README.md

---
license: mit
---

# Summary

**This dataset contains conformational ensembles generated by HyRes physics-based molecular dynamics simulations, together with selected sequence properties and simulation-derived observables, for intrinsically disordered proteins and regions (IDPs/IDRs)** from the human proteome in UniProt database (release 2026_01) and the DisProt database (release 2025_06 with ambiguous evidence).

Hybrid Resolution (HyRes) force field is hybrid-resolution protein model with atomistic backbone and an intermediate-resolution sidechains. By maintaining detailed backbone structure while simplifying sidechain representation, HyRes enables efficient simulation of IDPs/IDRs systems with reduced computational cost, while preserving key structural and dynamical features necessary for biologically meaningful results. The HyRes model is optimized using 20 A1-LCD variants for radius of gyration (Rg) and 15 diverse IDPs for residual helical propensity.

HyRes has been extensively benchmarked aganist experimental measurements of monomeric IDPs as well as their interactions in complex environments:

- Small-angle X-ray scattering (SAXS): benchmarked on 98 IDPs with sequence lengths ranging from 16 to 477 residues for the Rg, achieving a Pearson correlation of 0.97.
- Single-molecule FRET (smFRET): benchmarked on 16 IDPs for end-to-end distance (Re), yielding a Pearson correlation of 0.88.
- Paramagnetic Relaxation Enhancement (PRE): benchmarked on 9 IDPs, achiving a Pearson correlation of 0.72, demonstrating ability of HyRes to capture long-range interactions.
- NMR chemical shift: 40 IDPs for mean helicity, achiveing a Pearson correlation of 0.83.
- Dynamic interactions of IDPs in diverse environments: H1-ProTα is studied in the unbound, dimer, and condensate enviroment.
- Phase seperation of IDPs: benchmarked on ~64 IDPs, achieving a Pearson correlation of 0.74 for Csat.

**This dataset is a valuable resource for researchers working on intrinsically disordered proteins and regions (IDPs/IDRs)**. It can be used for bioinformatics analyses as well as for developing deep learning models for IDP-related studies.

## Data Description

| Dataset    | Sequences | Sequence length | Derived                       | Simulation details | Total frames |
|:----------:|:---------:|:---------------:|:-----------------------------:| :-----------------:| :-----------:|
| Human IDRs | 27,489    | 30-999 residues | AlphaFold2 pLDDT scores based | 2μ s and 4μ s for <300 and >300 residues respectively. | 1000 per Sequence* |
| DisProt    | 2,383     | 30-778 residues | DisProt + IUPRED2a analysis   | 2μ s and 4μ s for <300 and >300 residues respectively. | 1000 per Sequence* |
| Total      | 29,872    | 30-999 residues | - | - | - |

\* - after discarding inital 10% of the frames

### Data Files

Each individual folder contains the following files:

```
Q9UKF5_153_198/
├── Q9UKF5_153_198.psf
├── Q9UKF5_153_198.dcd
├── Q9UKF5_153_198_Rg.npy
├── Q9UKF5_153_198_E2E.npy
├── Q9UKF5_153_198_DSSP.npy
└── Q9UKF5_153_198.xlsx
    ├── Properties
    ├── Helical_segments
    └── Residual_helicity
```

1. **Structure File (`.psf`)**: Protein Structure File (PSF) containing the molecular topology. This file is required together with the trajectory file (`.dcd`) for structural analysis.

2. **Trajectory File (`.dcd`)**: Molecular dynamics trajectory file containing atomic coordinates for 1,000 frames, obtained after discarding the initial 10% of the simulation frames.

3. **Radius of Gyration (`_Rg.npy`)**: NumPy array containing the radius of gyration (Rg) values computed for 1,000 trajectory frames, with one value per frame. The radius of gyration provides a measure of the overall compactness of the IDPs/IDRs throughout the simulation.

4. **End-to-End Distance (`_E2E.npy`)**: NumPy array containing end-to-end distance (Re) computed for 1,000 trajectory frames, with one value per frame. This quantity measures the distance between the terminal residues during the simulation.

