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IEDB_pHLA_binding_data

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metadata 
license: cc-by-4.0
task_categories:
  - other
tags:
  - biology
  - protein-structure
  - MHC
  - HLA
  - peptide
  - binding-affinity
  - rosetta
  - immunology
pretty_name: pHLA Binding Affinity with Rosetta FlexPepDock Structures
size_categories:
  - 10K<n<100K

pHLA Binding Affinity with Rosetta FlexPepDock Structures

Author: K. Hunter, Vanderbilt University Medical Center
License: CC BY 4.0
Metadata source: Immune Epitope Database (IEDB)

Dataset Summary

This dataset combines experimentally measured peptide–HLA (pHLA) binding affinities from the IEDB with computationally generated 3D structures produced using Rosetta FlexPepDock. It covers 37 HLA class I alleles (HLA-A and HLA-B), 14,077 unique peptides, and 49,490 peptide–allele pairs with associated docked structures.

Each peptide–allele pair with a recorded binding measurement (Kd or IC50) is linked to an ensemble of 25 Rosetta FlexPepDock decoy structures, along with summary Rosetta energy statistics.

Intended uses include:

  • Training and benchmarking machine learning models for pHLA binding affinity prediction
  • Evaluating computational docking and scoring methods against experimental binding data

Dataset Structure 

pHLA_binding/
├── README.md
├── metadata.csv                    # Master table — one row per IEDB measurement
└── structures/
    ├── A0101/
    │   ├── FHEFLSSKL.silent
    │   ├── GILGFVFTL.silent
    │   └── ...
    ├── A0201/
    └── ...

Each silent file contains the full 25-decoy ensemble for a single peptide–allele pair, including embedded Rosetta energy scores. Allele directory names use filesystem-safe notation (e.g. A0101/ for HLA-A*01:01), and silent files are named after the peptide sequence. The allele column in metadata.csv contains the standard notation (A*01:01).

Metadata Fields

Column Description
allele_iedb Allele name in IEDB format (e.g. HLA-A_01_01)
allele Standard allele notation (e.g. A*01:01)
peptide Peptide amino acid sequence
peptide_length Length of peptide in residues
measurement_type IC50 or Kd
measurement_value Quantitative binding measurement
measurement_units Units of measurement (nM)
assay_method Assay method as reported in IEDB
assay_response Full assay response description from IEDB
pubmed_id PubMed ID of the source publication
parent_protein Source protein of the peptide
protein_accession UniProt/GenBank accession of source protein
source_organism Organism of origin
assay_pdb_id Experimental PDB ID from IEDB (if available)
flagged Whether entry was flagged during IEDB data cleaning
num_decoys Number of decoy structures in the silent file (typically 25)
rosetta_best_score Minimum total_score across 25 decoys (REU), extracted from silent file
rosetta_mean_score Mean total_score across 25 decoys (REU), extracted from silent file
pdb_dir Relative path to the peptide silent file within the dataset

Dataset Statistics

Metric Count
Total measurements 49,924
Unique alleles 37
Unique peptides 14,077
IC50 measurements 13,942
Kd measurements 35,982
Flagged entries 16
Total silent files 49,924
Total decoy structures 1,237,250

IEDB Data Curation

Binding affinity data were retrieved from a local copy of the IEDB bulk download (mhc_ligand_full.csv) and processed using a custom Python pipeline (IEDBTestPipeline.py). The curation procedure is described below.

1. Retrieval and Assay Filtering

The full IEDB MHC ligand table was read in chunks and filtered to retain only entries belonging to HLA-A or HLA-B alleles with quantitative binding measurements. Rows were retained only if they reported one of two assay response types:

  • Half maximal inhibitory concentration (IC50)
  • Dissociation constant (Kd)

Variant labels in the IEDB (e.g., dissociation constant KD (~EC50), dissociation constant KD (~IC50)) were normalized to a single canonical label (dissociation constant (KD)) prior to filtering. Entries missing either a quantitative measurement value or assay units were dropped.

2. Per-Allele Deduplication

After filtering, records were grouped by allele. Within each allele, duplicate entries for the same epitope (identified by IEDB Epitope IRI) were resolved separately for IC50 and Kd measurements using the following decision rules, applied in order:

Scenario Action
Two entries, one lacks a PubMed ID Retain the entry with a PubMed ID; drop the other
Two entries, both lack PubMed IDs, values differ by ≤10 nM Retain one entry (first occurrence)
Two entries, both lack PubMed IDs, values differ by >10 nM Drop both entries
Two entries, both have PubMed IDs, values differ by <10 nM Retain one entry (first occurrence)
Two entries, both have PubMed IDs, values conflict (≥10 nM difference) Flag both for manual review
More than two entries Drop entries lacking PubMed IDs first; if >2 remain, retain the entry closest to the median measurement value
Any other ambiguous case Flag for manual review

Entries that could not be unambiguously resolved were written to a separate flagged_IEDB_data.csv file for each allele and excluded from the cleaned dataset. These correspond to the flagged = True entries in metadata.csv.

