Modeling protein dynamics is crucial for understanding functional mechanisms and identifying transient binding pockets with therapeutic potential. While molecular dynamics (MD) simulations offer detailed, atomistic insights into conformational flexibility, their high computational cost and complex data analysis limit scalability for large-scale applications. To address these challenges, we introduce Pocket-Tracker, a Python library for automated detection, characterization, and analysis of protein binding pockets in dynamic simulations.
Pocket-Tracker integrates AlphaFlow, a machine learning-based method that generates conformational ensembles from protein sequences, with established cavity detection algorithms such as Fpocket and a geometry-based pocket encoder, S-Pocket. This pipeline enables the identification and tracking of pocket properties, such as volume, hydrophobicity, geometry, and druggability, across trajectories generated by both classical MD and AlphaFlow.
To assess the potential of AlphaFlow for dynamic pocket analysis, we applied Pocket-Tracker to protein trajectories from both simulation types, including those in the ATLAS database, observing strong qualitative agreement in pocket characteristics. By simplifying structural analysis and enabling high-throughput pocket characterization, Pocket-Tracker provides an efficient and scalable framework to support early-stage drug discovery
Modeling protein dynamics is crucial for understanding functional mechanisms and identifying transient binding pockets with therapeutic potential. While molecular dynamics (MD) simulations offer detailed, atomistic insights into conformational flexibility, their high computational cost and complex data analysis limit scalability for large-scale applications. To address these challenges, we introduce Pocket-Tracker, a Python library for automated detection, characterization, and analysis of protein binding pockets in dynamic simulations.
Pocket-Tracker integrates AlphaFlow, a machine learning-based method that generates conformational ensembles from protein sequences, with established cavity detection algorithms such as Fpocket and a geometry-based pocket encoder, S-Pocket. This pipeline enables the identification and tracking of pocket properties, such as volume, hydrophobicity, geometry, and druggability, across trajectories generated by both classical MD and AlphaFlow.
To assess the potential of AlphaFlow for dynamic pocket analysis, we applied Pocket-Tracker to protein trajectories from both simulation types, including those in the ATLAS database, observing strong qualitative agreement in pocket characteristics. By simplifying structural analysis and enabling high-throughput pocket characterization, Pocket-Tracker provides an efficient and scalable framework to support early-stage drug discovery Read More
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