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Related Concept Videos

Ligand Binding Sites02:40

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.
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...
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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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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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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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The Equilibrium Binding Constant and Binding Strength02:18

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The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Persistent spectral hypergraph based machine learning (PSH-ML) for protein-ligand binding affinity prediction.

Xiang Liu1,2,3, Huitao Feng2,4, Jie Wu3,5

  • 1Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371.

Briefings in Bioinformatics
|April 10, 2021
PubMed
Summary
This summary is machine-generated.

We introduce persistent spectral hypergraph (PSH) based molecular descriptors for machine learning. This novel approach enhances protein-ligand binding affinity prediction, outperforming traditional methods.

Keywords:
Drug designHodge LaplacianMachine learningPersistent spectral hypergraph

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Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Bioinformatics

Background:

  • Molecular descriptors are crucial for quantitative structure-activity/property relationship (QSAR/QSPR) models and machine learning in chemical and biological data analysis.
  • Traditional descriptors often rely on molecular graph models, which may not fully capture complex molecular interactions.

Purpose of the Study:

  • To propose novel persistent spectral hypergraph (PSH) based molecular descriptors for the first time.
  • To apply these PSH descriptors in characterizing molecular structures and interactions for enhanced machine learning models.
  • To improve protein-ligand binding affinity prediction using PSH descriptors combined with gradient boosting tree (GBT) models.

Main Methods:

  • Developed a hypergraph-based topological representation for protein-ligand interactions.
  • Introduced a filtration process to generate nested hypergraphs at various scales.
  • Utilized the eigen spectrum information from (Hodge) Laplacian matrices of these hypergraphs.
  • Generated molecular descriptors from persistent attributes of the PSH analysis.
  • Integrated PSH descriptors with gradient boosting tree (GBT) models for affinity prediction.

Main Results:

  • PSH-based molecular descriptors demonstrated effective characterization of molecular structures and interactions.
  • The PSH-GBT model achieved superior performance in protein-ligand binding affinity prediction across multiple benchmark datasets (PDBbind-2007, PDBbind-2013, PDBbind-2016).
  • Results indicate that PSH-GBT outperforms existing machine learning models utilizing traditional molecular descriptors.

Conclusions:

  • Persistent spectral hypergraphs offer a powerful new framework for generating molecular descriptors.
  • The proposed PSH-based approach significantly enhances the accuracy of protein-ligand binding affinity prediction.
  • This method represents a notable advancement in applying topological data analysis to cheminformatics and drug discovery.