Skip to content

rescoring.md

Latest commit

 

History

History
170 lines (121 loc) · 6.47 KB

rescoring.md

File metadata and controls

170 lines (121 loc) · 6.47 KB

Rescoring Predictions from Other Methods

P2Rank can rescore pocket predictions from other binding site prediction tools, re-ranking their pockets using its own ML model.

Quick Start

prank rescore test_data/fpocket.ds                            # rescore fpocket predictions
prank rescore test_data/pocketeer.ds -o rescore_pocketeer     # rescore pocketeer, output to specific dir
prank eval-rescore test_data/fpocket.ds                       # rescore and evaluate against known ligands
prank fpocket-rescore test_data/basic.ds                      # run fpocket and rescore in one step
prank rescore test_data/pocketeer.ds -c rescore_2024          # use new experimental rescoring model

Commands

Command Description
prank rescore <dataset.ds> Rescore predictions and output re-ranked pockets.
prank eval-rescore <dataset.ds> Rescore and evaluate against known ligands.
prank fpocket-rescore <dataset.ds> Run Fpocket on proteins, then rescore.
Convenience shortcut that can be used as a drop-in replacement for prank predict.

Supported Methods

Method PREDICTION_METHOD Prediction column points to Links
Fpocket fpocket Fpocket output file (.pdb/.cif) GitHub, paper
Pocketeer pocketeer pockets.json file GitHub
PUResNetV2.0 puresnet Directory with *.pkt.pdb files GitHub, paper
ConCavity concavity *_pocket.pdb grid file project page, paper
SiteHound sitehound *_summary.dat file paper
DeepSite deepsite Results PDB file paper
MetaPocket2 metapocket2 PDB file with MPT residues paper
LISE lise PDB file with HETATM records paper
P2Rank p2rank *_predictions.csv file GitHub, paper

Dataset File Format

A dataset file (.ds) tells P2Rank which prediction method was used, and lists pairs of prediction output files and their corresponding protein structures.

# Lines starting with # are comments

PARAM.PREDICTION_METHOD=<method>

HEADER: prediction protein

path/to/prediction_output  path/to/protein.pdb

Required elements:

  • PARAM.PREDICTION_METHOD -- name of the prediction method (see table above)
  • HEADER: line -- defines column order (must include prediction and protein)
  • Data rows -- whitespace-separated paths (relative to the .ds file location)

The protein column should point to the structure that was used as input to the prediction tool. For eval-rescore, the protein must contain ligands (to compute evaluation metrics). For plain rescore, ligands are not needed.

The column order in HEADER: is flexible -- prediction protein or protein prediction are both valid.

Examples

Rescoring Fpocket predictions

my_fpocket.ds:

PARAM.PREDICTION_METHOD=fpocket

HEADER: prediction protein

fpocket_output/1abc_out/1abc_out.pdb  structures/1abc.pdb
fpocket_output/2xyz_out/2xyz_out.pdb  structures/2xyz.pdb

prank rescore my_fpocket.ds

Rescoring Pocketeer predictions

my_pocketeer.ds:

PARAM.PREDICTION_METHOD=pocketeer

HEADER: prediction protein

pocketeer_output/1abc/pockets.json  structures/1abc.pdb
pocketeer_output/2xyz/pockets.json  structures/2xyz.cif

prank rescore my_pocketeer.ds

Evaluating rescoring quality

Use eval-rescore with liganated proteins to compare the original ranking against the rescored ranking. This works with any supported method.

my_eval.ds:

PARAM.PREDICTION_METHOD=fpocket

HEADER: prediction protein

fpocket_output/1abc_out/1abc_out.pdb  liganated/1abc.pdb
fpocket_output/2xyz_out/2xyz_out.pdb  liganated/2xyz.pdb

prank eval-rescore my_eval.ds

This outputs evaluation metrics (DCA, DSO success rates, etc.) showing whether rescoring improved pocket ranking.

Output

For each protein, two files are generated in the output directory:

File Contents
{name}_rescored.csv Re-ranked pockets with new scores
{name}_predictions.csv Pocket details (scores, centers, residues, surface atoms)

The _rescored.csv contains columns:

Column Description
name Pocket name
score New score assigned by P2Rank
rank New rank (after rescoring)
old_rank Original rank from the prediction method

PyMOL visualization files are also generated by default (disable with -visualizations 0).

Parameters

Override parameters on the command line with -param value. A few commonly used parameters:

prank rescore dataset.ds -o output_dir -threads 4 -visualizations 0
Parameter Default Description
-o auto-generated Explicit output directory (overrides default)
-threads all CPUs Number of parallel threads
-visualizations true Generate PyMOL visualization files
-fail_fast false Stop on first error
-model default_rescore ML model to use for rescoring

Experimental Rescoring Model (rescore_2024)

An alternative rescoring model is available via -c rescore_2024. It uses a different feature set that does not depend on B-factor, making it suitable for AlphaFold models, NMR, and cryo-EM structures.

prank rescore fpocket.ds -c rescore_2024
prank fpocket-rescore test.ds -c rescore_2024
prank eval-rescore fpocket.ds -c rescore_2024

This model shows promising results but has not been fully evaluated yet.