P2PSigLip logo
P2PSigLip
Multi-species interaction atlas

Explore P2PSigLip Across Plants, Animals, And Fungi

P2PSigLip is a multi-species protein-interaction prioritisation resource. Start from a species, browse its curated catalog, search by gene or protein ID, inspect annotations, and prioritise candidate partners for downstream structural modelling or experimental validation.

Important: P2PSigLip scores are prioritisation scores primarily intended to rank candidate pairs within a query or candidate pool. They are not calibrated interaction probabilities and should not be used as a universal decision threshold.
Database Snapshot
Current Release Overview
Live from PostgreSQL
Datasets
-
Genes In Catalog
-
Prediction Rows
-
Alias Mappings
-
Loading database summary...
Loading citation and resource links...
Figure 5 Style Coverage Map
Cross-Species Release Coverage
Explore released datasets by taxonomy. Each species pill below links directly to its analysis workspace, so this coverage map also acts as a compact entry point to the resource.
Loading taxonomy overview...
Loading coverage figure...
Recommended Workflow
How To Use P2PSigLip
Ranking first, validation later
1. Start from one species
Choose a released dataset and search by gene ID, representative protein ID, or accepted alias.
2. Read the score as priority
Use the ranking to shortlist candidates for follow-up within a query, rather than as direct proof of physical interaction or a universal thresholded decision.
3. Add supporting evidence
Review annotations, homologs, and local network structure to refine biological interpretation.
4. Validate top candidates
Move the strongest candidates into structure-aware modelling, Y2H, Co-IP, BiFC, or related assays.
Interpretation rule: treat P2PSigLip output as a query-level prioritisation ranking. Higher scores indicate stronger prioritisation signal, not calibrated interaction probability or definitive physical-interaction evidence.
Documentation
Continue With Paper And Help
The homepage is the entry point to released datasets. For manuscript framing, score interpretation, and practical usage notes, continue into the two documentation pages below.
A smoother end to the homepage