Deep Learning Models Capture Umbrella Sampling-Derived Energetic Trends: A Troponin C Case Study.

Abstract excerpt

Deep learning models have transformed several fields lately. In the past, capturing thermodynamic trends from free energies has relied on computationally expensive and time-consuming umbrella sampling simulations for dynamic proteins. Here, we investigate whether modern deep learning protein design methods (ProteinMPNN and ThermoMPNN) can obtain comparable energetic readouts more expeditiously. As a case study we...

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• Calcium
• Deep Learning
• Hydrophobic and Hydrophilic Interactions
• Molecular Dynamics Simulation
• Mutation