5. **DSSP Secondary Structure (`_DSSP.npy`)**: NumPy array containing DSSP-derived secondary structure assignments (8 discrete classes). Secondary structure is assigned to each residue in every simulation frame.

      | Code | Structure               |
      | :--: | ---------------------   |
      | H    | α-helix                 |
      | B    | Isolated β-bridge       |
      | E    | β-strand                |
      | G    | 3-10 helix              |
      | I    | π-helix                 |
      | T    | Turn                    |
      | S    | Bend                    |
      | -    | Loops                   |


6. **Analysis Spreadsheet (`.xlsx`)**: Excel workbook containing sequence-derived properties, simulation-derived properties, and protein annotation details, and information on helical segments and residual helicity.

    **`Properties`**

    The `Properties` sheet in each Excel file contains sequence-level physicochemical properties, simulation-derived observables, and biological annotations.

    - **Sequence information**
      | Column            | Description                                                      |
      | ----------------- | ---------------------------------------------------------------- |
      | `Name`            | Unique identifier for the sequence in the HyRes-IDRome dataset   |
      | `Seq`             | Amino acid sequence of the intrinsically disordered region (IDR) |
      | `Start`           | Starting residue index in the full protein sequence              |
      | `End`             | Ending residue index in the full protein sequence                |
      | `Sequence length` | Length of the IDR sequence                                       |
      | `Protein_name`    | Name of the parent protein                                       |

    - **Sequence-derived properties**
      The following properties were computed using [localCIDER](https://pappulab.wustl.edu/CIDER/).

      | Column               | Description                                                           |
      | -------------------- | --------------------------------------------------------------------- |
      | `kappa`              | Extent of charge segregation in the sequence                          |
      | `FCR`                | Fraction of charged residues                                          |
      | `NCPR`               | Net charge per residue                                                |
      | `Neg_Fraction`       | Fraction of negatively charged residues                               |
      | `Pos_Fraction`       | Fraction of positively charged residues                               |
      | `Fraction_expanding` | Fraction of residues predicted to promote chain expansion (E/D/R/K/P) |
      | `AA_fractions`       | Dictionary containing amino acid composition fractions                |
      | `Mean_net_charge`    | Absolute mean net charge                                              |
      | `Mean_hydropathy`    | Mean hydropathy computed using the normalized Kyte-Doolittle scale    |

    - **Simulation-derived properties**

      | Column           | Description                                 |
      | ---------------- | ------------------------------------------- |
      | `Mean_Rg (Å)`    | Mean radius of gyration                     |
      | `Std_Rg (Å)`     | Standard deviation of radius of gyration    |
      | `Mean_Re (Å)`    | Mean end-to-end distance                    |
      | `Std_Re (Å)`     | Standard deviation of end-to-end distance   |
      | `Mean_helicity`  | Mean helicity averaged over residue         |
      | `Flory_exponent` | Estimated Flory scaling exponent (ν)    |

    - **DisProt-derived annotations**
      The following annotations are available for sequences derived from [DisProt](https://disprot.org/).

      | Column           | Description                            |
      | ---------------- | -------------------------------------- |
      | `region_id`      | DisProt region identifier              |
      | `acc`            | UniProt accession identifier           |
      | `organism`       | Source organism                        |
      | `ncbi_taxon_id`  | NCBI taxonomy identifier               |
      | `disprot_id`     | DisProt entry identifier               |
      | `term_namespace` | Controlled vocabulary namespace        |
      | `term`           | Functional or structural ontology term |
      | `term_name`      | Human-readable ontology term           |
      | `ec`             | Evidence code                          |
      | `ec_name`        | Evidence code description              |
      | `reference`      | Literature or database reference       |

    - **Taxonomic annotations**

      | Column    | Description                  |
      | --------- | ---------------------------- |
      | `Domain`  | Annotated protein domain     |
      | `Kingdom` | Taxonomic kingdom            |
      | `Phylum`  | Taxonomic phylum             |
      | `Class`   | Taxonomic class              |
      | `Order`   | Taxonomic order              |
      | `Family`  | Taxonomic family             |
      | `Genus`   | Taxonomic genus              |
      | `Species` | Taxonomic species annotation |

      Note: There are few NaN values for the organism that have not assigned to any taxonomic category. Also, all the Viruses have the Domain "Virus" instead of NaN.

