TEMPL Pipeline

What is TEMPL Pipeline?

TEMPL Pipeline predicts protein-ligand binding poses by borrowing geometry from related protein-ligand complexes instead of starting from an unconstrained docking search. It is a template-based method from Jozef Fulop, Martin Sicho, and Wim Dehaen for rapid pose prediction when similar ligands and binding sites are already represented in structural data.

The method is strongest in interpolative settings: close analogs, conserved pockets, lead-optimization series, and targets with useful protein-ligand templates. It is less suitable for novel scaffolds, allosteric pockets with little template coverage, and cases where binding depends on large receptor rearrangements.

How to use TEMPL Pipeline online

A TEMPL Pipeline online run accepts a protein structure as PDB, ENT, or RCSB PDB ID and one organic small-molecule ligand as SMILES, PubChem, drawing, or SDF. ProteinIQ searches for related protein-ligand templates, generates constrained ligand poses, and returns ranked SDF files with shape, color, and combo Tanimoto scores.

Inputs

For structures with missing atoms, alternate locations, or unusual residues, PDB Fixer can be used before running pose prediction.

Settings

Results

The main result files are:

File names may use the selected template PDB ID rather than the submitted target ID. That is expected for TEMPL outputs because the winning template determines the pose file prefix.

How TEMPL Pipeline works

TEMPL is a data-driven baseline for protein-ligand pose prediction. It searches a library of known protein-ligand complexes, identifies suitable templates, transfers ligand geometry into the target frame, and ranks generated poses by 3D similarity.

Template search

The pipeline uses protein embeddings to find structurally related protein-ligand templates. The released TEMPL dataset includes precomputed ESM-2 protein embeddings and processed ligand structures from PDBbind-scale complexes, which makes the search fast enough for interactive use.

Template quality matters more than template count. A close template from the same protein family with a related ligand can be more useful than many weakly related hits.

MCS-guided pose generation

After a template ligand is selected, TEMPL finds the maximal common substructure (MCS) between the submitted ligand and the template ligand. The shared atoms act as geometric anchors, and RDKit ETKDG conformer generation builds candidate 3D ligand conformations around that constraint.

This is why TEMPL is most useful for analog series. If the submitted ligand and template ligand share a clear scaffold, the MCS constraint gives the generated pose a strong geometric prior. If the ligand is a new chemotype with little shared substructure, the constraint becomes weaker or may fall back to less informative alignment.

Shape and pharmacophore scoring

Generated conformers are aligned to the template ligand and scored with RDKit shape alignment:

• Shape score: Agreement between the 3D molecular volumes.
• Color score: Pharmacophore-like feature agreement, such as donor, acceptor, aromatic, and hydrophobic feature overlap.
• Combo score: Average of the shape and color scores.

The top poses are not binding free-energy estimates. They are similarity-ranked hypotheses for where the ligand could sit if the selected template captures the relevant binding mode.

Interpreting results

Higher Tanimoto scores indicate closer agreement with the selected template. Scores near 1.0 suggest strong shape or feature overlap, while values closer to 0 indicate weak agreement. The useful threshold depends on ligand size, scaffold similarity, and whether the template ligand binds the same pocket.

Visual inspection is essential. A high-scoring TEMPL pose should still be checked for steric clashes, impossible protonation assumptions, and whether key interactions are plausible in the submitted protein structure. The *_pipeline_results.json file is useful when documenting which template and MCS mapping drove a result.

When to use TEMPL Pipeline vs docking tools

TEMPL is a good first choice when the ligand resembles known binders and the protein has nearby structural templates. It can be faster and more stable than unconstrained docking for analog pose transfer because it starts from experimental geometry.

For a lead series around a known chemotype, TEMPL can quickly test whether new analogs preserve a crystallographic binding mode. For chemically distant ligands or targets without useful templates, AutoDock Vina, GNINA, or DiffDock-L usually provide a better starting point.