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Ligand Binding Sites02:40

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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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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.
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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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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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Recognizing ion ligand binding sites by SMO algorithm.

Shan Wang1, Xiuzhen Hu2, Zhenxing Feng1

  • 1College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China.

BMC Molecular and Cell Biology
|December 12, 2019
PubMed
Summary
This summary is machine-generated.

This study presents an efficient method for predicting ion ligand binding sites. The Sequential Minimal Optimization algorithm accurately identifies binding sites for acid radical and metal ion ligands, aiding protein function studies.

Keywords:
Binding siteIon ligandSMO algorithmSequence information

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Structural Biology

Background:

  • Protein function is crucial for life activities and relies on protein-ligand interactions.
  • Identifying ion ligand binding sites is vital for understanding protein function.

Purpose of the Study:

  • To develop an efficient method for predicting ion ligand binding sites.
  • To investigate the binding of various ion ligands to proteins.

Main Methods:

  • Utilized the Sequential Minimal Optimization (SMO) algorithm.
  • Employed sequence information for prediction.
  • Conducted 5-fold cross-validation for performance assessment.

Main Results:

  • Successfully predicted binding sites for four acid radical ions (nitrite, carbonate, sulfate, phosphate).
  • Accurately predicted binding sites for ten metal ions (e.g., Zn2+, Cu2+, Fe2+, Ca2+, Na+, K+).
  • Achieved improved prediction results through the proposed SMO-based method.

Conclusions:

  • An efficient and effective computational method for predicting ion ligand binding sites has been established.
  • The developed method aids in the study of protein-ligand interactions and protein function.