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Shehata Dataset Documentation

Status: ✅ Complete and validated Pipeline: data/test/shehata/ → preprocessing/shehata/

Quick Start

For the complete Shehata dataset pipeline, see the authoritative documentation:

👉 data/test/shehata/README.md ← SSOT

That README contains: - Complete 2-step pipeline (Excel → CSV → fragments) - Dataset statistics (398 paired antibodies → 16 fragment types) - Data source information (Shehata et al. 2019 Cell Reports) - Execution instructions - Validation procedures

Current Documentation Structure

Active Reference Docs

Technical Reports: - threshold_calibration_discovery.md - PSR-specific threshold (0.5495) discovery

Methodology & Status: - shehata_data_sources.md - Data provenance and source verification - shehata_preprocessing_implementation_plan.md - Implementation methodology - shehata_phase2_completion_report.md - Phase 2 completion and validation

Historical Archive

Development logs (October-November 2025): - archive/shehata_conversion_verification_report.md - Phase 1 verification - archive/p0_blocker_first_principles_validation.md - Gap character validation - archive/shehata_blocker_analysis.md - P0 blocker analysis and resolution - archive/shehata_cleanup_plan.md - Dataset reorganization plan

⚠️ Note: Archived docs contain historical status warnings that do not reflect current state.

Shehata Dataset Summary

Source: Shehata et al. (2019) - Affinity maturation and antibody specificity study Size: 398 paired antibodies (VH + VL) Processed: 398 sequences (0 failures, 100% success rate) Labels: Binary (0=low PSR, 1=high PSR) - Highly imbalanced (391 low / 7 high) Assay: PSR (Poly-Specificity Reagent)

Publication: Cell Reports 28(13):3300-3308.e4 (2019) DOI: https://doi.org/10.1016/j.celrep.2019.08.056

Key Results

P0 Fix (2025-11-02)

  • Issue: Gap characters in VH/VL/Full fragment sequences
  • Affected: 13 VH, 4 VL, 17 Full sequences
  • Fix: Changed annotation.sequence_alignment_aa → annotation.sequence_aa
  • Result: All 398 sequences now gap-free and ESM-1v compatible

PSR-Specific Threshold Calibration (2025-11-03, Updated: 2025-11-18)

  • Discovery: Default threshold (0.5) gives 52.5% accuracy
  • Optimized: PSR-specific threshold (0.5495) gives 58.29% accuracy
  • Result: Near-parity with Novo Nordisk benchmark (58.8%, gap: -0.51pp)
  • Implementation: src/antibody_training_esm/core/classifier.py (ASSAY_THRESHOLDS + predict)
  • CLI: antibody-test auto-detects PSR from dataset name and applies 0.5495; override with --threshold if needed

✅ Near-perfect benchmark replication achieved (+5.79pp improvement from baseline)

Data: - Raw Excel: data/test/shehata/raw/shehata-mmc2.xlsx - Processed: data/test/shehata/processed/shehata.csv - Canonical: data/test/shehata/canonical/ (empty - dataset already balanced) - Fragments: data/test/shehata/fragments/*.csv (16 fragment types)

Scripts: - Step 1: preprocessing/shehata/step1_convert_excel_to_csv.py - Step 2: preprocessing/shehata/step2_extract_fragments.py

Tests: - Fragment validation: scripts/validation/validate_fragments.py - Threshold testing: test_assay_specific_thresholds.py

Fragment Types

The Shehata dataset generates 16 fragment files (paired antibodies):

Heavy Chain (8 files): - VH_only_shehata.csv - Full heavy chain variable domain - H-CDR1_shehata.csv, H-CDR2_shehata.csv, H-CDR3_shehata.csv - H-FWRs_shehata.csv - Concatenated H-FWR1+2+3+4 - H-CDRs_shehata.csv - Concatenated H-CDR1+2+3

Light Chain (6 files): - VL_only_shehata.csv - Full light chain variable domain - L-CDR1_shehata.csv, L-CDR2_shehata.csv, L-CDR3_shehata.csv - L-FWRs_shehata.csv - Concatenated L-FWR1+2+3+4 - L-CDRs_shehata.csv - Concatenated L-CDR1+2+3

Combined (2 files): - VH+VL_shehata.csv - Paired heavy + light chains - Full_shehata.csv - Complete antibody sequence

PSR Threshold

Key Finding: PSR assay requires different threshold than ELISA assay.

  • ELISA datasets (Jain, Boughter): Use default 0.5 threshold
  • PSR datasets (Shehata, Harvey): Use calibrated 0.5495 threshold

Rationale: PSR assay has different probability distributions than ELISA. The 0.5495 threshold achieves near-parity with Novo Nordisk benchmarks (Shehata: 58.29% vs Novo 58.8%, gap: -0.51pp).

Usage: 

from antibody_training_esm.core.classifier import predict

# Automatic PSR threshold selection
predictions = predict(sequences, assay_type='PSR')

See threshold_calibration_discovery.md for technical details.

References

  • Shehata et al. (2019): Affinity Maturation Enhances Antibody Specificity but Compromises Conformational Stability. Cell Reports 28(13):3300-3308.e4. DOI: 10.1016/j.celrep.2019.08.056
  • Sakhnini et al. (2025): Prediction of Antibody Non-Specificity using Protein Language Models. bioRxiv DOI: 10.1101/2025.04.28.650927

Last Updated: 2025-11-18 Status: ✅ Production ready - Near-parity with Novo benchmark achieved