weirdo 2.1.1

Metrics of immunological foreignness for candidate T-cell epitopes

WEIRDO

Widely Estimated Immunological Recognition and Detection of Outliers

A Python library for computing peptide foreignness scores—predicting whether a peptide sequence is likely from a pathogen (bacteria, virus) or from self (human, mammalian).

Overview

WEIRDO trains a multi-layer perceptron (MLP) on k-mer presence data from SwissProt to predict organism category membership. Given any peptide, it outputs:

• Category probabilities: likelihood of appearing in human, bacteria, viruses, mammals, etc.
• Foreignness score: max(pathogens) / (max(pathogens) + max(self))

Quick Start

from weirdo.scorers import SwissProtReference, MLPScorer

# Load reference data (SwissProt 8-mers with organism labels)
ref = SwissProtReference().load()

# Define organism categories
categories = [
    'archaea', 'bacteria', 'fungi', 'human', 'invertebrates',
    'mammals', 'plants', 'rodents', 'vertebrates', 'viruses'
]

# Get training data: each 8-mer labeled with organism presence
peptides, labels = ref.get_training_data(
    target_categories=categories,
    multi_label=True,
    max_samples=200000  # Optional: sample for faster training
)

# Train the MLP
scorer = MLPScorer(k=8, hidden_layer_sizes=(256, 128, 64))
scorer.train(peptides, labels, target_categories=categories, epochs=200)

# Score new peptides (any length)
df = scorer.predict_dataframe(['MTMDKSEL', 'SIINFEKL', 'NLVPMVATV'])
print(df)

Output:

    peptide  human  viruses  bacteria  mammals  ...  foreignness
   MTMDKSEL   0.82     0.12      0.08     0.79  ...        0.127
   SIINFEKL   0.15     0.73      0.21     0.18  ...        0.802
  NLVPMVATV   0.31     0.68      0.15     0.35  ...        0.660

Installation

pip install weirdo

Download reference data (~2.5 GB compressed / ~7.5 GB uncompressed) for training:

weirdo data download

Training Data

WEIRDO uses pre-computed 8-mer data from SwissProt (~100M unique k-mers):

Category	Description
human	Homo sapiens proteins
rodents	Mouse, rat proteins
mammals	Other mammals (dog, cow, primates, etc.)
vertebrates	Fish, birds, reptiles, amphibians
invertebrates	Insects, worms, mollusks
bacteria	Bacterial proteins
viruses	Viral proteins
archaea	Archaeal proteins
fungi	Fungal proteins
plants	Plant proteins

Each 8-mer has True/False labels for each category, indicating whether it appears in proteins from that organism group.

Feature Extraction

The MLP uses 592 features extracted from each peptide:

Amino Acid Properties (48 features)

12 physicochemical properties × 4 statistics (mean, std, min, max):

• Hydropathy, hydrophilicity
• Mass, volume
• Polarity, pK side chain
• Accessible surface area (folded/unfolded)
• Local flexibility, refractivity
• Solvent exposed area, % exposed residues

Structural Features (27 features)

• Secondary structure propensities (12): helix, sheet, turn × 4 stats
• Category fractions (9): positive/negative charged, hydrophobic, aromatic, aliphatic, polar, tiny, small, cysteine
• Charge features (4): net charge, charge transitions, max cluster, R/(R+K) ratio
• Disorder features (2): disorder/order promoting fractions

Composition Features (420 features)

• Amino acid frequencies (20): fraction of each amino acid
• Dipeptide frequencies (400): fraction of each amino acid pair

Sequence Statistics (12 features)

• Length, log-length, sqrt-length
• Unknown fraction, unique AA fraction
• Max run length, repeat fraction
• Entropy/complexity (entropy, effective AAs, Gini, top-2/maximum frequency)

Reduced Alphabet Frequencies (80 features)

• Composition across common reduced alphabets (Murphy, GBMR, SDM, etc.)

Dipeptide Summary (5 features)

• Entropy, Gini, max/top2 frequency, homodipeptide fraction

API Reference

Training

from weirdo.scorers import SwissProtReference, MLPScorer

# Load reference
ref = SwissProtReference().load()

# Get training data
peptides, labels = ref.get_training_data(
    target_categories=['human', 'viruses', 'bacteria', 'mammals'],
    multi_label=True,
    max_samples=100000  # Optional: limit for memory
)

# Train
scorer = MLPScorer(
    k=8,
    hidden_layer_sizes=(256, 128, 64),
    activation='relu',
    alpha=0.0001,  # L2 regularization
)
scorer.train(
    peptides, labels,
    target_categories=['human', 'viruses', 'bacteria', 'mammals'],
    epochs=200,
    learning_rate=0.001
)

Prediction

# Category probabilities (sigmoid-activated)
probs = scorer.predict_proba(['MTMDKSEL'])
# Shape: (1, n_categories)

# Foreignness score
foreign = scorer.foreignness(
    ['MTMDKSEL'],
    pathogen_categories=['bacteria', 'viruses'],
    self_categories=['human', 'mammals', 'rodents']
)
# Returns: max(pathogens) / (max(pathogens) + max(self))

# Full DataFrame output (handles variable-length peptides)
df = scorer.predict_dataframe(['MTMDKSEL', 'SIINFEKL', 'NLVPMVATV'])

Feature Extraction

# Extract features as DataFrame
df = scorer.features_dataframe(['MTMDKSEL', 'SIINFEKL'])
# Shape: (2, 593) - 592 features + peptide column

# Feature names
names = scorer.get_feature_names()
# ['hydropathy_mean', 'hydropathy_std', ..., 'dipep_YY']

Model Persistence

from weirdo import save_model, load_model, list_models

# Save trained model
save_model(scorer, 'my-foreignness-model')

# List saved models
for model in list_models():
    print(f"{model.name}: {model.scorer_type}")

# Load model
scorer = load_model('my-foreignness-model')

CLI

# Data management
weirdo data download        # Download SwissProt reference
weirdo data list            # Show data status

# Model management
weirdo models list          # List trained models
weirdo models train --data train.csv --name my-model
weirdo models info my-model # Show model details

# Scoring
weirdo score --model my-model MTMDKSEL SIINFEKL

Architecture

weirdo/
├── scorers/
│   ├── mlp.py          # MLPScorer with feature extraction
│   ├── swissprot.py    # SwissProtReference (training data)
│   ├── config.py       # Presets and configuration
│   ├── registry.py     # Scorer registry
│   └── trainable.py    # TrainableScorer base class
├── model_manager.py    # Save/load trained models
├── amino_acid_properties.py  # 12 AA property dictionaries
└── api.py              # High-level functions

Citation

@software{weirdo,
  title = {WEIRDO: Widely Estimated Immunological Recognition and Detection of Outliers},
  author = {PIRL-UNC},
  url = {https://github.com/pirl-unc/weirdo}
}

License

Apache License 2.0. See LICENSE for details.