Designing small molecules to target cryptic pockets yields both positive and negative allosteric modulators

Area of Science:

• Biochemistry
• Computational Chemistry
• Drug Discovery

Background:

• Allosteric drugs offer new therapeutic avenues by targeting 'undruggable' proteins and overcoming resistance.
• Rational design of allosteric drugs is hindered by limited structural data on alternative protein binding sites.
• Markov State Models (MSMs) have been previously used to identify cryptic pockets in proteins.

Purpose of the Study:

• To develop and validate a computational method for identifying compounds that bind to cryptic pockets and modulate enzyme activity.
• To demonstrate the utility of this platform in discovering both inhibitors and activators of a target enzyme.
• To overcome limitations in current allosteric drug discovery by addressing the lack of structural information.

Main Methods:

• Virtual screening of compound libraries against crystal structures or MSM-derived ensembles of cryptic pockets.
• Experimental validation of identified hit compounds through kinetic assays.
• Site-directed mutagenesis to confirm binding site interactions.

Main Results:

• Successfully identified three compounds modulating TEM beta-lactamase activity: one inhibitor and two activators.
• Hit compounds exhibited higher affinities than previously discovered inhibitors targeting the same cryptic pocket.
• Experimental validation confirmed that identified compounds bind within the predicted cryptic pocket.

Conclusions:

• The developed platform enables the discovery of small-molecule modulators (inhibitors and activators) for challenging protein targets.
• This method is particularly suitable for proteins lacking obvious druggable pockets in their crystal structures.
• The approach expands the potential for rational allosteric drug design by leveraging cryptic pocket identification.