Click to upload (.pdb, .ent, .cif, .mmcif, .pdbx)
SuperWater predicts ordered water molecule positions around protein structures. It uses a generative model to sample candidate hydration sites, then filters and clusters those candidates to recover likely crystallographic and interface waters from a single structure.
Published in Communications Chemistry in December 2025, SuperWater was introduced as a score-based diffusion framework with equivariant graph neural networks and ESM-derived features. In the reported benchmarks, it outperformed HydraProt and GalaxyWater-CNN across much of the precision-coverage range for protein surface waters as well as protein-protein and protein-ligand interface waters.
ProteinIQ provides browser-based access to SuperWater on hosted compute, so hydration-site prediction can be run from an uploaded structure or an RCSB entry without local setup.
| Input | Description |
|---|---|
Protein Structure | One protein structure in PDB, ENT, CIF, mmCIF, or PDBx format, or a structure fetched from RCSB. The file must contain protein atoms. Maximum file size is 50 MB. |
| Setting | Description |
|---|---|
Water ratio | Approximate number of sampled water candidates per residue. Range 1-10, default 8. Higher values increase coverage but also runtime and memory use. |
Confidence cutoff | Minimum confidence score required to keep a predicted hydration site. Range 0.02-0.5, default 0.1. Higher values improve precision but return fewer waters. |
Inference steps | Reverse-diffusion steps used during sampling. 10 is faster, 20 is the default, and 30 may improve quality at the cost of runtime. |
ProteinIQ returns a 3D viewer, a tabular summary, and downloadable result files.
| Output | Description |
|---|---|
Viewer | Interactive structure view with predicted water coordinates included in the returned files. |
Data table | Summary table for the processed structure and prediction settings. |
Files | Downloadable structure and result files, including the combined structure with predicted waters and auxiliary confidence outputs. |
| Column | Description |
|---|---|
Structure | Input structure name or identifier |
Predicted waters | Number of hydration sites retained after confidence filtering and clustering |
Sampled candidates | Number of candidate positions generated before final filtering |
Max confidence | Highest confidence assigned to any retained water site |
Mean confidence | Average confidence across retained water sites |
Water ratio | Water sampling multiplier used for the run |
Confidence cutoff | Acceptance threshold used to keep predicted waters |
Input source |
SuperWater does not score a fixed 3D voxel grid the way earlier hydration predictors do. Instead, it learns the gradient of the water-position distribution around a protein and uses that learned score field to refine randomly initialized water coordinates through reverse diffusion.
The architecture reported in the paper combines a score-based diffusion model with equivariant graph neural networks, which preserves geometric consistency under rotation. The published method also incorporates ESM features to encode sequence-derived context around residues. After sampling, a separate confidence model removes low-probability positions, and a clustering step consolidates nearby candidates into final hydration sites.
The benchmark protocol described in the paper sampled an initial number of candidates proportional to protein length, then traced different precision-coverage tradeoffs by varying the internal confidence threshold (cap). On an independent test set of 1,709 crystal structures, SuperWater defined the best overall precision-coverage frontier among the compared methods across many operating points.
SuperWater predictions represent likely ordered water sites, not every transient solvent molecule around the protein. High-confidence predictions are more likely to correspond to persistent hydration sites that recur in experimental structures or stabilize interfaces.
| Confidence pattern | Interpretation |
|---|---|
High Max confidence with moderate-to-high Predicted waters | Strong evidence for a structured hydration pattern around the input fold or interface |
Low Confidence cutoff and many returned waters | Broader coverage, useful for exploratory analysis but more likely to include false positives |
Higher Confidence cutoff and fewer returned waters | More conservative set of hydration sites, typically better for prioritizing inspection |
Large gap between Sampled candidates and Predicted waters | Many sampled positions were rejected during filtering, indicating a stricter or more selective final result |
The paper reports mean absolute deviation of approximately 0.3 ± 0.06 Å at cap = 0.5 for matched predictions, which indicates that true-positive sites can be placed with sub-angstrom accuracy. That figure should be interpreted as benchmarked localization accuracy against crystallographic waters, not as a guarantee for every structure.
SuperWater predicts positions from a static protein structure. It does not explicitly model long-timescale solvent dynamics, alternate conformations, protonation-state uncertainty, or experimental conditions such as crystal packing, buffer composition, or ligand occupancy.
Like other hydration-site predictors, it is biased toward ordered waters that are recoverable from structural datasets. Disordered, low-occupancy, or rapidly exchanging solvent molecules may be absent from the output even when they are biologically relevant.
Prediction counts also depend directly on Water ratio and Confidence cutoff. A denser sampling strategy can improve recall, but it does not by itself validate the biological importance of every returned site.
| Whether the structure came from file upload or RCSB fetch |
