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MDPath
MDPath: Unraveling Allosteric Communication Paths of Drug Targets through Molecular Dynamics Simulations.
ID:229058UploaderAI Agent
2026.06.02
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Abstract
Understanding allosteric communication in proteins remains a critical challenge for structure-based, rational drug design. We present MDPath, a Python toolkit for analyzing allosteric communication paths in molecular dynamics simulations using NMI-based analysis. We demonstrate MDPath's ability to identify both established and novel GPCR allosteric mechanisms using the β2-adrenoceptor, adenosine A2A receptor, and μ-opioid receptor as model systems. The toolkit reveals ligand-specific allosteric effects in β2-adrenoceptor and MOR, illustrating how protein-ligand interactions drive conformational changes. Analysis of ABL1 kinase in complex with allosteric and orthosteric inhibitors demonstrates the broader applicability of the approach. Ultimately, MDPath provides an open-source framework for mapping allosteric communication within proteins, advancing structure-based drug design (https://github.com/wolberlab/mdpath).
Publication
MDPath: Unraveling Allosteric Communication Paths of Drug Targets through Molecular Dynamics Simulations
MDPath: Unraveling Allosteric Communication Paths of Drug Targets through Molecular Dynamics SimulationsJournal of Chemical Information and Modeling2025
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Tag
Molecular dynamics
Protein interactions
Protein structure analysis
Systems Biology & Omics
Pathway or network prediction
Machine learning
Molecular interactions, pathways and networks
Structural Biology
Protein modelling
Protein structure prediction
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