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) ```python 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 ```python 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. ```python 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. ```python 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. ```python 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. ```python 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