ProteinLens

ProteinLens analyzes allosteric signaling in biomolecular structures using atomistic, energy-weighted graph-theoretical methods to identify communication pathways relevant to allosteric regulation.

Key Features:

• Atomistic Graph Representation: Generates a fully atomistic, energy-weighted graph of a biomolecule or biomolecular complex from a PDB file that captures atom-level structural relationships.
• Allosteric Signaling Analysis: Employs the computationally efficient methods Markov Transients and bond-to-bond propensities to analyze signaling and communication across the molecular network.
• Scoring and Ranking: Scores and ranks individual bonds and residues according to the speed and magnitude of fluctuation propagation from any chosen site, such as an active site.
• Statistical Quantile Scores and Visualization: Reports results using statistical quantile scores and visualization in plots and 3D structure viewers to represent significant communication pathways.

Scientific Applications:

• Allosteric site identification: Identifies potential allosteric sites and key residues involved in signaling and cooperativity.
• Pathway mapping: Reveals long-range communication pathways and connectivity between specific sites within biomolecular structures.
• Drug discovery and modulation: Informs strategies for targeting allosteric sites to modulate protein function for therapeutic purposes.

Methodology:

Construct an atomistic, energy-weighted graph from a PDB file and analyze it with graph-theoretical approaches using Markov Transients and bond-to-bond propensities to compute propagation speed and magnitude, score and rank bonds and residues, and derive statistical quantile scores.