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Ligand Binding Sites

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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.
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Ligand Binding Sites

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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...

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Related Experiment Video

Updated: Apr 17, 2026

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

Computational methods for fragment-based ligand design: growing and linking.

Rachelle J Bienstock¹

¹Independent Researcher and Consultant, 300 Pitch Pine Lane, Chapel Hill, NC, 27514, USA, rachelleb1@gmail.com.

Methods in Molecular Biology (Clifton, N.J.)

|February 25, 2015

Summary

Fragment-based drug design enhances chemical exploration for novel compounds. Computational methods and ADME data integration are key for designing selective fragments with improved activity profiles.

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Area of Science:

Medicinal Chemistry
Computational Drug Discovery

Background:

Fragment-based drug design (FBDD) is a powerful strategy for exploring chemical space.
FBDD enables the discovery of novel lead compounds and drug candidates.

Purpose of the Study:

To highlight the role of computational techniques in FBDD.
To emphasize the importance of ADME data and property clustering in fragment design.

Main Methods:

Utilizing computational methods for fragment identification and linking.
Integrating Absorption, Distribution, Metabolism, and Excretion (ADME) data.
Clustering chemical properties, toxicophores, and chemotypes.

Main Results:

Computational approaches significantly aid in linking, growing, and evolving fragments.
ADME studies and property clustering facilitate the design of selective fragments.
The integration of these methods leads to fragments with favorable activity profiles.

Conclusions:

FBDD, supported by computational tools and ADME data, is crucial for modern drug discovery.
The strategic design of fragments can yield potent and selective drug candidates.
Optimizing fragments for activity and safety profiles is achievable through integrated approaches.