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Binder recovery benchmark

This directory contains the code required to re-run our computational benchmark in which we aim to recover known binding partners from drug-induced protein complexes. To this end, we split 14 ligand-induced protein complexes into two subunits each and search against a database of these 28 proteins plus 200 decoys. Since the ligand can be considered as a part of each subunit, we have 28 benchmarking cases. The search can be highly parallelised and should only take a couple of minutes.

Summary of targets

  • 6QTL : Caffeine recognizing nanobody
  • 7DC8 : Switch Ab Fab and hIL6R in complex with ATP
  • 4DRI : FKBP51, Rapamycin and the FRB fragment of mTOR
  • 6OB5 : Computationally-designed, modular sense/response system (S3-2D)
  • 1A7X : FKBP12-FK1012 COMPLEX
  • 6ENG : 43K ATPase domain of Escherichia coli gyrase B in complex with an aminocoumarin
  • 6H0F : DDB1-CRBN-pomalidomide complex bound to IKZF1(ZF2)
  • 6SJ7 : human DDB1-DDA1-DCAF15 E3 ubiquitin ligase bound to RBM39 and Indisulam
  • 7TE8 : CA14-CBD-DB21 ternary complex
  • 1TCO : CALCINEURIN A FRAGMENT, CALCINEURIN B, FKBP12 AND THE IMMUNOSUPPRESSANT DRUG FK506 (TACROLIMUS) (Use chain C vs AB)
  • 1S9D: ARF1 IN COMPLEX WITH BREFELDIN A AND A SEC7 DOMAIN (Ignore GDP)
  • 4MDK : Cdc34-ubiquitin-CC0651 complex
  • 3QEL : NMDA receptor subunit GluN1 and GluN2B in complex with ifenprodil
  • 6N4N: Crystal structure of the designed protein DNCR2/danoprevir/NS3a complex

benchmark_pdbs.txt contains a summary of all benchmark complexes, a definition of the two subunits, and the relevant small molecule. The format is PDB ID, chain 1, chain 2, drug, SDF name. The drug is specified as <3_letter_code>_<chain>.

Each PDB can be fetched from RCSB together with the "Instance coordinates" file in SDF format (using the online interface or a command like this:

https://models.rcsb.org/v1/6qtl/ligand?auth_seq_id=201&label_asym_id=J&encoding=sdf&filename=6qtl_J_CFF.sdf

Decoys

We provide two sets of decoys which are taken from known PPIs without small molecules at the interface

Preparing the structures

Prior to the search, we need to preprocess all target complexes as well as the database proteins. This includes surface triangulation, feature calculation, MaSIF-site interface prediction and computation of MaSIF-search descriptors. Every subunit of the target complexes is processed individually and together with the drug. An already processed version of the dataset (including the smaller decoy set) is provided on Zenodo [Download]. For the larger decoy set we provide the input PDB files on Zenodo [Download]

Targets

A helper script generates all the required processing commands for the target complexes (to process each subunit with and without the small molecule).

python generate_preprocessing_commands.py benchmark_pdbs.txt <structure_dir> <processed_dir> preprocessing_commands.sh

<structure_dir> should contain all the PDBs and SDFs of the benchmark complexes.

All commands can be executed like this:

source preprocessing_commands.sh

The outputs will be written to <processed_dir>.

NOTE: Two benchmark complexes (7TE8/P0T_C and 6ENG/BHW_B) unfortunately required manual intervention because the automatic protonation and mol2 conversion produced inconsistent results. We include manually modified mol2 files in the mol2_files folder. These can be passed to the processing script using the the -m flag. We are happy to provide further instructions or the processed files upon request.

Decoys

We provide a script that parallelises the downloading and preprocessing of the decoy proteins using slurm:

sbatch pdbbind_decoys/prepare_decoys_parallel.slurm pdbbind_decoys/pdbbind_decoy_list.txt <decoy_dir>

All outputs will be saved in <decoy_dir>.

Running the search

To finally run a search, we first create a parameter file for each target (with ligand) in benchmark_pdbs.txt using:

python make_param_files.py <structure_dir> <processed_dir> <decoy_dir> <search_output_dir>

This script creates the output folder structure including parameter files in <search_output_dir>. The parameters can be changed by modifying the template in make_param_files.py and running it again.

The search & docking step can then be launched in parallel using the newly created parameter files, for example:

sbatch run_search.slurm <search_output_dir>/masif-neosurf-benchmark/with_ligand/search_params_targets

This step has to be repeated for all search_params_* folders (4 folders should have been created in the previous step). When all searches are completed, the hits and docked PDB structures can be found in <search_output_dir>. Some helper functions for further analysis are included in analysis_utils.py.