Ohm

Ohm maps allosteric communication networks in proteins by applying percolation theory to protein structural data to identify communication pathways and critical residues.

Key Features:

• Network-Based Methodology: Uses a network representation of protein structure to identify and analyze allosteric pathways.
• Percolation Theory Application: Applies percolation theory to detect communication paths across the protein without requiring molecular dynamics simulations.
• Structure-Only Analysis: Relies solely on protein structural information rather than simulation-based approaches.
• Identification of Critical Residues: Identifies residues that are central to allosteric communication networks.
• Scalability and Validation: Demonstrated computational efficiency and was applied to a dataset of 20 experimentally validated allosterically regulated proteins.

Scientific Applications:

• Prediction of Allosteric Mutations: Predicts mutations that may alter protein function by perturbing allosteric networks.
• Design of Small-Molecule Agents: Informs design of small molecules targeting allosteric pathways to modulate protein activity.
• Correlation with Experimental Data: Produces predictions that can be compared with experimental datasets such as NMR CHESCA for CheY.

Methodology:

Applies percolation theory to protein structural information to identify communication paths and critical residues; validated against experimental data including NMR CHESCA for CheY and applied to a set of 20 experimentally validated allosteric proteins.