bio-immunoinformatics-epitope-prediction

Version Compatibility

Reference examples tested with: pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Epitope Prediction

"Predict B-cell and T-cell epitopes in my protein" → Identify immunogenic regions in antigens for vaccine design using sequence-based and structure-based prediction tools.

• Python: IEDB API for B-cell epitope prediction (BepiPred)
• Python: mhcflurry for T-cell epitope MHC binding prediction

B-Cell Epitope Prediction

Goal: Predict linear B-cell epitopes from protein sequence using IEDB prediction tools.

Approach: Submit sequence to IEDB B-cell prediction API with selectable method (BepiPred-2.0 recommended) and parse tab-separated results.

BepiPred-2.0 (Sequence-Based)

import requests

def predict_bcell_epitopes_iedb(sequence, method='bepipred2'):
    '''Predict B-cell epitopes using IEDB API

    Methods:
    - bepipred2: Deep learning (recommended)
    - bepipred: Original BepiPred
    - emini: Surface accessibility
    - kolaskar-tongaonkar: Antigenicity
    - parker: Hydrophilicity

    BepiPred-2.0 uses deep learning on crystal structures
    Threshold: >0.5 predicted as epitope (default)
    '''
    url = 'http://tools-cluster-interface.iedb.org/tools_api/bcell/'

    params = {
        'method': method,
        'sequence_text': sequence
    }

    response = requests.post(url, data=params)

    # Parse response (tab-separated)
    lines = response.text.strip().split('\n')
    header = lines[0].split('\t')
    data = [line.split('\t') for line in lines[1:]]

    return header, data

Parse BepiPred Results

import pandas as pd

def parse_bepipred_results(header, data, threshold=0.5):
    '''Parse BepiPred output and identify epitope regions

    Output columns:
    - Position: Amino acid position
    - Residue: Amino acid
    - Score: BepiPred score (higher = more likely epitope)

    Epitope threshold:
    - >0.5: Default, balanced sensitivity/specificity
    - >0.6: More stringent, fewer false positives
    - >0.4: More sensitive, more candidates
    '''
    df = pd.DataFrame(data, columns=header)
    df['Score'] = df['Score'].astype(float)
    df['Position'] = df['Position'].astype(int)

    # Identify epitope regions
    df['is_epitope'] = df['Score'] > threshold

    # Find continuous epitope regions
    epitopes = []
    current_epitope = []

    for _, row in df.iterrows():
        if row['is_epitope']:
            current_epitope.append(row)
        else:
            if len(current_epitope) >= 5:  # Minimum epitope length
                epitopes.append({
                    'start': current_epitope[0]['Position'],
                    'end': current_epitope[-1]['Position'],
                    'sequence': ''.join(r['Residue'] for r in current_epitope),
                    'avg_score': sum(r['Score'] for r in current_epitope) / len(current_epitope)
                })
            current_epitope = []

    return df, epitopes

T-Cell Epitope Prediction

Goal: Predict T-cell epitopes by MHC-I binding across multiple HLA alleles.

Approach: Query IEDB MHC-I API for each allele-sequence combination and aggregate predictions.

def predict_tcell_epitopes_iedb(sequence, alleles, method='recommended'):
    '''Predict T-cell epitopes using IEDB

    MHC-I methods:
    - recommended: Consensus of methods
    - netmhcpan_ba: NetMHCpan binding affinity
    - netmhcpan_el: NetMHCpan eluted ligand

    MHC-II methods:
    - recommended
    - netmhciipan
    '''
    url = 'http://tools-cluster-interface.iedb.org/tools_api/mhci/'

    results = []
    for allele in alleles:
        params = {
            'method': method,
            'sequence_text': sequence,
            'allele': allele,
            'length': '9'  # Most common for MHC-I
        }

        response = requests.post(url, data=params)
        # Parse results...

    return results

Linear vs Conformational Epitopes

Goal: Classify epitopes as linear (continuous) or conformational (discontinuous) and predict structure-based epitopes.

Approach: Distinguish by residue continuity in primary sequence; for conformational epitopes, use structure-based tools (DiscoTope, ElliPro) via web servers.

def classify_epitope_type(epitope_info):
    '''Classify epitope as linear or conformational

    Linear (continuous) epitopes:
    - Consecutive amino acids in primary sequence
    - ~10% of B-cell epitopes
    - Easier to predict from sequence

    Conformational (discontinuous) epitopes:
    - Non-consecutive residues brought together by folding
    - ~90% of B-cell epitopes
    - Requires structure for prediction
    '''
    pass


def predict_conformational_epitopes(pdb_file, chain='A'):
    '''Predict conformational B-cell epitopes from structure

    Uses surface accessibility and protrusion index.
    Requires 3D structure (PDB/mmCIF).

    Tools:
    - DiscoTope 2.0 (structure-based)
    - ElliPro (protrusion)
    - SEPPA 3.0
    '''
    # Structure-based prediction requires specialized tools
    # Usually accessed via web servers

    print('For conformational epitopes:')
    print('- DiscoTope: http://tools.iedb.org/discotope/')
    print('- ElliPro: http://tools.iedb.org/ellipro/')
    pass

Combine Multiple Predictions

Goal: Improve epitope prediction reliability by combining multiple methods into a consensus score.

Approach: Run each method independently, threshold per method, then count agreements per position and assign confidence levels.

def consensus_epitope_prediction(sequence, methods=['bepipred2', 'emini', 'parker']):
    '''Combine multiple prediction methods

    Consensus approach improves reliability:
    - Regions predicted by multiple methods more reliable
    - Different methods capture different properties

    Scoring:
    - 3/3 methods: High confidence
    - 2/3 methods: Moderate confidence
    - 1/3 methods: Low confidence
    '''
    all_results = {}

    for method in methods:
        header, data = predict_bcell_epitopes_iedb(sequence, method)
        df = pd.DataFrame(data, columns=header)
        all_results[method] = df

    # Combine scores
    consensus = all_results[methods[0]][['Position', 'Residue']].copy()

    for method in methods:
        threshold = 0.5 if method == 'bepipred2' else 0  # Method-specific thresholds
        all_results[method]['is_epitope'] = all_results[method]['Score'].astype(float) > threshold
        consensus[method] = all_results[method]['is_epitope'].astype(int)

    consensus['consensus_score'] = consensus[methods].sum(axis=1)
    consensus['confidence'] = consensus['consensus_score'].map({
        3: 'high', 2: 'moderate', 1: 'low', 0: 'none'
    })

    return consensus

Epitope Mapping from Experimental Data

Goal: Map epitope regions from overlapping peptide array binding data.

Approach: Process signal intensity values from overlapping peptide arrays and identify continuous high-signal regions as epitopes.

def map_epitopes_from_peptide_array(array_results, overlap=11):
    '''Map epitopes from peptide array experiments

    Peptide arrays test binding of overlapping peptides
    covering the entire antigen sequence.

    Args:
        array_results: Dict mapping peptide -> signal intensity
        overlap: Overlap between consecutive peptides

    Returns:
        Epitope map with per-residue scores
    '''
    # Implementation would process experimental binding data
    pass

Related Skills

• immunoinformatics/mhc-binding-prediction - T-cell epitope prediction
• immunoinformatics/immunogenicity-scoring - Epitope ranking
• structural-biology/geometric-analysis - Structure-based epitopes