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How Well Can AI and Physics-Based Simulations Predict the Probability a Cryptic Pocket Is Open?

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New AI and molecular dynamics methods show promise for identifying cryptic pockets in drug discovery. While predicting mutation effects on pocket opening is successful, accurately determining absolute opening probabilities remains a challenge, especially for rare pockets.

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Cryptic pockets are crucial targets in drug discovery for modulating challenging proteins.
  • Current AI structure prediction methods lack the physics to fully characterize protein conformational ensembles, including cryptic pockets.
  • Several new AI-based methods (AlphaFlow, BioEmu, PocketMiner, CryptoBank) aim to address this limitation.

Purpose of the Study:

  • To benchmark AI models and molecular dynamics (MD) simulations in characterizing known cryptic pockets.
  • To evaluate the ability of these methods to recapitulate experimental thermodynamics of cryptic pockets in Ebola VP35 and TEM β-lactamase.
  • To assess performance in predicting pocket opening probability changes due to mutations.

Main Methods:

  • Benchmarking AI models (AlphaFlow, BioEmu, PocketMiner, CryptoBank) and physics-based molecular dynamics (MD) simulations.
  • Utilizing experimentally characterized cryptic pockets in Ebola VP35 and TEM β-lactamase.
  • Comparing predicted pocket opening probabilities with experimental data for wild-type proteins and mutants.

Main Results:

  • Multiple AI and MD methods accurately predict whether mutations increase or decrease cryptic pocket opening probability.
  • No method reliably predicts the absolute probability of pocket opening, particularly for rare pockets (<1% opening).
  • BioEmu and PocketMiner show trends for pockets with >1% opening but exhibit systematic errors and struggle with rare pockets.

Conclusions:

  • AI and simulation-based strategies hold significant promise for cryptic pocket characterization in drug discovery.
  • Further improvements are needed for robust prediction of absolute cryptic pocket opening probabilities, especially for low-probability events.
  • Current methods show potential for guiding mutation strategies but require refinement for precise thermodynamic characterization.