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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...

Ligand Binding Sites

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Ligand Binding and Linkage

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked. In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...

Ligand Binding and Linkage

Ligand Binding and Linkage

Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

Conserved Binding Sites

Conserved Binding Sites

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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

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Published on: June 20, 2025

Multiple grid arrangement improves ligand docking with unknown binding sites: Application to the inverse docking

Tomohiro Ban¹, Masahito Ohue², Yutaka Akiyama³

¹School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo 152-8550, Japan; Education Academy of Computational Life Sciences, Tokyo Institute of Technology, 2-12-1 W8-93 Ookayama, Meguro-ku, Tokyo 152-8550, Japan.

Computational Biology and Chemistry

|February 27, 2018

Summary

A new multiple grid arrangement method improves ligand docking accuracy for drug discovery. This computational approach enhances the prediction of drug-target interactions, though further refinement is needed for inverse docking applications.

Keywords:

Computational ligand docking Conformational search space Drug–target interactions Inverse docking Multiple grid arrangement Scoring function Structure-based drug design Virtual screening

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Area of Science:

Computational chemistry
Drug discovery and development
Bioinformatics

Background:

Accurate identification of drug-target interactions is crucial for drug discovery.
Existing computational methods, including ligand docking, have limitations in predicting binding affinity and target interactions.
A need exists for more precise docking methodologies to enhance virtual screening processes.

Purpose of the Study:

To propose and validate a novel computational method, termed multiple grid arrangement, to improve ligand docking accuracy.
To assess the efficacy of the multiple grid arrangement in enhancing conformational search within docking software.
To evaluate the method's performance in both standard docking and inverse docking scenarios.

Main Methods:

Developed a multiple grid arrangement technique to cover predicted ligand-binding sites.
Integrated the multiple grid arrangement into the Glide docking software via a modified cross-docking script (xglide.py).
Validated the method using the Astex diverse benchmark dataset and blind binding site predictions.

Main Results:

The multiple grid arrangement improved the correct prediction rate of the top scoring docking pose from 27.1% to 34.1% in re-docking experiments.
The method demonstrated facilitation of conformational search for grid-based ligand docking.
A slight improvement was observed in target prediction accuracy for inverse docking scenarios, highlighting current scoring function limitations.

Conclusions:

The proposed multiple grid arrangement method enhances ligand docking pose prediction accuracy.
The findings underscore the need for continued development of more accurate computational docking and scoring functions.
The developed method is available online for broader application in drug discovery research.