whisper-ppi 0.1.1

Weak Heuristic Inference for Supervisory Protein intERaction mapping for PDB and AP-MS datasets

whisper

whisper-ppi is a Python package for scoring protein–protein interactions from proximity labeling and affinity purification mass spectrometry datasets.
It uses interpretable features, programmatic weak supervision, and decoy-based false discovery rate (FDR) estimation to identify high-confidence interactors.

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Install from PyPI:

pip install whisper-ppi

Install from GitHub:

git clone https://github.com/camlab-bioml/whisper
cd whisper
pip install .

Input Format

• A CSV file with:
  • One column named Protein
  • Other columns representing bait replicate intensities, named as BAIT_1, BAIT_2, etc.
• Control samples must be identifiable via substrings in their column names (e.g., "EGFP" or "Empty").

Usage

#protein-level
from whisper.protein_features import feature_engineering_protein
from whisper.protein_train import train_and_score_protein
import pandas as pd


# Load intensity table
intensity_df = pd.read_csv("input_intensity_dataset.tsv", sep="\t")

controls = ['EGFP', 'Empty', 'NminiTurbo']

# Run feature engineering
features_df = feature_engineering_protein(intensity_df, controls)

# You can save the features to use in the next step with different settings without generating them again.
features_df = pd.read_csv("features.csv")


# Run scoring and FDR estimation
scored_df = train_and_score_protein(features_df, initial_positives=15, initial_negatives=200)


#peptide-level
from whisper.peptide_features import feature_engineering_peptide
from whisper.peptide_train import train_and_score_peptide
import pandas as pd


# Load intensity table
intensity_df = pd.read_csv("input_intensity_dataset.tsv", sep="\t")

controls = ['EGFP', 'Empty', 'NminiTurbo']

# Run feature engineering
features_df = feature_engineering_peptide(intensity_df, controls)

# features_df = pd.read_csv("features.csv")


# Run scoring and FDR estimation
scored_df = train_and_score_peptide(features_df, initial_positives=15, initial_negatives=200)


#fragment-level
from whisper.fragment_features import feature_engineering_fragment
from whisper.fragment_train import train_and_score_fragment
import pandas as pd


# Load intensity table
intensity_df = pd.read_csv("input_intensity_dataset.tsv", sep="\t")

controls = ['EGFP', 'Empty', 'NminiTurbo']

# Run feature engineering
features_df = feature_engineering_fragment(intensity_df, controls)

# features_df = pd.read_csv("features.csv")


# Run scoring and FDR estimation
scored_df = train_and_score_fragment(features_df, initial_positives=15, initial_negatives=200)

Output

The final output includes:

• predicted_probability: Probability of each bait–prey interaction being real
• FDR: Estimated false discovery rate
• global_cv_flag: Flag for likely background preys based on variability across all samples

Tutorial

Read the full documentation

Citation

This software is authored by: Vesal Kasmaeifar, Kieran R Campbell

Lunenfeld-Tanenbaum Research Institute & University of Toronto