Pathfinder: Protein Structure Ensemble Clustering and Representative Selection

Fig. 1: Overview of the Pathfinder clustering pipeline, from PDB input to ranked representatives.

Pathfinder is a tool for clustering protein structure ensembles (e.g., from AlphaFold predictions) and selecting representative conformations. It supports dimensionality reduction via distance maps or TM-score matrices, followed by clustering using algorithms like K-means, hierarchical, DBSCAN, spectral, or GMM. Optional integration with reference structures enables ranking based on TM-align scores.

The pipeline processes PDB files in ensembles, extracts features, clusters them, and outputs ranked representatives with metrics.

Features

• Feature Extraction: Residue distance maps or TM-score distance matrices of given ensemble(s).
• Clustering: Multiple algorithms with/without PCA dimensionality reduction.
• Ranking: Confidence-weighted selection and ranking; optional TM-score comparison to references.
• Parallelism: Multi-process support for efficiency.
• Batch Processing: Handle single proteins, multiple, or directories via a wrapper script.

Prerequisites

• Python 3.8+ with NumPy, Pandas, scikit-learn, and SciPy.
• External tools (installed with conda):
  • Foldseek (for TM-score matrices).
  • TM-align (binary in PATH).
• A conda environment (example provided in scripts).

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/pathfinder.git
   cd pathfinder

2. Create and activate a mamba environment:

   mamba create -n pathfinder -f environment.yml 
   mamba activate pathfinder
   

Quick Start

Activate your environment and ensure src/ and scripts are in your PATH or current directory.

Run the test script to see an example:

chmod +x run_test.sh
./run_test.sh

python src/main.py \
    --ensemble_dir /path/to/ensemble/dir \
    --output_dir /path/to/output/dir \
    --cluster_method kmeans \
    --n_clusters 10 \
    --n_pca_components 10 \
    --transformer tmscore \
    --alpha 1.0 \
    --n_processes 32 \
    --ref_list_txt /path/to/refs.txt  # Optional

Interactive ensemble analysis and state identification

cd dashapp
python app.py

Fig. 1: Overview of the ensemble analysis and state identification interactive utility

Cite

To be added