNU General Public License version 2 (GPLv2)](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html). | ||
|
||
|
||
|
||
--- | ||
output: github_document | ||
--- | ||
|
||
<!-- README.md is generated from README.Rmd. Please edit that file --> | ||
|
||
```{r, include = FALSE} | ||
knitr::opts_chunk$set( | ||
collapse = TRUE, | ||
comment = "#>", | ||
fig.path = "man/figures/README-", | ||
out.width = "50%" | ||
) | ||
``` | ||
|
||
# deepdirect | ||
|
||
<!-- badges: start --> | ||
<!-- badges: end --> | ||
|
||
Deepdirect is an in silico approach to generate mutations for protein complexes towards a specified direction (increase/decrease) in binding affinity. | ||
|
||
## System requirements and dependencies | ||
### Hardware Requirements | ||
`deepdirect` model is able to be trained and perform its operations on a standard computer. | ||
|
||
### OS Requirements | ||
The `deepdirect` model should be compatible with Windows, Mac, and Linux operating systems. The package has been tested on the following systems: | ||
|
||
* Linux 3.10.0 | ||
* Windows 10 | ||
|
||
### Dependencies | ||
`deepdirect` framework is built and trained on the `Tensorflow 2.4.0` and `Keras 2.4.0`. | ||
|
||
## Framework construction | ||
|
||
The python file including all required deepdirect framework built function is able to be downloaded from GitHub: `deepdirect_framework/model_function.py` | ||
|
||
|
||
## Data structure | ||
* `data` folder contains the original datasets used for building the training datasets. | ||
* `deepdirect_framework` folder contains the trained model weights and the model constructing functions. | ||
* `deepdirect_paper` folder contains codes for building and training models, and performing analysis in the deepdirect manuscript. The file `ab_bind_data_extract.py` and `skempi_data_extract.py` contains code for constructing training datasets for Deepdirect framework. `train_step_1.py` contains code for training step 1 for the mutation mutator. `final_model.py` contains code for training step 2 (final) for the mutation mutator. `model_function.py` contains code for constructing the Deepdirect framework. `model_evaluation_application.py` contains code for model evaluation, and teh application on Novavax-vaccine. `evolution_analysis.py` contains code for performing evolution analysis. | ||
|
||
## File source | ||
For files that are required as input in the code but not generated from other codes, please refer to the data availability section in the original paper. | ||
|
||
## Installation | ||
Clone repository: | ||
|
||
``` | ||
git clone https://github.com/tianlt/deepdirect.git | ||
``` | ||
|
||
Create virtual environment: | ||
|
||
``` | ||
conda create --name deepdirect python=3.6.8 | ||
``` | ||
|
||
Activate virtual environment: | ||
|
||
``` | ||
conda activate deepdirect | ||
``` | ||
|
||
Install dependencies: | ||
|
||
``` | ||
pip install tensorflow==2.4.0 | ||
pip install keras==2.4.0 | ||
``` | ||
|
||
## Running deepdirect | ||
### data processing | ||
Data to be input to deepdirect include sequence to be mutated `pre`, RBD site `rbd`, ligand-receptor index `same`, protein tertiary structure information `x`, `y` and `z`, and random noise `input_noi`. All input has to be `tf.float32` type. | ||
|
||
|
||
### Build deepdirect mutator with trained weights | ||
``` | ||
aa_mutator = build_aa_mutator() | ||
aa_mutator.load_weights( | ||
'deepdirect_framework/model_i_weights.h5') | ||
``` | ||
|
||
### Binding affinity-guided mutation | ||
``` | ||
aa_mutator.predict([pre, rbd, same, x, y, z, input_noi]) | ||
``` | ||
|
||
### Additional information | ||
Expected outputs: mutated amino acid sequence | ||
|
||
Expected runtime for mutation: ~1 mintue | ||
|
||
|
||
## Issues and bug reports | ||
Please use <https://github.com/tianlt/deepdirect/issues> to submit issues, bug reports, and comments. | ||
|
||
|
||
|
||
## License | ||
deepdirect is distributed under the [GNU General Public License version 2 (GPLv2)](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html). | ||
|
||
|
||
|
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -1,74 +1,116 @@ | ||
|
||
<!-- README.md is generated from README.Rmd. Please edit that file --> | ||
|
||
# deepdirect | ||
|
||
<!-- badges: start --> | ||
|
||
<!-- badges: end --> | ||
|
||
Deepdirect is an in silico approach to generate mutations for protein | ||
complexes towards a specified direction (increase/decrease) in binding | ||
affinity. | ||
|
||
## System requirements and dependencies | ||
|
||
### Hardware Requirements | ||
|
||
`deepdirect` model is able to be trained and perform its operations on a | ||
standard computer. | ||
|
||
### OS Requirements | ||
|
||
The `deepdirect` model should be compatible with Windows, Mac, and Linux | ||
operating systems. The package has been tested on the following systems: | ||
|
||
- Linux 3.10.0 | ||
- Windows 10 | ||
|
||
### Dependencies | ||
|
||
`deepdirect` framework is built and trained on the `Tensorflow 2.4.0` | ||
and `Keras 2.4.0`. | ||
|
||
## Framework construction | ||
|
||
The python file including all required deepdirect framework built | ||
function is able to be downloaded from GitHub: | ||
`deepdirect_framework/model_function.py` | ||
|
||
## Data structure | ||
|
