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Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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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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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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Updated: Feb 17, 2026

Ion Mobility-Mass Spectrometry Techniques for Determining the Structure and Mechanisms of Metal Ion Recognition and Redox Activity of Metal Binding Oligopeptides
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mFASD: a structure-based algorithm for discriminating different types of metal-binding sites.

Wei He1, Zhi Liang1, Maikun Teng1

  • 1Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, Anhui, China.

Bioinformatics (Oxford, England)
|February 5, 2015
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Summary

A new algorithm, mFASD, effectively distinguishes various metal-binding sites in proteins using 3D structures. This computational method enhances the accuracy of predicting essential metal-binding sites for protein stability and function.

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Area of Science:

  • Proteomics
  • Structural Biology
  • Bioinformatics

Background:

  • Proteins utilize metal ions for stability and biological function.
  • Computational identification of metal-binding sites is crucial when experimental data is limited.
  • Existing methods primarily distinguish metal-binding from non-binding sites, lacking discrimination between site types.

Purpose of the Study:

  • To develop a novel algorithm for discriminating between different types of metal-binding sites.
  • To improve the accuracy of computational metal-binding site prediction.

Main Methods:

  • Proposed a new algorithm, mFASD (metal-binding Functional Atom Set Discrimination).
  • mFASD analyzes the local chemical environment of functional atoms interacting with metal ions.
  • A distance measure on functional atom sets facilitates comparison between metal-binding sites.

Main Results:

  • mFASD effectively discriminates between various metal-binding site types with high sensitivity and accuracy.
  • The algorithm leverages 3D structure data for prediction.
  • Combining mFASD with existing methods significantly improves overall prediction accuracy.

Conclusions:

  • mFASD offers a powerful computational approach for classifying metal-binding sites.
  • This method advances the accurate prediction of functionally important metal-binding sites in proteins.
  • The algorithm is freely available for research use.