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Ligand Binding Sites

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...

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Related Experiment Video

Updated: Jun 26, 2026

Modeling Ligands into Maps Derived from Electron Cryomicroscopy

Modeling Ligands into Maps Derived from Electron Cryomicroscopy

Published on: July 19, 2024

Ultrafast shape recognition: evaluating a new ligand-based virtual screening technology.

Pedro J Ballester¹, Paul W Finn, W Graham Richards

¹Physical & Theoretical Chemistry Laboratory, Oxford University, South Parks Road, Oxford OX1 3QZ, UK. pedro.ballester@gmail.com

Journal of Molecular Graphics & Modelling

|February 4, 2009

Summary

This summary is machine-generated.

Ultrafast Shape Recognition (USR) is a novel, rapid method for molecular shape comparison. It outperforms existing tools in virtual screening, accelerating drug discovery by enabling faster searches of chemical space for active compounds.

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Area of Science:

Computational Chemistry
Drug Discovery
Cheminformatics

Background:

Large-scale database searching is crucial for identifying biologically active molecules in drug discovery.
Molecular shape is a key property for predicting biological activity, but existing comparison methods are slow.
Faster and more reliable methods for molecular shape comparison are highly desirable.

Purpose of the Study:

To evaluate the performance of Ultrafast Shape Recognition (USR) in virtual screening for drug discovery.
To assess USR's ability to retrieve biologically active compounds compared to existing methods.

Main Methods:

Implemented and tested the non-superposition based Ultrafast Shape Recognition (USR) method.
Conducted retrospective Virtual Screening experiments using USR.
Compared USR's performance and computational speed against a commercial shape similarity method.

Main Results:

USR demonstrated superior performance on average in retrieving biologically active compounds.
USR screened molecular conformers over 2500 times faster than the commercial method.
USR offers a significant advancement in computational performance for molecular shape comparison.

Conclusions:

USR is a highly efficient and effective tool for virtual screening in drug discovery.
Its speed allows for searching significantly larger chemical spaces, enhancing lead molecule identification.
USR represents a valuable new asset for accelerating drug discovery programs.