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Measuring Biomolecular DSC Profiles with Thermolabile Ligands to Rapidly Characterize Folding and Binding Interactions

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Funnel metadynamics as accurate binding free-energy method.

Vittorio Limongelli¹, Massimiliano Bonomi, Michele Parrinello

¹Department of Pharmacy, University of Naples Federico II, I-80131 Naples, Italy. vittoriolimongelli@gmail.com

Proceedings of the National Academy of Sciences of the United States of America

|April 5, 2013

Summary

This summary is machine-generated.

We developed funnel metadynamics, a computational method to accurately predict drug-target binding affinity. This approach enhances sampling of binding sites, accelerating drug discovery and understanding molecular interactions.

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Area of Science:

Computational Chemistry
Molecular Dynamics
Drug Discovery

Background:

Accurate estimation of drug affinity to protein targets is crucial for efficient drug discovery.
Understanding ligand-protein interactions guides the development of novel therapeutics.

Purpose of the Study:

To develop a novel computational method for enhanced sampling of protein-ligand binding.
To accurately calculate absolute protein-ligand binding free energy.
To provide insights into the molecular mechanisms of ligand-protein binding.

Main Methods:

Development of funnel metadynamics, a simulation technique enhancing sampling of binding sites and solvated states.
Application of the method to benzamidine/trypsin and SC-558/cyclooxygenase 2 systems.
Calculation of binding free-energy surfaces and absolute binding free energies.

Main Results:

The X-ray conformation was identified as the lowest free-energy pose in both tested systems.
Computed binding free energies showed good agreement with experimental data.
Funnel metadynamics revealed alternative binding modes and the role of water molecules in binding.

Conclusions:

Funnel metadynamics provides an efficient and accurate method for characterizing protein-ligand binding free energy.
The approach offers valuable insights into binding mechanisms at an affordable computational cost.
This method is a promising tool for accelerating drug discovery and addressing diverse problems in chemistry and physics.