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G-ReInCATALiZE

GPU-accelerated Reinforcement learning-enabled Combination of Automated Transformer-based Approaches with Ligand binding and 3D prediction for Enzyme evolution
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgments

About The Project

This project is the result of collabborative work from the CCBIO team at the university of Applied Sciences (ZHAW) in Wädenswil.

Use G-Reincatalyze for in-silivo evolution purposes.

• Find the best mutant froma wildtype enzyme for targetet transformation of selected ligands.

Use the BLANK_README.md to get started.

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Prerequisites: Working with Docker

This is an example of how to list things you need to use the software and how to install them.

1. Docker
   Follow the Install using the apt repository chapter in: https://docs.docker.com/engine/install/ubuntu/#set-up-the-repository
2. nvidia-container-toolkit
   1. Add repository
      if you have ubuntu23 just change the distribution variable
   2. sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
   3. sudo nvidia-ctk runtime configure --runtime=docker
   4. sudo systemctl restart docker
   5. Check successfull installation with:
      sudo docker run --rm --runtime=nvidia --gpus all nvidia/cuda:11.6.2-base-ubuntu20.04 nvidia-smi
      you should see the normal output of the nvidia-smi command.

Whats inside the docker container:

Boost library

1. Download
   • https://boostorg.jfrog.io/artifactory/main/release/1.82.0/source/boost_1_82_0.tar.bz2
2. mv to /usr/local/ #default location
3. Extract: tar --bzip2 -xf boost_1_82_0.tar.bz2
4. sudo ./bootstrap.sh
5. sudo ./b2 install #now we want to make the path globally accessable
6. Add this line to your ~/.bashrc file
   export PATH="$PATH:/usr/local/boost_1_82_0/stage/lib"

Vina-GPU-2.0

Before setting up Vina-GPU make sure to have exportet the LD_LIBRARY_PATH from above

1. Clone the Vina-GPU-2.0 repository
2. Change the makefile file to:
   # Need to be modified according to different users
   BOOST_LIB_PATH=/usr/local/boost_1_82_0
   OPENCL_LIB_PATH=/usr/local/cuda
   OPENCL_VERSION=-DOPENCL_3_0
   GPU_PLATFORM=-DNVIDIA_PLATFORM`
3. cd into the Vina-GPU-2.0/Vina-GPU+ dir
4. make clean && make source ignore warnings

Autodock-Vina (for scripts)

1. git clone https://github.com/ccsb-scripps/AutoDock-Vina.git

Open babel

First you need cmake:

1. cd /usr/local/
2. wget https://github.com/Kitware/CMake/releases/download/v3.26.4/cmake-3.26.4-linux-x86_64.sh
3. chmod +x cmake-3.26.4-linux-x86_64.sh
4. sudo ./cmake-3.26.4-linux-x86_64.sh
5. sudo rm cmake-3.26.4-linux-x86_64.sh
6. export PATH="$PATH:/usr/local/cmake-3.26.4-linux-x86_64/bin"

Binary location for manual download: https://sourceforge.net/projects/openbabel/files/openbabel/2.4.1/

RATHER DO WITH CONDA: ``conda install -c conda-forge openbabel

conda install -c conda-forge openbabel

If conda doesnt work:

1. bash download
   wget https://sourceforge.net/projects/openbabel/files/openbabel/2.4.1/openbabel-2.4.1.tar.gz/download -O openbabel-2.4.1.tar.gz
2. tar -xf openbabel-2.4.1.tar.gz
3. cd openbabel-2.4.1
4. mkdir build && cd build
5. cmake ..
6. make -j2
7. sudo make install

Pyrossetta

Follow instructions: https://www.pyrosetta.org/downloads (tar is in /docker_reincat_pipeline) chapter: Installation with an environment manager

ADFRsuite-1.0

1. download linux version: wget https://ccsb.scripps.edu/adfr/download/1028/ -O ADFRsuite_Linux-x86_64_1.0_install
2. make it executable with chmod a+x ADFRsuite_Linux-x86_64_1.0_install
3. and install with sh ADFRsuite_Linux-x86_64_1.0_install

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Usage

To use the G-Reincatalyze Pipeline you should create/adapt your config.yaml file with your specifications

For more examples, please refer to the Documentation

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1. Build docker docker build --platform linux/amd64 -t gaesp .
2. Run Image with GPU docker run -d --gpus all --name XXX -p 80:80 gaesp

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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