Skip to content

Instantly share code, notes, and snippets.

@dehaenw
Created June 30, 2026 22:04
Show Gist options
  • Select an option

  • Save dehaenw/279136ca9cf08fb3d4d48af41ab25ef3 to your computer and use it in GitHub Desktop.

Select an option

Save dehaenw/279136ca9cf08fb3d4d48af41ab25ef3 to your computer and use it in GitHub Desktop.
Short submission report

PXR–Ligand Pose Prediction: Methods

Overview

Protein–ligand complexes were predicted for the pregnane X receptor (PXR) ligand-binding domain against a blind set of 185 ligands using AlphaFold 3 (AF3) in its standard configuration, followed by a confidence-based reranking step to select a single pose per ligand. No target-specific training, fine-tuning, or docking refinement was applied.

Input preparation

A single protein chain corresponding to the PXR ligand-binding domain (290 residues) was used for all predictions. A multiple sequence alignment was generated once with MMseqs2 (ColabFold protocol) and supplied to AF3 as a precomputed unpaired MSA (unpairedMsaPath); the paired MSA was left empty, as the system is a monomer, and no structural templates were provided. Each ligand was supplied as a SMILES string taken directly from the challenge input table, with one AF3 job generated per ligand (protein chain A, ligand chain B).

Structure prediction

Predictions were run with AlphaFold 3 using the official model weights and Docker image on a local GPU. The genetic-search data pipeline was disabled (--norun_data_pipeline) so that the precomputed MSA was used as-is. Each job used a single random seed (123) and produced the default five diffusion samples, giving five candidate complexes per ligand.

Model selection (reranking)

For each ligand, the five samples were ranked and a single best model retained. Rather than the global ranking_score — which, for a single small-molecule system, is dominated by the protein and is a weak proxy for pocket quality — samples were ranked by the protein–ligand interface ipTM, taken as the off-diagonal terms of the chain_pair_iptm matrix in each sample's summary_confidences.json. Ties were broken by the interface PAE, the corresponding off-diagonal of chain_pair_pae_min (lower is better). This selection targets confidence in the relative placement of the ligand against the protein, which is expected to track lDDT-PLI more closely than complex-global confidence.

Output and post-processing

The selected mmCIF for each ligand was converted to PDB format with gemmi. The ligand residue was renamed to LIG, and each file was named by its challenge structure identifier (<structure_id>.pdb). A per-ligand audit table recording the chosen seed/sample and its interface ipTM, interface PAE, and ranking score was retained alongside the final structures.

Notes and limitations

The pipeline is "vanilla" AF3 with a single seed and confidence-based pose selection; no consensus clustering, physics-based rescoring, or pose-validity filtering (e.g. PoseBusters) was applied. Confidence-based selection is least reliable for targets dissimilar to the training distribution, so per-ligand accuracy may vary with how well each chemotype is represented.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment