README.md

metadata

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:

{
    "seq": "MKTFFVLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFL",
    "tertiary": [[x1,y1,z1], [x2,y2,z2], ...],
    "valid_mask": [true, true, false, ...]
}

Default Dataset: fredzzp/contact_data (automatically downloaded from HuggingFace Hub)