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---
dataset_info:
features:
- name: name
dtype: string
- name: seq
dtype: string
- name: tertiary
sequence:
sequence: float64
- name: valid_mask
sequence: bool
- name: metadata
struct:
- name: contact_density
dtype: float64
- name: has_valid_structure
dtype: bool
- name: invalid_residues
dtype: int64
- name: long_range_contacts
dtype: int64
- name: medium_range_contacts
dtype: int64
- name: seq_length
dtype: int64
- name: short_range_contacts
dtype: int64
- name: token_count
dtype: int64
- name: tokenization_ratio
dtype: float64
- name: total_contacts
dtype: int64
- name: valid_residues
dtype: int64
splits:
- name: train
num_bytes: 169570266
num_examples: 25299
- name: validation
num_bytes: 1350998
num_examples: 224
- name: test
num_bytes: 356465
num_examples: 40
download_size: 96822838
dataset_size: 171277729
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
## Background
### What is Protein Contact Prediction?
Protein contact prediction is a fundamental task in computational biology that aims to predict which amino acid residues in a protein sequence are in close spatial proximity (typically within 8Å) in the protein's 3D structure. This information is crucial for:
- **Protein structure prediction**
- **Understanding protein folding mechanisms**
- **Drug discovery and design**
- **Evolutionary analysis**
### Linear Probing for Protein Language Models
Linear probing is a technique used to evaluate the quality of learned representations from pre-trained language models. In the context of protein language models (PLMs):
1. **Freeze** the pre-trained model parameters
2. **Train** only a simple linear classifier on top of the frozen embeddings
3. **Evaluate** how well the linear classifier performs on downstream tasks
This approach helps assess the quality of protein representations learned by different PLMs without the confounding effects of fine-tuning the entire model.
## Task Definition
### Contact Map Generation
| Parameter | Value | Description |
|-----------|--------|-------------|
| **Distance Threshold** | 8.0 Å | Distance between Cα atoms |
| **Sequence Separation** | ≥ 6 residues | Minimum separation (\|i-j\| ≥ 6) |
| **Valid Contacts** | Required | Only consider residues with valid 3D coordinates |
### Evaluation Metrics
The evaluation follows standard contact prediction protocols with precision at different coverage levels:
#### Range Categories
| Range | Separation | Description |
|-------|------------|-------------|
| **Short Range** | 6 ≤ \|i-j\| ≤ 11 | Local contacts |
| **Medium Range** | 12 ≤ \|i-j\| ≤ 23 | Medium-distance contacts |
| **Long Range** | \|i-j\| ≥ 24 | Long-distance contacts |
#### Precision Metrics
- **P@L**: Precision at top L contacts (L = sequence length)
- **P@L/2**: Precision at top L/2 contacts
- **P@L/5**: Precision at top L/5 contacts
Each metric is calculated separately for each range category, resulting in **9 total metrics** per evaluation.
### Dataset Format
The system uses HuggingFace datasets with entries containing:
```json
{
"seq": "MKTFFVLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFL",
"tertiary": [[x1,y1,z1], [x2,y2,z2], ...],
"valid_mask": [true, true, false, ...]
}
```
**Default Dataset**: `fredzzp/contact_data` (automatically downloaded from HuggingFace Hub)
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