GeoDock icon

GeoDock

(1.0.4)

Flexible protein-protein docking with a multi-track iterative transformer. Learn more

Input

Inputs

Settings

0 credits

Output

Configure inputs to begin

Set options on the left, then click “Submit job”.

What is GeoDock?

GeoDock predicts a protein–protein complex from two separate protein structures. ProteinIQ runs the Gray Lab release with its DIPS 0.3 checkpoint and offers the source tool’s OpenMM and PyRosetta refinement methods.

Inputs

Upload one structure for each partner, or fetch each by its RCSB PDB ID.

InputAccepted structuresLimit
Receptor (first protein partner)PDB, ENT, CIF, or mmCIF1000 protein residues
Ligand (second protein partner)PDB, ENT, CIF, or mmCIF1000 protein residues

The combined limit is 1200 protein residues. Both partners are proteins; the ligand slot is not for small molecules. For small-molecule docking, use GNINA.

GeoDock reads the first coordinate model and includes all protein chains in that model. Original input files remain available for download. Parsing, residue recognition, alternate-location selection, and missing-backbone handling follow the native tool. Missing backbone atoms are not repaired before prediction; prepare suitable structures for the biological question you want to test.

If you have sequences rather than structures, predict the partners first with ESMFold or AlphaFold 2.

Settings

SettingDefaultMeaning
RefinementOnRefine the predicted complex and retain the unrefined prediction as a separate download.
Refinement methodOpenMMChoose native OpenMM minimization or PyRosetta refinement.

The collapsed Refinement options section exposes the selected method’s controls:

MethodSettingDefaultMeaning
OpenMMRestraint stiffness10 kcal/mol/ŲPosition restraints on N, CA, C, and CB atoms. Zero disables their force.
OpenMMForce tolerance2.39 kcal/mol/nmPositive minimization convergence threshold, approximately 10 kJ/mol/nm.
PyRosettaMinimization iterations100Maximum iterations for each native minimization call.
PyRosettaCoordinate constraintsOnApply the native backbone coordinate constraints.
PyRosettaIdealize geometryOffRun the native idealization step before repacking.

OpenMM uses PDBFixer, the amber14 ff14SB force field, hydrogen addition, and CPU minimization. PyRosetta applies its native minimization and repacking procedure. Refinement can change atom content, coordinates, and PDB annotations; inspect both the prediction and the refined structure. Refinement may vary between runs and can take substantially longer than docking, particularly for large partners.

Results

GeoDock returns one predicted complex. The Viewer displays structure files; Data shows confidence summaries; Files provides the original inputs, results, and provenance. Logs shows the scientific run record.

FileContents
geodock_complex.pdbRefined complex when refinement is on; otherwise the unrefined prediction.
geodock_complex_unrefined.pdbPrediction before refinement, returned when refinement is on. Native per-residue pLDDT values are in its B-factor column.
receptor_input.*, ligand_input.*Original partner files in their submitted formats.
provenance.jsonSource revision, compatibility patch, model and dependency identities, settings, and input hashes.
run.logRun status, protein residue counts, completed phases, settings, and confidence summary.

Mean, minimum, and maximum pLDDT always describe the unrefined GeoDock prediction. They do not measure the quality of the refined coordinates. Use the unrefined PDB to inspect per-residue confidence: refinement can overwrite B-factors or change residue content.

The native pLDDT scale runs from 0 to 100. Higher values indicate higher model confidence, but a confident local structure does not establish that the proteins bind or that the interface is correct. Evaluate the interface against experimental evidence and other structural predictions.

The workflow output is the primary geodock_complex.pdb; reference structures, confidence-bearing raw output, provenance, and the Run log remain downloadable from the job.

Scientific method

GeoDock uses a multi-track iterative transformer to model residue features and pairwise geometry. The served checkpoint uses four recycling iterations. ProteinIQ preserves the native model output and optional refinement results rather than rebuilding their coordinates or scores.

For the method and its evaluation, see Flexible Protein–Protein Docking with a Multi-Track Iterative Transformer and the GeoDock source repository.

Table of contents

Related tools

AF2Dock

AF2Dock

AF2Dock adapts AlphaFold2-style co-folding for structure-based protein-protein docking. It docks receptor and ligand protein structures with flow-matching refinement and ranks sampled complexes by iPTM.

protein-dockingai-powered+5
DFMDock

DFMDock

DFMDock (Denoising Force Matching Dock) is a diffusion model that unifies sampling and ranking for protein-protein docking within a single framework. It predicts docked poses for protein-protein complexes from unbound structures using denoising score matching with optional clash force guidance.

protein-dockingai-powered+5
EquiDock

EquiDock

EquiDock is an SE(3)-equivariant graph neural network for rigid protein-protein docking. It predicts a binding pose for a protein-protein complex from unbound structures using geometric deep learning, with DIPS and DB5 pretrained checkpoints from the native release.

protein-dockingai-powered+5
ColabDock

ColabDock

ColabDock is a protein-protein docking framework that uses AlphaFold2 to predict complex structures guided by experimental restraints from cross-linking mass spectrometry, NMR, or other sources.

protein-dockinginteraction-prediction+4
HADDOCK3

HADDOCK3

HADDOCK (High Ambiguity Driven protein-protein DOCKing) is an integrative modeling platform for biomolecular complexes. It uses experimental data and bioinformatic predictions to guide the docking process, generating accurate protein-protein complex structures.

protein-dockinginteraction-prediction+4
LightDock

LightDock

LightDock is a protein-protein, protein-peptide, and protein-DNA docking framework using Glowworm Swarm Optimization (GSO). It predicts macromolecular binding modes and interfaces for biological complexes.

protein-dockinginteraction-prediction+4
ParaSurf

ParaSurf

ParaSurf is a state-of-the-art surface-based deep learning model for predicting interactions between antibodies and antigens. It identifies paratope binding sites on antibody structures with high accuracy across multiple benchmark datasets.

protein-analysisinteraction-prediction+4
SurfDock

SurfDock

SurfDock is a surface-informed diffusion generative model for protein-ligand docking, published in Nature Methods 2024. It leverages protein surface geometry to guide a diffusion process for reliable and accurate protein-ligand complex prediction.

protein-dockingstructure-prediction+5
DiffDock-L

DiffDock-L

DiffDock-L is a state-of-the-art molecular docking tool that uses diffusion models to predict how small molecule ligands bind to protein targets. It generates multiple binding poses with confidence scores.

protein-dockingblind-docking+5
DynamicBind

DynamicBind

DynamicBind is an AI-powered protein-ligand binding prediction tool that recovers ligand-induced conformational changes from unbound protein structures. It predicts both ligand binding poses and protein conformational changes.

protein-dockingblind-docking+5

Input

Inputs

Settings

0 credits

Output

Configure inputs to begin

Set options on the left, then click “Submit job”.