Datasets:

ChatterjeeLab
/

SNOOPPI

configs:
  - config_name: default
    data_files:
      - split: positive
        path: data/positive-*
      - split: negative
        path: data/negative-*
      - split: unknown
        path: data/unknown-*
dataset_info:
  features:
    - name: SNOOPPI_final_label
      dtype: string
    - name: MI0915_final_label
      dtype: string
    - name: SNOOPPI_Partner_A_ID
      dtype: string
    - name: SNOOPPI_Partner_B_ID
      dtype: string
    - name: SNOOPPI_Pair_ID
      dtype: string
    - name: SNOOPPI_Labeled_Pair_ID
      dtype: string
    - name: seq_sort
      dtype: string
    - name: unique_interaction_mi_terms
      dtype: string
    - name: partner_A_sequence
      dtype: string
    - name: partner_B_sequence
      dtype: string
    - name: partner_A_length
      dtype: string
    - name: partner_B_length
      dtype: string
    - name: partner_A_mol_type
      dtype: string
    - name: partner_B_mol_type
      dtype: string
    - name: intact_interaction_ids
      dtype: string
    - name: n_intact_interaction_ids
      dtype: string
    - name: partner_A_intact_interactor_ids
      dtype: string
    - name: partner_B_intact_interactor_ids
      dtype: string
    - name: partner_A_n_intact_interactor_ids
      dtype: string
    - name: partner_B_n_intact_interactor_ids
      dtype: string
    - name: partner_A_uniprot_ids
      dtype: string
    - name: partner_B_uniprot_ids
      dtype: string
    - name: partner_A_gene_names
      dtype: string
    - name: partner_B_gene_names
      dtype: string
    - name: partner_A_species
      dtype: string
    - name: partner_B_species
      dtype: string
    - name: mi_scores_all
      dtype: string
    - name: mi_score_mean
      dtype: string
    - name: mi_score_min
      dtype: string
    - name: mi_score_max
      dtype: string
    - name: assays_unique
      dtype: string
    - name: n_assays_unique
      dtype: string
    - name: n_papers
      dtype: string
    - name: pubmed_ids
      dtype: string
    - name: publication_years_all
      dtype: string
    - name: publication_year_min
      dtype: string
    - name: publication_year_max
      dtype: string
    - name: feature_relationship_types
      dtype: string
    - name: feature_relationship_summary
      dtype: string
    - name: has_mutation_relationship
      dtype: string
    - name: has_ptm_relationship
      dtype: string
    - name: has_binding_site_relationship
      dtype: string
    - name: partner_A_wildtype_sequence_if_feature_modified
      dtype: string
    - name: partner_B_wildtype_sequence_if_feature_modified
      dtype: string
    - name: wildtype_seq_sort_if_feature_modified
      dtype: string
    - name: wildtype_SNOOPPI_final_label
      dtype: string
    - name: wildtype_MI0915_final_label
      dtype: string
    - name: exact_sequence_pair_intact_label_summary
      dtype: string
    - name: snooppi_label_transition_from_intact
      dtype: string
    - name: seq_sort_origin_categories
      dtype: string
    - name: unique_expansions
      dtype: string
    - name: partner_A_go_terms
      dtype: string
    - name: partner_B_go_terms
      dtype: string
    - name: partner_A_dip
      dtype: string
    - name: partner_B_dip
      dtype: string
    - name: partner_A_ensg
      dtype: string
    - name: partner_B_ensg
      dtype: string
    - name: partner_A_ense
      dtype: string
    - name: partner_B_ense
      dtype: string
    - name: partner_A_enst
      dtype: string
    - name: partner_B_enst
      dtype: string
    - name: partner_A_interpro
      dtype: string
    - name: partner_B_interpro
      dtype: string
    - name: partner_A_rcsb_pdb
      dtype: string
    - name: partner_B_rcsb_pdb
      dtype: string
    - name: partner_A_reactome
      dtype: string
    - name: partner_B_reactome
      dtype: string
    - name: partner_A_host_context
      dtype: string
    - name: partner_B_host_context
      dtype: string
    - name: partner_A_sequence_snooppi_degree_by_label
      dtype: string
    - name: partner_B_sequence_snooppi_degree_by_label
      dtype: string
    - name: partner_A_sequence_modified_role_counts
      dtype: string
    - name: partner_B_sequence_modified_role_counts
      dtype: string
    - name: partner_A_sequence_partner_modified_role_counts
      dtype: string
    - name: partner_B_sequence_partner_modified_role_counts
      dtype: string
    - name: partner_A_gene_snooppi_degree_by_label
      dtype: string
    - name: partner_B_gene_snooppi_degree_by_label
      dtype: string
    - name: partner_A_gene_modified_role_counts
      dtype: string
    - name: partner_B_gene_modified_role_counts
      dtype: string
    - name: partner_A_gene_partner_modified_role_counts
      dtype: string
    - name: partner_B_gene_partner_modified_role_counts
      dtype: string
  splits:
    - name: positive
      num_bytes: 153392772
      num_examples: 33625
    - name: negative
      num_bytes: 34725795
      num_examples: 5378
    - name: unknown
      num_bytes: 4633269465
      num_examples: 835083
  download_size: 1457344515
  dataset_size: 4821388032

SNOOPPI: Sequence-Normalized Database of On- and Off-Target Protein-Protein Interactions

SNOOPPI is a curated protein–protein interaction (PPI) dataset containing positive, negative, and unknown interaction annotations.

