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Accelerating Cryptic Pocket Discovery Using AlphaFold.

Artur Meller1,2, Soumendranath Bhakat1,3, Shahlo Solieva3

  • 1Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States.

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This summary is machine-generated.

AlphaFold aids cryptic pocket discovery by predicting open states in 60% of cases. For difficult targets like plasmepsin II, AlphaFold-generated structures accelerate simulations, revealing pocket dynamics missed by conventional methods.

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Area of Science:

  • Computational Biology
  • Drug Discovery
  • Structural Biology

Background:

  • Cryptic pockets are promising drug targets but challenging to study due to slow opening dynamics.
  • Conventional simulations often fail to capture these slow motions, hindering drug discovery efforts.

Purpose of the Study:

  • To evaluate AlphaFold's capability in accelerating the discovery of cryptic pockets.
  • To assess if AlphaFold can generate open or partially open cryptic pocket structures for enhanced simulation.

Main Methods:

  • AlphaFold was used to generate structural ensembles for 10 known cryptic pocket targets.
  • Simulations were initiated from AlphaFold-generated structures, including partially open states.
  • Markov state models (MSMs) were constructed to analyze pocket opening dynamics.

Main Results:

  • AlphaFold successfully sampled the open cryptic pocket state in 6 out of 10 cases.
  • For plasmepsin II, AlphaFold provided a partially open state that enabled successful simulation of pocket opening.
  • AlphaFold-seeded simulations generated free energy landscapes comparable to established methods like well-tempered metadynamics.

Conclusions:

  • AlphaFold shows utility in identifying and facilitating the study of cryptic pockets.
  • While promising, AlphaFold alone may not be sufficient for sampling all cryptic pocket openings.
  • Combining AlphaFold with simulations offers a powerful strategy for cryptic pocket discovery.