Understanding Cryptic Pocket Formation in Protein Targets by Enhanced Sampling Simulations.

Area of Science:

• Computational chemistry
• Structural biology
• Drug discovery

Background:

• Cryptic pockets are transient protein sites that emerge upon drug binding, offering novel drug development opportunities.
• Classical binding sites are well-studied, but cryptic sites present unique challenges for discovery and characterization.

Purpose of the Study:

• To investigate the nature and dynamics of cryptic sites in pharmacologically relevant protein targets.
• To compare the efficacy of various simulation-based approaches for discovering cryptic sites.
• To develop and validate a novel computational method for identifying druggable cryptic pockets.

Main Methods:

• Molecular dynamics (MD) simulations of four pharmacologically relevant protein targets.
• Evaluation of standard MD, enhanced sampling (Parallel Tempering), and fragment probing techniques.
• Development and application of a novel Hamiltonian Replica Exchange-based method (SWISH) combined with fragment probes.
• Analysis of simulation data to understand cryptic site formation mechanisms and distinguish true pockets from false positives.

Main Results:

• Cryptic sites do not exist as stable minima in the free energy landscape of unliganded proteins and tend to close rapidly in simulations.
• Standard MD and temperature-based enhanced sampling methods were insufficient for observing cryptic site opening.
• Fragment probing in long simulations occasionally revealed cryptic site opening and binding.
• The novel SWISH method, combined with probes, demonstrated significant promise for general cryptic site discovery.
• A method was proposed to differentiate true druggable cryptic pockets from false positives.

Conclusions:

• Cryptic site formation involves an interplay between induced-fit and conformational selection mechanisms.
• The SWISH method offers a robust computational strategy for identifying cryptic pockets, advancing drug discovery efforts.
• Understanding pocket dynamics is crucial for selecting efficient computational methods in drug development.