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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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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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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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A Protocol for Computer-Based Protein Structure and Function Prediction
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Classification of ligand molecules in PDB with graph match-based structural superposition.

Clara Shionyu-Mitsuyama1, Atsushi Hijikata1, Toshiyuki Tsuji1

  • 1Department of Bioscience, Nagahama Institute of Bio-science and Technology, 1266 Tamura, Nagahama, 526-0829, Japan.

Journal of Structural and Functional Genomics
|December 25, 2016
PubMed
Summary
This summary is machine-generated.

The COMPLIG algorithm was enhanced with structural superposition for small molecule ligand classification. This improved method in the Protein Data Bank (PDB) resulted in more precise clustering of functionally related ligands.

Keywords:
BioinformaticsDrug designGraph matchProtein ligandStructural superposition

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Area of Science:

  • Computational chemistry
  • Structural bioinformatics
  • Cheminformatics

Background:

  • Accurate classification of small molecule ligands is crucial for drug discovery and understanding biological processes.
  • Existing methods for ligand classification may not fully capture structural nuances, leading to suboptimal grouping.

Purpose of the Study:

  • To improve the COMPLIG algorithm for small molecule ligand classification by incorporating structural superposition.
  • To re-classify ligands in the Protein Data Bank (PDB) using the enhanced algorithm and compare the results with previous methods.

Main Methods:

  • The COMPLIG algorithm was modified to include a structural superposition process for atom-atom matching verification.
  • 16,660 small molecule ligands from the PDB were classified based on their 3D structures using the enhanced COMPLIG.

Main Results:

  • The enhanced COMPLIG algorithm classified 16,660 PDB ligands into 7561 clusters, compared to 3371 clusters using the previous method.
  • The new classification system showed an increase in singleton clusters, highlighting differences in chirality, conformation, substructures, and bond length.
  • Superposition-based classification effectively grouped functionally related ligands, such as drugs targeting specific biological processes.

Conclusions:

  • Structural superposition significantly refines small molecule ligand classification by enforcing stricter atom-atom matching.
  • The enhanced COMPLIG algorithm provides a more detailed and functionally relevant categorization of ligands.
  • This improved classification aids in identifying ligands with specific biological activities and structural characteristics.