In silico directed evolution of peptide binders
EvoBind (v2) designs novel peptide binders based only on a protein target sequence. It is not necessary to specify any target residues within the protein sequence or the length of the binder (although this is possible). Cyclic binder design is also possible.
The probability of obtaining a binder with Kd≤μM affinity (according to SPR analysis) is 15% using only EvoBind2 and 46% if adversarial evaluation with AlphaFold-Multimer is included. Read more about it here
EvoBind2 accounts for adaptation of the receptor interface structure to the peptide being designed during optimisation. This consideration of flexibility is crucial for binding.
Receptor in green and peptide in blue.
EvoBind2 is based on AlphaFold2, which is available under the Apache License, Version 2.0 and so is EvoBind2, 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.
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 using a CPU. EvoBind2 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:
Receptor fasta sequence
Optional arguments:
Peptide length - default=10
Target residues within the raceptor sequence - default=all
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
If you use EvoBind in your research, please cite
- Li Q, Vlachos E.N., Bryant P. Design of linear and cyclic peptide binders of different lengths only from a protein target sequence. bioRxiv. 2024. p. 2024.06.20.599739. doi:10.1101/2024.06.20.599739
- Bryant P, Elofsson A. EvoBind: in silico directed evolution of peptide binders with AlphaFold. bioRxiv. 2022. p. 2022.07.23.501214. doi:10.1101/2022.07.23.501214
EvoBind (v1) is available here
It is possible to run EvoBind2 online in the colab here
Set TARGET_RESIDUES to all residues in your protein target to obtain an untargeted design protocol.