    - **Human IDRome-derived annotations**
      The following annotations are available for sequences derived from [UniProt](https://www.uniprot.org/).

      | Column                | Description                                  |
      | --------------------- | -------------------------------------------- |
      | `UniProt_ID`          | UniProt accession identifier                 |
      | `Gene`                | Gene name                                    |
      | `Cellular Components` | Gene Ontology cellular component annotations |
      | `GO_ID:Cellular Components` | GO identifiers corresponding to the cellular component annotations |
      | `GO_ID_depth:Cellular Components` | Depth of each cellular component GO term in the GO hierarchy. |
      | `Molecular Functions` | Gene Ontology molecular function annotations |
      | `GO_ID:Molecular_Functions` | GO identifiers corresponding to the molecular function annotations |
      | `GO_ID_depth:Molecular_Functions` | Depth of each molecular function GO term in the GO hierarchy. |

    ---

    **`Helical_segments`**
      Helical segment statistics represented as (Derived from 1800 frames):

      | Start residue | End residue | Segment length | Count |
      | :-----------: | :---------: | :------------: | :---: |
      | 1             | 8           | 8              | 160   |

      where:

      - `Start residue` = starting residue index of helical segment.
      - `End residue` = ending residue index of helical segment.
      - `Segment length` = length of helical segment.
      - `Count` = total number of frames in which the helical segment occurs.

    ---

    **`Residual_helicity`**
    Per-residue helicity values.

    | Residue Index | Residue Name | Helicity |
    | :-----------: | :----------: | :------: |
    | 1             |  A           |   0.25   |

    where:
    - `Helicity` represents the fraction of frames in which the residue adopts an α-helical conformation.

## Usage

Recommended Python packages for loading and analyzing the dataset:

```bash
pip install numpy pandas openpyxl MDAnalysis datasets huggingface_hub 
```

---

```python
# Whole dataset
from datasets import load_dataset
ds = load_dataset("umassmdlab/HyRes-IDRome")

# Specific IDR
from huggingface_hub import snapshot_download
Q9UKF5_153_198_path = snapshot_download(
    repo_id="umassmdlab/HyRes-IDRome",
    allow_patterns="Q9UKF5_153_198/*",
    repo_type="dataset"
)

```

```python
import numpy as np
import pandas as pd
import MDAnalysis as mda

# Load radius of gyration
Rg = np.load("Q9UKF5_153_198_Rg.npy")

# Load end to end distance
Re = np.load("Q9UKF5_153_198_E2E.npy")

# Load DSSP assignments
dssp = np.load("Q9UKF5_153_198_DSSP.npy")

# Load Excel data
properties = pd.read_excel("Q9UKF5_153_198.xlsx", sheet_name="Properties")
Helical_segments = pd.read_excel("Q9UKF5_153_198.xlsx", sheet_name="Helical_segments")
Residual_helicity = pd.read_excel("Q9UKF5_153_198.xlsx", sheet_name="Residual_helicity")

# Load trajectory
u = mda.Universe("Q9UKF5_153_198.psf", "Q9UKF5_153_198.dcd")

```

---

## Trajectory Conversion

The provided trajectories were generated using the HyRes model. To reconstruct atomistic sidechains from the coarse-grained sidechains, we recommend using [CG2ALL](https://github.com/huhlim/cg2all).

```bash

conda create --name cg2all pip cudatoolkit=11.3 dgl=1.0 -c dglteam/label/cu113

```

Q9UKF5_153_198 workflow:

```bash

convert_cg2all \
  -p Q9UKF5_153_198.psf \
  -d Q9UKF5_153_198.dcd \
  -o Q9UKF5_153_198.aa.dcd \
  -opdb Q9UKF5_153_198.aa.pdb \
  --cg MainchainModel \
  --all

```

Please refer to the official CG2ALL documentation for installation instructions and advanced usage options.

## Additional Info
### Reference

Shanlong Li, Shrishti Barethiya, and Jianhan Chen. HyRes: Accurate Physics-Based Simulation of Dynamic Protein Structures and Interactions in Complex Environments at Scale. bioRxiv 2026.
DOI: [https://doi.org/10.64898/2026.06.23.734133](https://doi.org/10.64898/2026.06.23.734133)