BioPhi is an open-source antibody design platform. It features methods for automated antibody humanization (Sapiens), humanness evaluation (OASis) and an interface for computer-assisted antibody sequence design.
Learn more in the BioPhi, Sapiens and OASis in our publication:
David Prihoda, Jad Maamary, Andrew Waight, Veronica Juan, Laurence Fayadat-Dilman, Daniel Svozil & Danny A. Bitton (2022) BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning, mAbs, 14:1, DOI: https://doi.org/10.1080/19420862.2021.2020203
The university-hosted BioPhi web server is available at: http://biophi.dichlab.org
For more information about the Sapiens antibody language model, see the Sapiens repository
The data and notebooks supporting the analysis are found in the BioPhi-2021-publication repository
Intro video
Contributing
BioPhi is an open and extensible platform, contributions are welcome.
If you have ideas about what to improve or which tools could be integrated, please submit any feature requests using the Issues tab.
Running BioPhi on your machine
If you don't want to use the public BioPhi server, you can run BioPhi on your own machine.
1. Download OASis database
To run BioPhi with OASis humanness evaluation locally, you will need to download and unzip the OASis database file (22GB uncompressed).
# Download database file
wget https://zenodo.org/record/5164685/files/OASis_9mers_v1.db.gz
# Unzip
gunzip OASis_9mers_v1.db.gz2. Install BioPhi using Conda
You can install BioPhi using Conda or one of the alternatives (Miniconda, Miniforge).
Install BioPhi using:
# Recommended: Create a separate BioPhi environment
conda create -n biophi python=3.9
conda activate biophi
# Install BioPhi
# Using Bioconda and Conda-Forge channels
conda install biophi -c bioconda -c conda-forge --override-channelsIf conda installation fails, you can try running using Docker. See Run BioPhi using provided Docker image.
3. Run simplified server
# Set up path to OASis database (downloaded and unzipped)
export OASIS_DB_PATH=/path/to/downloaded/OASis_9mers_v1.db
# Run simplified BioPhi server (not for live deployment!)
biophi webNote: This is simplified usage for local use only. See Deploying your own BioPhi server section below to learn about deploying BioPhi properly on a server.
Run BioPhi using provided Docker image
First, download OASis DB as described above.
Then, run a simplified BioPhi server using the provided Docker image:
docker run \
-v /your/absolute/path/to/oasis/directory/:/data \
-e OASIS_DB_PATH=/data/OASis_9mers_v1.db \
-p 5000:5000 \
quay.io/biocontainers/biophi:1.0.5--pyhdfd78af_0 \
biophi web --host 0.0.0.0The application will be accessible at localhost:5000.
Note: This is simplified usage for local use only. See Deploying your own BioPhi server section below to learn about deploying BioPhi properly on a server.
BioPhi command-line interface
BioPhi also provides a command-line interface that enables bulk processing.
See more
```bash # Get humanized FASTA # Expected input: Both chains of each antibody should have the same ID # with an optional _VL/_VH or _HC/_LC suffix biophi sapiens mabs.fa --fasta-only --output humanized.fa # Run full humanization & humanness evaluation pipeline biophi sapiens mabs.fa \ --oasis-db path/to/downloaded/OASis_9mers_v1.db \ --output humanized/ # Get the Sapiens probability matrix (score of each residue at each position) biophi sapiens mabs.fa --scores-only --output scores.csv # Get mean Sapiens score (one score for each sequence) biophi sapiens mabs.fa --mean-score-only --output scores.csv # Get OASis humanness evaluation biophi oasis mabs.fa \ --oasis-db path/to/downloaded/OASis_9mers_v1.db \ --output oasis.xlsx ```Development
BioPhi is composed of three services that need to be running at the same time:
web: Flask web server that handles both the frontend and the backend of the web applicationcelery: Asynchronous worker service(s) that process long-running tasksredis: In-memory database for storing celery queue tasks and results
Run BioPhi dev server through Docker Compose
Running through Docker Compose is easiest in terms of setup, but web server autoreload is not supported, so you will have to restart the services after each code update.
See more
#### 1. Install Docker See https://docs.docker.com/get-docker/ #### 2. Clone this repository Download or clone this repository using: ```bash git clone https://github.com/Merck/BioPhi.git ``` #### 3. Download OASis DB Download OASis database as described above. Put it in local `data/` dir inside the project folder. #### 4. Build all images using Docker Compose ```bash # Open BioPhi directory cd BioPhi # Build docker image using Makefile make docker-build # or directly using docker-compose build ``` #### 4. Run all services using Docker Compose ```bash # Run using Makefile make docker-run # or directly using docker-compose up ``` The application will be accessible at [localhost:5000](http://localhost:5000). To build and run, you can use: ```bash # Run using Makefile make docker-build docker-run # or directly using docker-compose up --build ``` #### 5. Handle code updates After your code is updated, you will need to stop the services, run build and start again. See the next section for info on running locally with flask auto-reload.Run BioPhi dev server using Conda
Running each service locally using Conda will enable flask auto-reload, which is useful if you are going back and forth between your IDE and the browser.
See more
#### 1. Install Conda Install [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/download.html) or one of the alternatives ([Miniconda](https://docs.conda.io/en/latest/miniconda.html), [Miniforge](https://github.com/conda-forge/miniforge)) #### 2. Install Redis server Install and run [Redis server](https://redis.io/download). On Mac, you can [install Redis using Brew](https://medium.com/@petehouston/install-and-config-redis-on-mac-os-x-via-homebrew-eb8df9a4f298). #### 3. Clone this repository Download or clone this repository using: ```bash git clone https://github.com/Merck/BioPhi.git ``` #### 4. Download OASis DB Download OASis database as described above. #### 5. Setup environment ```bash # Open BioPhi directory cd BioPhi # Install dependencies using the provided Makefile make env # Or directly using conda env create -n biophi -f environment.yml conda activate biophi pip install -e . --no-deps ``` #### 6. Run all services You will have to run each service in a separate terminal (Use Cmd+T to open a new tab): ```bash # Run Redis server (this depends on your installation, the server might already be running) redis-server # In a separate terminal, run celery worker queue export OASIS_DB_PATH=/path/to/OASis_9mers_v1.db make celery # In a separate terminal, run flask web server export OASIS_DB_PATH=/path/to/OASis_9mers_v1.db make web ``` See the provided #### 7. Handle code updates After your code is updated, the flask web service should refresh automatically. However, the celery service needs to be stopped and started manually, so you will need to do that if you update code that is executed from the workers.Deploying your own BioPhi server
You can deploy your own internal BioPhi server. You will need to run the three separate services - the flask web server, the celery worker and the redis database.
This will depend on your platform and your cloud provider, the easiest deployment is using Docker Compose through the provided docker-compose.yml file.
For 🐧 Ubuntu deployment, feel free to copy the deployment configs used on the public university server: lich-uct/biophi.dichlab.org
Acknowledgements
BioPhi is based on antibody repertoires from the Observed Antibody Space:
Kovaltsuk, A., Leem, J., Kelm, S., Snowden, J., Deane, C. M., & Krawczyk, K. (2018). Observed Antibody Space: A Resource for Data Mining Next-Generation Sequencing of Antibody Repertoires. The Journal of Immunology, 201(8), 2502–2509. https://doi.org/10.4049/jimmunol.1800708
Antibody numbering is performed using ANARCI:
Dunbar, J., & Deane, C. M. (2016). ANARCI: Antigen receptor numbering and receptor classification. Bioinformatics, 32(2), 298–300. https://doi.org/10.1093/bioinformatics/btv552