To compare protein liquid–liquid phase separation (LLPS) across species, combine three complementary layers rather than searching for one definitive catalog: PhaSePro for manually curated, experimentally verified driver proteins and regions; PhaSepDB for broader phase-separation records across organisms; and DisProt for disorder annotations. Then join records by stable identifiers such as UniProt accessions while preserving species, experimental role, sequence boundaries, and evidence provenance. A protein’s absence from one resource is not automatically evidence that it cannot phase-separate.
Why LLPS databases cannot be read as one universal list
Phase-separation resources answer different questions. One may count a protein once, another may count every experiment or publication, and a third may record a mapped sequence region rather than a whole protein. Their inclusion rules also differ: a manually verified database may exclude computational predictions, while a broader knowledgebase can include records with varied evidence and annotation depth.
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That means database totals are not prevalence estimates for all proteomes, and they are not interchangeable measures of biological frequency. Compare like with like—proteins with proteins, experiments with experiments, and regions with regions—before drawing conclusions.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThe most useful cross-species workflow keeps each row tied to its source database, organism or taxon, UniProt accession when available, biological role, sequence scope, publication, and evidence type. The result is a source-aware comparison rather than a misleading merged tally.
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The three resources and what each contributes
| Resource | Primary purpose | Current figures and version | How to use it |
|---|---|---|---|
| PhaSepDB | Broad knowledgebase of phase-separation-related proteins and records | 148 organisms, 1,849 proteins, 3,484 entries, 1,620 publications and 9,523 experiments; v3.0 release notice dated 2025-08-01 | Widen organism coverage, inspect annotations and trace records to experiments and publications |
| PhaSePro | Manually curated, experimentally verified LLPS driver proteins and regions | Version 1.1.0; 121 entries, 144 LLPS regions and 429 publications on the statistics page accessed 2026-10-13 | Start with high-confidence driver evidence and retrieve records by gene name, protein name or UniProt accession |
| DisProt | Curated intrinsic-disorder annotations, not an LLPS catalog | Version 9.10, release 2026_06, with 3,337 entries | Add disorder locations and descriptions as sequence context, never as a stand-alone LLPS verdict |
The PhaSepDB figures count different object types: an organism, protein, entry, publication and experiment are separate units. Likewise, PhaSePro’s entry and LLPS-region counts should not be compared directly with PhaSepDB’s protein or experiment totals. All of these numbers are time-stamped database snapshots and can change with later releases.
PhaSePro: the experimentally verified driver layer
PhaSePro describes itself as manually curated and based solely on experimentally verified LLPS cases. Its foundational publication says the authors collected proteins experimentally verified to drive phase separation in vivo and/or in vitro, while cases inferred from computational prediction or homology were not included. Treat that as a statement about PhaSePro’s inclusion policy, not as a claim that every listed protein behaves identically in every organism or cellular condition.
Each record is linked to a source species and supporting literature. The live statistics page reports 121 entries, 144 LLPS regions and 429 publications for version 1.1.0 at the stated access date. The historical 2019 paper contains older species-distribution and record figures; use the live page for current totals.
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PhaSepDB: broader context
PhaSepDB is useful when your question extends beyond the manually verified driver set—for example, when you need a wider list of phase-separation-related proteins, more organisms, or links between proteins, experiments and publications. Its 2025-08-01 v3.0 release notice reports 148 organisms and 1,849 proteins, alongside the larger entry, publication and experiment counts shown above.
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A PhaSepDB record can therefore broaden discovery, but you must inspect the annotation and evidence attached to each record before treating it as equivalent to a PhaSePro driver.
DisProt: disorder as a separate evidence layer
DisProt provides curated intrinsically disordered-region information. Disorder is often relevant to multivalent interactions and condensate formation, but an intrinsically disordered protein is not automatically an LLPS protein. Conversely, phase separation can involve ordered domains, folded interaction modules, or combinations of ordered and disordered segments. Use DisProt to annotate sequence context, not to replace experimental phase-separation evidence.
How do I find phase-separating proteins in different species?
