Binding Networks Identify Targetable Protein Pockets for Mechanism-Based Drug Design

Area of Science:

• Biochemistry
• Computational Biology
• Drug Discovery

Background:

• The human genome encodes a limited number of druggable proteins, primarily receptors and enzymes.
• Enzymes with long binding cavities present untapped potential for new drug-binding mechanisms and modes.
• Identifying transient prerequisite binding sites within these cavities is a significant challenge in drug design.

Purpose of the Study:

• To introduce NetBinder, a novel computational method for systematically identifying and classifying prerequisite binding sites at atomic resolution.
• To elucidate the complete drug-binding mechanism, including previously undiscovered events, by analyzing atomistic simulations.
• To validate NetBinder's efficacy using the blebbistatin-myosin 2 interaction as a model system.

Main Methods:

• NetBinder employs atomistic simulations to model the entire inhibitor binding process.
• A networking framework is utilized to select key binding modes and map the binding pathway.
• The method was validated through the study of blebbistatin binding to myosin 2.

Main Results:

• NetBinder successfully identified and classified prerequisite binding sites at atomic resolution.
• The study uncovered the complete binding mechanism of blebbistatin to myosin 2.
• The proposed mechanism demonstrated excellent agreement with experimental data on myosin 2 structural changes.

Conclusions:

• NetBinder offers a new paradigm for drug design by elucidating binding mechanisms at atomic resolution.
• The method can be readily applied to other proteins with long internal cavities, such as G-protein-coupled receptors and ion channels.
• This approach enhances the discovery of novel drug-binding modes and therapeutic targets.