Protein solvent accessibility (using foldx)#

Type of objective function: discrete Environment to run this objective function: poli protein

About#

This objective function returns the solvent accesible surface area (SASA) of a wildtype and its mutations using foldx and biopython.

Note

If you are interested in computing both a protein’s stability and it’s SASA score, try foldx_stability_and_sasa instead! Import FoldXStabilityAndSASABlackBox instead of FoldXSASABlackBox.

Prerequisites#

  • Have foldx installed, and available in your home directory. We expect the following files to be there:

    • ~/foldx/foldx: the binary. You might need to rename it.

    • ~/foldx/rotabase.txt: a text file necessary for foldx to run (only if you are using v4 of foldx).

  • A (list of) wildtype_pdb_file: a (repaired) pdb file of the wildtype.

We can repair the file for you

By default, we will assume you are passing a repaired pdb file to us (indeed, we check if the filename contains _Repair). If you want us to repair the file for you and keep it in a cache, you can add the eager_repair=True keyword argument to the create method.

Otherwise, pre-repair your files using e.g.

~/foldx/foldx --command=RepairPDB --pdb your_file.pdb --water -CRYSTAL --pH 7.0

In our repairing process, we also remove heteroatoms using pdbtools.

How to run#

from pathlib import Path
    
from poli.objective_repository import FoldXSASAProblemFactory, FoldXSASABlackBox

wildtype_pdb_path = Path("path/to/your/wildtype_Repair.pdb")

# Creating the black box
f = FoldXSASABlackBox(wildtype_pdb_path=[wildtype_pdb_path])

# Creating a problem
problem = FoldXSASAProblemFactory().create(wildtype_pdb_path=[wildtype_pdb_path])
f, x0 = problem.black_box, problem.x0

# Example evaluation: SASA of your wildtype
print(f(x0))

How to cite#

If you use this black box, we expect you to cite the following resources:

[1] Schymkowitz, Joost, Jesper Borg, Francois Stricher, Robby Nys, Frederic Rousseau, and Luis Serrano. “The FoldX Web Server: An Online Force Field.” Nucleic Acids Research 33, no. Web Server issue (July 1, 2005): W382–88. https://doi.org/10.1093/nar/gki387.

[2] Stanton, Samuel, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, and Andrew Gordon Wilson. “Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders.” arXiv, July 12, 2022. http://arxiv.org/abs/2203.12742.

[3] González-Duque, M., Bartels, S., & Michael, R. (2024). poli: a libary of discrete sequence objectives [Computer software]. MachineLearningLifeScience/poli


@article{Schymkowitz:foldx:2005, title={The FoldX web server: an online force field},
  volume={33},
  ISSN={0305-1048},
  DOI={10.1093/nar/gki387},
  journal={Nucleic Acids Research},
    author={Schymkowitz, Joost and Borg, Jesper and Stricher, Francois and Nys, Robby and Rousseau, Frederic and Serrano, Luis},
    year={2005},
    month=jul,
    pages={W382–W388}
}

@article{stanton:LaMBO:2022,
  title   = {Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders},
  author  = {Stanton, Samuel and Maddox, Wesley and Gruver, Nate and Maffettone, Phillip and Delaney, Emily and Greenside, Peyton and Wilson, Andrew Gordon},
  journal = {arXiv preprint arXiv:2203.12742},
  year    = {2022}
}

@software{Gonzalez-Duque:poli:2024,
author = {González-Duque, Miguel and Bartels, Simon and Michael, Richard},
month = jan,
title = {{poli: a libary of discrete sequence objectives}},
url = {https://github.com/MachineLearningLifeScience/poli},
version = {0.0.1},
year = {2024}
}