||
- `data` folder contains the original datasets used for building the | ||
training datasets. | ||
- `deepdirect_framework` folder contains the trained model weights and | ||
the model constructing functions. | ||
- `deepdirect_paper` folder contains codes for building and training | ||
models, and performing analysis in the deepdirect manuscript. The | ||
file `ab_bind_data_extract.py` and `skempi_data_extract.py` contains | ||
code for constructing training datasets for Deepdirect framework. | ||
`train_step_1.py` contains code for training step 1 for the mutation | ||
mutator. `final_model.py` contains code for training step 2 (final) | ||
for the mutation mutator. `model_function.py` contains code for | ||
constructing the Deepdirect framework. | ||
`model_evaluation_application.py` contains code for model | ||
evaluation, and teh application on Novavax-vaccine. | ||
`evolution_analysis.py` contains code for performing evolution | ||
analysis. | ||
|
||
## File source | ||
|
||
For files that are required as input in the code but not generated from | ||
other codes, please refer to the data availability section in the | ||
original paper. | ||
|
||
## Issues and bug reports | ||
|
||
Please use <https://github.com/tianlt/deepdirect/issues> to submit | ||
issues, bug reports, and comments. | ||
|
||
## License | ||
|
||
deepdirect is distributed under the [GNU General Public License | ||
version 2 | ||
(GPLv2)](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html). | ||
|
||
<!-- README.md is generated from README.Rmd. Please edit that file --> | ||
|
||
# deepdirect | ||
|
||
<!-- badges: start --> | ||
<!-- badges: end --> | ||
|
||
Deepdirect is an in silico approach to generate mutations for protein | ||
complexes towards a specified direction (increase/decrease) in binding | ||
affinity. | ||
|
||
## System requirements and dependencies | ||
|
||
### Hardware Requirements | ||
|
||
`deepdirect` model is able to be trained and perform its operations on a | ||
standard computer. | ||
|
||
### OS Requirements | ||
|
||
The `deepdirect` model should be compatible with Windows, Mac, and Linux | ||
operating systems. The package has been tested on the following systems: | ||
|
||
- Linux 3.10.0 | ||
- Windows 10 | ||
|
||
### Dependencies | ||
|
||
`deepdirect` framework is built and trained on the `Tensorflow 2.4.0` | ||
and `Keras 2.4.0`. | ||
|
||
## Framework construction | ||
|
||
The python file including all required deepdirect framework built | ||
function is able to be downloaded from GitHub: | ||
`deepdirect_framework/model_function.py` | ||
|
||
## Data structure | ||
|
||
- `data` folder contains the original datasets used for building the | ||
training datasets. | ||
- `deepdirect_framework` folder contains the trained model weights and | ||
the model constructing functions. | ||
- `deepdirect_paper` folder contains codes for building and training | ||
models, and performing analysis in the deepdirect manuscript. The file | ||
`ab_bind_data_extract.py` and `skempi_data_extract.py` contains code | ||
for constructing training datasets for Deepdirect framework. | ||
`train_step_1.py` contains code for training step 1 for the mutation | ||
mutator. `final_model.py` contains code for training step 2 (final) | ||
for the mutation mutator. `model_function.py` contains code for | ||
constructing the Deepdirect framework. | ||
`model_evaluation_application.py` contains code for model evaluation, | ||
and teh application on Novavax-vaccine. `evolution_analysis.py` | ||
contains code for performing evolution analysis. | ||
|
||
## File source | ||
|
||
For files that are required as input in the code but not generated from | ||
other codes, please refer to the data availability section in the | ||
original paper. | ||
|
||
## Installation | ||
|
||
Clone repository: | ||
|
||
git clone https://github.com/tianlt/deepdirect.git | ||
|
||
Create virtual environment: | ||
|
||
conda create --name deepdirect python=3.6.8 | ||
|
||
Activate virtual environment: | ||
|
||
conda activate deepdirect | ||
|
||
Install dependencies: | ||
|
||
pip install tensorflow==2.4.0 | ||
pip install keras==2.4.0 | ||
|
||
## Running deepdirect | ||
|
||
### data processing | ||
|
||
Data to be input to deepdirect include sequence to be mutated `pre`, RBD | ||
site `rbd`, ligand-receptor index `same`, protein tertiary structure | ||
information `x`, `y` and `z`, and random noise `input_noi`. All input | ||
has to be `tf.float32` type. | ||
|
||
### Build deepdirect mutator with trained weights | ||
|
||
aa_mutator = build_aa_mutator() | ||
|
||
aa_mutator.load_weights( | ||
'deepdirect_framework/model_i_weights.h5') | ||
|
||
### Binding affinity-guided mutation | ||
|
||
aa_mutator.predict([pre, rbd, same, x, y, z, input_noi]) | ||
|
||
### Additional information | ||
|
||
Expected outputs: mutated amino acid sequence | ||
|
||
Expected runtime for mutation: ~1 mintue | ||
|
||
## Issues and bug reports | ||
|
||
Please use <https://github.com/tianlt/deepdirect/issues> to submit | ||
issues, bug reports, and comments. | ||
|
||
## License | ||
|
||
deepdirect is distributed under the [GNU General Public License version | ||
2 (GPLv2)](https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html). |