Cryptic Pockets Repository through Pocket Dynamics Tracking and Metadynamics on Essential Dynamics Space:...

Area of Science:

• Computational chemistry
• Structural biology
• Drug discovery

Background:

• Detecting cryptic pockets is crucial for structure-based drug discovery, particularly for targeting previously undruggable proteins.
• Experimental methods for cryptic pocket detection are costly and time-consuming.
• Computational methods like molecular dynamics (MD) simulations offer insights into protein dynamics but struggle with high-energy barriers inherent in detecting transient pockets.

Purpose of the Study:

• To evaluate Metadynamics on essential coordinates as a general computational approach for detecting cryptic pockets.
• To apply this method to the antiapoptotic protein Mcl-1 as a model system.
• To characterize the conformational landscape of Mcl-1 and identify its cryptic pockets in an unsupervised manner.

Main Methods:

• Utilized Metadynamics, an enhanced sampling technique, to overcome time-scale limitations of unbiased MD simulations.
• Employed Metadynamics biasing on essential coordinates, avoiding the need for a priori knowledge of binding sites.
• Applied the method to atomistic simulations of the Mcl-1 protein.

Main Results:

• The Metadynamics approach successfully characterized the conformational space of Mcl-1.
• The method effectively identified both known and novel cryptic pockets of Mcl-1.
• Demonstrated the unsupervised and general applicability of the Metadynamics biasing scheme for cryptic pocket detection.

Conclusions:

• Metadynamics applied to essential coordinates offers a powerful, generalizable, and unsupervised strategy for cryptic pocket detection in drug discovery.
• This computational approach enhances the ability to explore protein dynamics and identify potential drug targets.
• The method provides a valuable tool for drugging the undruggable proteome by revealing hidden binding sites.