This repository contains supplementary information to

Niklas E. Siedhoff^1,§, Alexander-Maurice Illig^1,§, Ulrich Schwaneberg^1,2, Mehdi D. Davari^1,*,
PyPEF – an Integrated Framework for Data-driven Protein Engineering, Journal of Chemical Information and Modeling (2021)
¹_{Institute of Biotechnology, RWTH Aachen University, Worringer Weg 3, 52074 Aachen, Germany}
²_{DWI-Leibniz Institute for Interactive Materials, Forckenbeckstraße 50, 52074 Aachen, Germany}
^*_{Corresponding author}
^§_{Equal contribution}

a framework written in Python 3 for performing sequence-based machine learning-assisted protein engineering.

Protein engineering by rational or random approaches generates data that can aid the construction of self-learned sequence-function landscapes to predict beneficial variants by using probabilistic methods that can screen the unexplored sequence space with uncertainty in silico. Such predictive methods can be applied for increasing the success/effectivity of an engineering campaign while partly offering the prospect to reveal (higher-order) epistatic effects. Here we present an engineering framework termed PyPEF for assisting the tuning and validation of models for combination of identified substitutions using machine learning algorithms (partial least squares (PLS), support vector machines (SVR), random forest (RF), and multilayer perceptron (MLP)-based regression) from the scikit-learn package. As training input, the developed software tool requires the sequence and the corresponding screening results (fitness labels) of the identified variants as .csv (or .fasta datasets following a self-defined convention). Using PLS, SVR, RF, or MLP regression, PyPEF trains on the given learning data while optimizing model hyperparameters and validates model performances on left-out data. Finally, the selected or best model for validation can be used to perform directed evolution walks in silico (see Church-lab implementation or the reimplementation) or to predict natural diverse or recombinant sequences that subsequently are to be designed and validated in the wet-lab.

Detailed information is given in the following publication,

PyPEF - an Integrated Framework for Data-driven Protein Engineering,

and the workflow procedure is explained in the Jupyter notebook (.ipynb) protocol (see ./workflow directory).

PyPEF was developed to be run using a command-line interface:

python3 pypef.py --help
python3 pypef.py mklsvs -w WT_SEQUENCE.FASTA -i VARIANT-FITNESS_DATA.CSV 
python3 pypef.py run -l LEARNING_SET.FASTA -v VALIDATION_SET.FASTA --regressor TYPE 
python3 pypef.py --show
python3 pypef.py run -m MODEL12345 -f VALIDATION_SET.FASTA
python3 pypef.py run -m MODEL12345 -p PREDICTION_SET.FASTA
python3 pypef.py mkps -w WT_SEQUENCE.FASTA -i VARIANT-FITNESS_DATA.CSV --drecomb
python3 pypef.py run -m MODEL12345 --pmult --drecomb
python3 pypef.py directevo -m MODEL12345 --ywt WT_FITNESS -w WT_SEQUENCE.FASTA --usecsv -i VARIANT-FITNESS_DATA.CSV

PyPEF's package dependencies are linked here. A small API for sequence encoding and model validation is provided in the encoding_validation_api directory. Further, for designing your own API based on the PyPEF workflow, modules can be adapted from the source code provided in the src directory.

Before starting running the tutorial, it is a good idea to set-up a new Python environment using Anaconda, https://www.anaconda.com/, e.g. using Anaconda (Anaconda3-2020.11-Linux-x86_64.sh installer download) or Miniconda. Change to the download directory and run the installation, e.g. in Linux:

bash Anaconda3-2020.11-Linux-x86_64.sh

After accepting all steps, the conda setup should also be written to your ~/.bashrcfile, so that you can call anaconda typing conda. Next, to download this repository click Code > Download ZIP and unzip the zipped file, e.g. with unzip PyPEF-main.zip, or just clone this repository using your bash shell to your local machine git clone https://github.com/niklases/PyPEF. To setup a new environment with conda you can either create the conda environment from the provided YAML file inside the PyPEF directory (cd PyPEF or cd PyPEF-main dependent on the downloaded file name):

conda env create --file pypef_environment.yml

or you can create a new environment yourself. You just need to specify the name of the environment and the Python version, e.g.:

conda create --name pypef python=3.7

To activate the environment you can define:

conda activate pypef

After activating the environment you can install required packages after changing the directory to the PyPEF directory (cd PyPEF or cd PyPEF-main) and install required packages with pip if you did not use the YAML file for creating the environment (when using conda packages will be installed in anaconda3/envs/pypef/lib/python3.7/site-packages):

python3 -m pip install -r requirements.txt

and optionally:

python3 -m pip install -r requirements_parallelization.txt

Now, after installing required packages, you should be able to directly run pypef in your preferred command-line interface (see running example).

To run the tutorial after installing required packages either from the YAML environment file or the TEXT file(s), you have to open a Jupyter Notebook. If you have installed Anaconda, Jupyter Notebook and other commonly used packages for scientific computing and data science should be already installed in Python. If not, you can also install Jupyter via conda install ipython jupyter. To use the pypef environment as kernel inside the Jupyter Notebook, you need to install the ipykernel:

conda install -c anaconda ipykernel
python3 -m ipykernel install --user --name=pypef

Now change the directory to ./workflow (cd workflow) and run the .ipynb file:

jupyter notebook

Copy the notebook URL in your internet browser and select the Workflow_PyPEF.ipynb file to open it. Now you can select the pypef Python environment at the top Notebook menu: Kernel > Change kernel > pypef (otherwise you would use your default Python version as environment, i.e. you would have to install the required packages for this interpreter as well; for this case the installation of the prerequisite packages can also be done within the notebook in provided code fields).

Good luck and have fun!