- Define the comparison. Decide whether you need experimentally verified drivers, a broad phase-separation inventory, disorder annotations, or all three. Also decide whether your unit of analysis is a protein, an LLPS-driving region, an experiment or a publication.
- Choose a stable identifier. Use a UniProt accession whenever one is available. Gene names and common protein names can vary between species, isoforms and database releases. Keep the original name as a searchable alias, but do not use it as your sole join key.
- Search PhaSePro first for driver evidence. Its site supports searches by gene name, common or UniProt protein name, and UniProt accession. Search the accession when possible, and retain the source species or taxon attached to the result.
- Retrieve records in bulk when comparing many proteins. PhaSePro offers JSON, TSV and XML downloads. Its documented REST route accepts a valid UniProt accession; the documentation uses P35637 as an example. Store the returned accession, species, region coordinates, role, publication and evidence fields rather than copying only a display name.
- Expand coverage with PhaSepDB. Search the same accession and species context, then record the PhaSepDB entry identifier, annotation, role and linked experiments or publications. Do not silently upgrade a broad record to “experimentally verified driver” status.
- Add disorder information from DisProt. Match the protein accession where possible and capture the annotated disordered segments and the DisProt release used. Mark this as a disorder annotation, not as a phase-separation outcome.
- Inspect the confident LLPS datasets interface. Use its filters to examine intersections among source databases, driver/client/negative labels, disorder fractions and sequence context. Export CSV or XLSX when you need an auditable working file.
- Join without erasing species. Deduplicate technical repeats by a stable protein identifier, but retain one species field per source record. If orthologs are mapped into a family-level analysis, keep the original accession and organism in separate columns and document the orthology rule.
This sequence lets you begin with the strongest curated driver evidence, widen the search, and then add sequence features without confusing annotation categories.
What to compare in every record
| Comparison axis | Questions to record | Why it matters |
|---|---|---|
| Evidence and provenance | Was the observation in vivo, in vitro, or both? Which publication supports it? Is the record manually curated, high-throughput, or computationally inferred? | Two records with the same protein name can represent very different evidence strength. |
| Biological role | Is the protein a driver/scaffold, client, regulator, or negative/non-LLPS participant? | Role is functional and context-dependent; a client is not equivalent to a driver. |
| Sequence scope | Does the evidence apply to the full-length protein or a mapped LLPS-driving region? Are residue boundaries provided? | Region-level evidence should not be presented as proof that every segment of the full protein drives condensation. |
| Species and taxon | Which organism produced the tested protein, and which organism is attached to the database record? | Orthologs can differ in sequence, expression, partners and experimental behavior. |
| Database scope and unit | Is the number a protein, entry, experiment, region or publication? | Matching units prevents false rankings based on counting conventions. |
| Disorder context | Which residues are annotated as disordered, and by which release? | Disorder is informative context, not sufficient evidence for LLPS. |
PhaSePro’s record design and foundational description make the accession, species, literature and mapped-region details especially important. The integrated confident-dataset documentation also distinguishes exclusive and shared driver/client classifications, so preserve those labels rather than reducing them to a single “LLPS-positive” column.
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Driver, client and negative proteins are different biological categories
Drivers or scaffolds
A driver contributes interactions that can produce or sustain a condensed phase under the tested conditions. PhaSePro is centered on experimentally verified driver proteins and, where available, the regions implicated in that activity. A driver label still describes the assay and context reported by the source; it is not a guarantee of constitutive condensation in every cell or buffer.
Clients
A client partitions into a condensate formed by other components but may not be sufficient to create that phase on its own. The integrated datasets interface separates client and driver roles and can identify proteins that share both classifications. When comparing species, keep “client-only,” “driver-only” and shared labels distinct.
Negative or non-LLPS participants
Negative proteins are useful controls in benchmarking and classifier evaluation. Their presence in an integrated dataset does not mean they are biologically irrelevant; it means they were labeled negative for the particular LLPS question or benchmark. Record the source and definition of the negative label before using it to train or test a model.
