https://github.com/giorginolab/evobind

In silico directed evolution of peptide binders with AlphaFold

https://github.com/giorginolab/evobind

Repository

In silico directed evolution of peptide binders with AlphaFold

https://github.com/giorginolab/EvoBind/blob/master/

# EvoBind
In silico directed evolution of peptide binders with [AlphaFold2](https://www.nature.com/articles/s41586-021-03819-2)
\
**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](http://www.apache.org/licenses/LICENSE-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](https://creativecommons.org/licenses/by/4.0/legalcode) and have not been modified.
\
**You may not use these files except in compliance with the licenses.**
\
EvoBind is also available as a **Colab notebook** here: https://colab.research.google.com/github/patrickbryant1/EvoBind/blob/master/EvoBind.ipynb

# Computational requirements
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.

# Setup
All needed packages are supplied through a Singularity image.
The only requirement for running MoLPC is therefore singularity, which can be installed by following: https://sylabs.io/guides/3.0/user-guide/quick_start.html
\
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](https://storage.googleapis.com/alphafold/alphafold_params_2021-07-14.tar), installs the singularity image and downloads [uniclust30_2018_08](http://wwwuser.gwdg.de/~compbiol/uniclust/2018_08/uniclust30_2018_08_hhsuite.tar.gz) which is used to generate the receptor MSA.

# Design binders
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**

A test case is provided in **design_local.sh**. \
This script can be run by simply doing:
```
bash design_local.sh
```

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