SNOOPPI is sequence-indexed, meaning one unique SNOOPPI PPI or non-PPI indicates a unique combination of protein sequences.
The dataset preserves protein sequences, identifiers, species information, PSI-MI interaction terms, experimental evidence, publications, and feature relationships where available.

Dataset splits

SNOOPPI is distributed in three splits:

Split	Description
`positive`	Protein pairs retained as positive interactions under the SNOOPPI annotation criteria
`negative`	Protein pairs retained as negative interactions under the SNOOPPI annotation criteria
`unknown`	Protein pairs present in IntAct's source interaction records but not assigned a definitive positive or negative label under the SNOOPPI criteria

The unknown split should not automatically be interpreted as a negative or as a set of experimentally tested non-interactions. These entries represent interactions for which the available evidence was insufficient, conflicting, or otherwise unresolved under the annotation framework.

Installation

Install the Hugging Face datasets package:

pip install -U datasets

Load the complete dataset

from datasets import load_dataset

snooppi = load_dataset("ChatterjeeLab/SNOOPPI")

print(snooppi)

This returns a DatasetDict containing the positive, negative, and unknown splits.

Access an individual split from the resulting object:

snooppi_positive = snooppi["positive"]
snooppi_negative = snooppi["negative"]
snooppi_unknown = snooppi["unknown"]

Load one split directly

Load only the positive interactions:

from datasets import load_dataset

snooppi_positive = load_dataset(
    "ChatterjeeLab/SNOOPPI",
    split="positive",
)

Load only the negative interactions:

snooppi_negative = load_dataset(
    "ChatterjeeLab/SNOOPPI",
    split="negative",
)

Load only the unknown interactions:

snooppi_unknown = load_dataset(
    "ChatterjeeLab/SNOOPPI",
    split="unknown",
)

Convert a split to pandas

from datasets import load_dataset

snooppi_pos = load_dataset(
    "ChatterjeeLab/SNOOPPI",
    split="positive",
).to_pandas()

Converting a complete split to pandas loads that split into memory. Users working in memory-constrained environments may prefer to filter the Hugging Face Dataset before calling .to_pandas().

Example queries

Retrieve direct human positive interactions

The PSI-MI term MI:0407 denotes a direct interaction. The NCBI taxonomy identifier for humans is 9606.

from datasets import load_dataset

snooppi_pos = load_dataset(
    "ChatterjeeLab/SNOOPPI",
    split="positive",
).to_pandas()

direct_human_positive = snooppi_pos.loc[
    snooppi_pos[
        "unique_interaction_mi_terms"
    ].fillna("").str.contains(
        "MI:0407",
        regex=False,
    )
    & snooppi_pos[
        "partner_A_species"
    ].fillna("").str.contains(
        "9606",
        regex=False,
    )
    & snooppi_pos[
        "partner_B_species"
    ].fillna("").str.contains(
        "9606",
        regex=False,
    )
].copy()

print(
    f"Direct human positive interactions: "
    f"{len(direct_human_positive):,}"
)

Find all positive interactions involving a UniProt accession

from datasets import load_dataset

snooppi_pos = load_dataset(
    "ChatterjeeLab/SNOOPPI",
    split="positive",
).to_pandas()

uniprot_accession = "P00734"

uniprot_ppis = snooppi_pos.loc[
    snooppi_pos[
        "partner_A_uniprot_ids"
    ].fillna("").str.contains(
        uniprot_accession,
        regex=False,
    )
    | snooppi_pos[
        "partner_B_uniprot_ids"
    ].fillna("").str.contains(
        uniprot_accession,
        regex=False,
    )
].copy()

print(
    f"Positive interactions involving {uniprot_accession}: "
    f"{len(uniprot_ppis):,}"
)

Notes on identifier searches

Identifier fields may contain multiple source identifiers or annotations within one string. The examples therefore use literal substring matching with regex=False.

For analyses requiring exact identifier membership, users should parse the relevant identifier field according to its delimiter structure before testing membership.

Intended use

SNOOPPI may support:

Evaluation of protein–protein interaction prediction methods
Analysis of positive and negative interaction evidence
Study of sequence and feature relationships between interacting proteins
Retrieval of interactions by UniProt accession, species, or PSI-MI term

Users should preserve the distinction among positive, negative, and unknown annotations when constructing evaluation or training datasets.

Citation

If you find SNOOPPI useful in your work, please consider citing our paper.

@article{10.1093/bioinformatics/btag736,
    author = {Vincoff, Sophia and Chatterjee, Pranam},
    title = {SNOOPPI: a sequence-normalized database of on- and off-target protein–protein interactions},
    journal = {Bioinformatics},
    volume = {42},
    number = {10},
    pages = {btag736},
    year = {2026},
    month = {10},
    issn = {1367-4811},
    doi = {10.1093/bioinformatics/btag736},
    url = {https://doi.org/10.1093/bioinformatics/btag736},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/42/10/btag736/71611875/btag736.pdf},
}