EquiDock

Dock rigid protein-protein complexes using SE(3)-equivariant geometric modeling.

Input

Receptor (larger protein)

Ligand (smaller protein)

30 credits

Output

Configure inputs to begin

Set options on the left, then click “Submit job” — or start from an example.

DIPS docking

DB5 docking

Clash-reduced docking

What is EquiDock?

EquiDock predicts how two proteins bind together. Given the 3D structures of two unbound proteins, it outputs a docked complex showing their predicted binding pose.

The method uses SE(3)-equivariant graph neural networks, which means the predicted complex is always identical regardless of how the input structures are oriented. Rotate the inputs however you like, and the output remains consistent. This mathematical property makes predictions reliable and reproducible.

Developed at MIT and ETH Zurich, EquiDock was published as a spotlight paper at ICLR 2022. It runs 80-500x faster than traditional docking software by avoiding heavy sampling and refinement steps. The tradeoff is that it assumes rigid binding: neither protein changes shape upon contact.

How does EquiDock work?

EquiDock combines three techniques to predict binding poses:

Keypoint prediction: An attention mechanism identifies representative points on each protein that likely participate in binding. These keypoints approximate where the binding interface will form.

Optimal transport alignment: The model matches keypoints between the two proteins using optimal transport, finding correspondences between binding regions.

Differentiable Kabsch: Given matched keypoints, the algorithm computes the optimal rotation and translation to superimpose one protein onto the other, producing the final docked complex.

The neural network architecture extends Graph Matching Networks with E(3)-equivariant coordinate updates. Each protein is represented as a graph where nodes are residues and edges connect nearby residues. The network exchanges information both within each protein graph and between the two graphs to learn the binding transformation.

Confidence scoring

EquiDock computes confidence scores from keypoint alignment quality. Lower keypoint RMSD between predicted binding regions indicates higher confidence that the proteins dock well at that configuration.

How to use EquiDock online

ProteinIQ provides GPU-accelerated EquiDock predictions directly in the browser.

Inputs

Input	Description
`Receptor`	PDB file or RCSB PDB ID. Typically the larger protein. Max 1000 residues.
`Ligand`	PDB file or RCSB PDB ID. Typically the smaller protein. Max 1000 residues.

Both proteins should be unbound structures (not already in a complex). The tool expects standard ATOM records with backbone atoms (N, CA, C).

Settings

Setting	Description
`Model variant`	`DIPS` (default): trained on ~42,000 complexes for general use. `DB5`: optimized for the Docking Benchmark 5 dataset.
`Number of poses`	How many binding configurations to generate: `1` (fastest), `3`, or `5` (most thorough). Additional poses use random initial orientations.

Results

EquiDock returns predicted complex structures and quality metrics:

Output	Description
Complex PDB files	Receptor + ligand in predicted binding poses, ranked by confidence
Confidence score	Keypoint alignment quality (0-1, higher = better)
Contact map	Binary matrix showing residue-residue contacts at the interface
Interface residues	Lists of receptor and ligand residues within 8 Angstroms of each other

Interpreting confidence

Confidence	Interpretation
> 0.5	Good alignment, likely reasonable pose
0.3 - 0.5	Moderate, review the interface manually
< 0.3	Poor alignment, pose may be unreliable

Additional metrics help assess pose quality:

Min CA distance: Closest approach between receptor and ligand backbone atoms. Values under 3 Angstroms suggest clashes.
Interface contacts: Number of residue pairs within 10 Angstroms. More contacts generally indicate a larger binding interface.
Clash count: Residue pairs closer than 3 Angstroms. Should be zero or very low.

Limitations

EquiDock performs rigid docking only. Both proteins retain their input conformations. For proteins that undergo significant conformational changes upon binding, consider flexible docking methods.

The method predicts a single binding mode per pose. It does not model:

Conformational flexibility or induced fit
Allosteric effects
Multiple simultaneous binding sites

Predictions work best for proteins with clear, localized binding interfaces. Extended or diffuse interfaces may produce lower confidence scores.

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