Welcome to the Phosphosite Library!
We are first sharing this resource locally to get feedback and stimulate collaboration. Please:
- Give us feedback. Do we successfully predict known phospho-regulated interactions in your field? Are there interesting new predictions? All information will be held in confidence.
- Consider collaborating. Please contact us if you can contribute data that confirms one or more predictions.
- Cite this website and the eventual publication for any interactions discovered here.
- Do not share outside of your labs.
The Phosphosite Library
We have used a high-throughput structure prediction pipeline to identify new protein-protein interactions (PPIs) that are regulated, both positively or negatively, by phosphorylation (“p-PPIs”). We first mapped phosphorylation sites from mass spectrometry databases onto thousands of high-confidence PPIs that we previously predicted with AlphaFold-Multimer (Schmid and Walter, 2025, and unpublished). To identify phosphorylation events that inhibit PPIs, we screened for predicted PPIs where an interfacial phosphate in one protein resides within 6 Å of a negatively charged residue in the interacting protein.
To identify stimulatory interfacial phosphorylation events, we trained a machine learning model on p-PPIs in the PDB, yielding a Classifier of Phosphosite Interactions (COPI, unpublished). COPI evaluates predicted PPIs where an interfacial phosphate in one protein resides within 6 Å of a positively charged residue in the interacting protein and gives a score between 0 and 1. A COPI score of 0.6 or above recalls ~85% of known, stimulatory p-PPIs with an associated false discovery rate of 20% (unpublished).
The searchable table below compiles phosphosites from ~22,610 unique PPI predictions involving ~9,000 human proteins. Click on a pair to retrieve an interactive structure viewer and information about the putative regulatory kinase.
Filters
★ = experimentally validated
| Chain A / Chain B i |
phos_chain i |
phos_res i |
contact i |
SPOC i |
models i |
avg_PAE i |
avg_pLDDT i |
ht-lt_refs i |
Avg. COPI i |
kinase i |
source i |
|---|