3. Sequence-Level Filtering

After deduplication, peptide sequences containing the + character (used by IEDB to denote non-canonical or modified amino acids) were excluded from the final FASTA output and downstream structure generation.

4. Outputs per Allele

For each allele, the pipeline produced:

  • {allele}_cleaned_IEDB_data.csv — deduplicated, unflagged entries retained for structure modeling
  • {allele}_flagged_IEDB_data.csv — ambiguous entries excluded from modeling but retained for reference
  • {allele}_IEDB_data.fasta — peptide sequences in FASTA format for input to Rosetta FlexPepDock

These per-allele files were generated in filesystem-safe allele directories (e.g., HLA-A_01_01/ for HLA-A*01:01) within the working directory at the time of pipeline execution.

5. Manual Review Checkpoint

By default, the pipeline pauses after generating per-allele cleaned and flagged CSVs to allow manual literature review of flagged entries before structure generation proceeds. In this dataset, flagged entries were reviewed and those confirmed as erroneous or unresolvable were excluded. The --skipReview flag bypasses this checkpoint when manual review is not required.

Computational Methods

3D structures were generated using Rosetta FlexPepDock with 25 decoys per peptide–allele pair. Input structures were prepared by threading the peptide sequence of interest onto a template pHLA structure and performing flexible peptide docking.

All decoy structures and their associated Rosetta energy scores are stored in per-peptide Rosetta silent files (structures/{allele}/{peptide}.silent). Scores are reported as total_score (Rosetta Energy Units, REU) and are embedded directly in the silent file alongside the decoy coordinates. Summary statistics (min, mean) across the 25-decoy ensemble are precomputed and included in metadata.csv. Individual decoy structures can be extracted from silent files using extract_pdbs from the Rosetta toolkit.

Known Limitations and Caveats

Binding measurement caveats:

  • IC50 values show large spikes at 20,000 nM and 70,000 nM, consistent with assay detection limits. These values should be treated as censored (i.e. greater than the reported value) rather than exact measurements.
  • Kd values show analogous saturation at 5,000 nM and 20,000 nM for the same reason.
  • IC50 and Kd are not directly comparable and have not been normalized or converted between each other. Both are retained with measurement_type indicating which was recorded.

Structure caveats:

  • All 3D structures are computationally generated; no experimental validation has been performed.
  • RMSD values embedded in silent files are calculated relative to the threaded template input, not to any experimental reference structure, and should not be interpreted as a measure of model accuracy.
  • The best-scoring Rosetta decoy is not guaranteed to represent the native-like conformation.

Coverage caveats:

  • Allele representation is highly uneven: A*02:01 accounts for 9,500 entries (19% of the dataset), while several alleles have fewer than 10 entries.
  • The dataset covers HLA class I only (HLA-A and HLA-B). HLA-C and class II alleles are not included.
  • The assay_pdb_id column references experimental structures deposited in the PDB as recorded by IEDB; these structures are not included in this dataset.

License

This dataset is released under CC BY 4.0. You are free to share and adapt the material for any purpose, provided appropriate credit is given.

Experimental binding data is sourced from the Immune Epitope Database (IEDB), which is also available under CC BY 4.0. Please cite IEDB if you use this dataset:

Vita R, Mahajan S, Overton JA, et al. The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Research. 2019;47(D1):D339–D343. https://doi.org/10.1093/nar/gky1006

Usage Example 

import pandas as pd
from pathlib import Path

# Load metadata
df = pd.read_csv("metadata.csv")

# Filter to unflagged IC50 entries below detection limit
df_clean = df[
    ~df["flagged"] &
    (df["measurement_type"] == "IC50") &
    (df["measurement_value"] < 20000)
]

print(f"{len(df_clean)} clean IC50 entries")
print(f"Alleles: {df_clean['allele'].nunique()}")
print(f"Unique peptides: {df_clean['peptide'].nunique()}")

# Locate the silent file for a specific peptide-allele pair
silent_file = Path("structures/A0101/FHEFLSSKL.silent")
print(f"Silent file exists: {silent_file.exists()}")

# To extract individual PDB structures, use Rosetta's extract_pdbs:
# extract_pdbs.linuxgccrelease -in:file:silent structures/A0101/FHEFLSSKL.silent