In silico directed evolution of peptide binders with AlphaFold2
EvoBind designs peptide binders towards user-specified target residues using only sequence information. EvoBind accounts for adaptation of the receptor interface structure to the peptide design during optimization. This consideration of flexibility is crucial for binding.
AlphaFold2 is available under the Apache License, Version 2.0 and so is EvoBind, which is a derivative thereof.
The AlphaFold2 parameters are made available under the terms of the CC BY 4.0 license and have not been modified.
You may not use these files except in compliance with the licenses.
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https://colab.research.google.com/github/patrickbryant1/EvoBind/blob/master/EvoBind.ipynb
Before beginning the process of setting up this pipeline on your local system, make sure you have adequate computational resources. Make sure you have an available GPU as this will speed up the prediction process substantially compared to CPU optimization. EvoBind assumes you have NVIDIA GPUs on your system, readily available. A Linux-based system is assumed.
To setup this pipeline, clone this github repository:
git clone https://github.com/patrickbryant1/EvoBind.git
Then do
bash setup.sh
This script fetches the AlphaFold2 parameters, installs a conda env and downloads uniclust30_2018_08 which is used to generate the receptor MSA.
To design binders the following needs to be specified:
Target residues
Receptor CAs
Receptor fasta sequence
Peptide length
Peptide centre of mass relative to the receptor CAs
If you want to design a cyclic peptide, add the flag --cyclic_offset=1 in the design script when calling mc_design.py. Based on cyclic offset.
A test case is provided in design_local.sh.
This script can be run by simply doing:
bash design_local.sh