How to compare species without collapsing real biological differences
Suppose the same UniProt accession family appears in records for two organisms. A safe comparison keeps separate rows for each species and includes:
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- the exact accession and isoform, if supplied;
- the organism and taxonomic identifier or name used by the source;
- the source database and release or access date;
- the experimental system, such as in vivo or in vitro;
- the driver, client, regulator or negative role;
- the full-protein or region-level sequence scope and residue boundaries;
- the supporting publication and any linked experiment identifier; and
- disorder annotations with their source release.
Only after preserving those fields should you create a family-level view. If two orthologs have different LLPS labels, treat that as a result requiring biological interpretation—not as a data error to be silently resolved. Differences may reflect sequence divergence, expression level, post-translational modification, binding partners, salt or crowding conditions, or simply unequal experimental coverage.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A reproducible data table for cross-species analysis
A practical master table can use one row per source record or one row per experimentally tested protein–condition combination. The following columns preserve the distinctions that are most often lost during merging:
- source_database and source_release_or_access_date
- uniprot_accession, gene_name and protein_name
- organism and taxon
- role and, where supplied, exclusive/shared driver-client classification
- evidence_system (in vivo, in vitro or both)
- sequence_scope (full protein or mapped region)
- region_start and region_end
- publication_identifier and experiment_identifier
- disprot_entry_or_region and disorder_release
- notes_on_conditions, including concentration, partners or other conditions when the source provides them
Keep the raw downloads unchanged and create a separate normalized table for joins. This makes it possible to trace a summary back to the original record when a release changes or when two sources disagree.
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Present in PhaSePro but absent from PhaSepDB
Check release dates, identifier spelling, isoform selection and species fields first. Different update schedules or record granularity can explain the mismatch. Do not assume one database is wrong without inspecting its inclusion rules and current release.
Present in PhaSepDB but absent from PhaSePro
This can be expected because PhaSepDB is broader and PhaSePro focuses on experimentally verified drivers. Read the PhaSepDB evidence and role annotations before deciding what the record establishes.
Present in DisProt but absent from both LLPS resources
That indicates a disorder annotation without a corresponding record in the LLPS resources you checked. It does not prove that the protein never phase-separates; it means the specific evidence is not represented in those datasets.
Different labels for orthologs
Preserve both labels and compare the tested construct, region, assay and organism. A homologous sequence does not transfer an experimental result automatically, which is why accession-linked, species-aware records are preferable to name-only merges.
Common analytical mistakes and their fixes
- Adding database totals together. Fix: report each count with its unit and release date.
- Calling every database hit a driver. Fix: retain driver, client, regulator and negative roles separately.
- Using disorder as an LLPS predictor by itself. Fix: pair disorder annotations with direct experimental evidence and other sequence features.
- Dropping species during deduplication. Fix: join on accession while retaining organism and taxon columns.
- Transferring a region result to the whole protein. Fix: preserve residue boundaries and state whether the assay used a fragment or full length.
- Treating absence as a negative result. Fix: distinguish “not represented in this resource or release” from an experimentally measured negative.
- Mixing historical and current statistics. Fix: label the 2019 foundational PhaSePro figures as historical and use the current statistics page for present totals.
- Reporting a snapshot as universal prevalence. Fix: describe counts as database inventories, not as the fraction of all species or proteomes that undergo LLPS.
A concise decision framework
- If you need the strongest curated driver evidence, begin with PhaSePro and inspect its supporting publications and mapped regions.
- If you need broad organism and record coverage, add PhaSepDB and retain its evidence and record units.
- If your question concerns sequence composition or disorder, add DisProt or the disorder fractions in the integrated confident datasets.
- If you are benchmarking a classifier, include the integrated driver, client and negative labels and document their definitions.
- If you are comparing species, preserve accession, organism, taxon, construct and assay fields before any orthology-level summary.
The resulting comparison is strongest when every biological claim can be traced to a particular source record, experiment, region and species. That approach gives you a useful cross-species view while respecting what each database actually measured.
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