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Computational advances in discovering cryptic pockets for drug discovery.

Martijn P Bemelmans1, Zoe Cournia2, Kelly L Damm-Ganamet3

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Many therapeutic target proteins are considered "undruggable" due to absent ligandable pockets.
  • Protein dynamics reveal transient "cryptic" pockets that form upon ligand binding.
  • These pockets offer a strategy to target difficult proteins.

Purpose of the Study:

  • To review computational methods for modeling cryptic pockets.
  • To highlight established and emerging techniques in cryptic pocket identification.
  • To discuss applications in therapeutically relevant proteins.

Main Methods:

  • Mixed solvent molecular dynamics simulations.
  • Enhanced sampling techniques.
  • Artificial intelligence (AI)-based methods for pocket prediction.

Main Results:

  • Established methods like mixed solvent MD are effective for modeling cryptic pockets.
  • Enhanced sampling and AI approaches show promise for identifying transient binding sites.
  • These computational strategies are being applied to challenging therapeutic targets.

Conclusions:

  • Cryptic pocket modeling is a viable strategy for targeting previously undruggable proteins.
  • Advancements in computational methods are expanding the druggable proteome.
  • This review provides insights into current and future directions for drug discovery targeting cryptic pockets.