ProtSpace

ProtSpace is a visualization tool for exploring protein embeddings or similarity matrix along their 3D protein structures. It allows users to interactively visualize high-dimensional protein language model data in 2D or 3D space, color-code proteins based on various features, and view protein structures when available.

Table of Contents

• ProtSpace
  • Table of Contents
  • Quick Start with Google Colab
  • Example Outputs
    • 2D Scatter Plot (SVG)
    • 3D Interactive Plot
  • Installation
  • Usage
    • Data Preparation
    • Running protspace
  • Features
  • Data Preparation
    • Required Arguments
    • Optional Arguments
    • Method-Specific Parameters
  • Custom Feature Styling
  • File Formats
    • Input
    • Output

Quick Start with Google Colab

Try ProtSpace instantly using our Google Colab notebooks:

Note: Some Google Colab functionalities may not work properly in Safari browsers. For the best experience, we recommend using Chrome or Firefox.

1. Explore Pre-computed Visualizations:

2. Generate Protein Embeddings:

3. Full Pipeline Demo:

Example Outputs

2D Scatter Plot (SVG)

3D Interactive Plot

View 3D Interactive Plot

Installation

pip install protspace

Usage

Data Preparation

protspace-json -i embeddings.h5 -m features.csv -o output.json --methods pca3 umap2 tsne2

Running protspace

protspace output.json [--pdb_zip pdb_files.zip] [--port 8050]

Access the interface at http://localhost:8050

Features

• Interactive 2D/3D visualization with multiple dimensionality reduction methods:
  • Principal Component Analysis (PCA)
  • Multidimensional Scaling (MDS)
  • Uniform Manifold Approximation and Projection (UMAP)
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)
  • Pairwise Controlled Manifold Approximation (PaCMAP)
• Feature-based coloring and marker styling
• Protein structure visualization (with PDB files)
• Search and highlight functionality
• High-quality plot exports
• Responsive web interface

Data Preparation

The protspace-json command supports:

Required Arguments

• -i, --input: HDF file (.h5) or similarity matrix (.csv)
• -m, --metadata: CSV file with features (first column must be named "identifier" and match IDs in HDF5/similarity matrix)
• -o, --output: Output JSON path
• --methods: Reduction methods (e.g., pca2, tsne3, umap2, pacmap2, mds2)

Optional Arguments

• --custom_names: Custom projection names (e.g., pca2=PCA_2D)
• --verbose: Increase output verbosity

Method-Specific Parameters

• UMAP:
  • --n_neighbors: Number of neighbors (default: 15)
  • --min_dist: Minimum distance (default: 0.1)
• t-SNE:
  • --perplexity: Perplexity value (default: 30)
  • --learning_rate: Learning rate (default: 200)
• PaCMAP:
  • --mn_ratio: MN ratio (default: 0.5)
  • --fp_ratio: FP ratio (default: 2.0)
• MDS:
  • --n_init: Number of initializations (default: 4)
  • --max_iter: Maximum iterations (default: 300)

Custom Feature Styling

Use protspace-feature-colors to customize feature appearance:

protspace-feature-colors input.json output.json --feature_styles '{
  "feature_name": {
    "colors": {
      "value1": "#FF0000",
      "value2": "#00FF00"
    },
    "shapes": {
      "value1": "circle",
      "value2": "square"
    }
  }
}'

Available shapes: circle, circle-open, cross, diamond, diamond-open, square, square-open, x

File Formats

Input

1. Embeddings/Similarity

   • HDF5 (.h5) for embeddings
   • CSV for similarity matrix

2. Metadata

   • CSV with mandatory 'identifier' column matching IDs in embeddings/similarity data
   • Additional columns for features

3. Structures

   • ZIP containing PDB/CIF files
   • Filenames match identifiers (dots replaced with underscores)

Output

• JSON containing:
  • Protein features
  • Projection coordinates
  • Visualization state (colors, shapes)