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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

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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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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 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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Binding sites linkages can regulate a protein's function.  For example, enzyme activity is often regulated through a feedback mechanism where the end product of the biochemical process serves as an inhibitor.
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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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Updated: May 22, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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ProtCB-bind: Protein-carbohydrate binding site prediction using an ensemble of classifiers.

Divnesh Prasad1, Ronesh Sharma2, M G M Khan1

  • 1School of Information Technology, Engineering, Mathematics and Physics, The University of the South Pacific, Suva, Fiji.

Carbohydrate Research
|March 14, 2025
PubMed
Summary

This study introduces ProtCB-Bind, a new computational model that accurately predicts protein-carbohydrate interactions. This tool enhances understanding of essential biomolecular binding events for biological functions.

Keywords:
Classifier ensembleProbability based predictionProtein-carbohydrateSequence based technique

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Bioinformatics

Background:

  • Proteins and carbohydrates are fundamental biomolecules essential for numerous biological processes, including immune response and energy storage.
  • Identifying protein-carbohydrate binding regions is crucial for understanding these vital interactions.
  • Current computational methods require improvement for accurate prediction of these interactions.

Purpose of the Study:

  • To develop and validate ProtCB-Bind, a novel computational model for predicting protein-carbohydrate interactions.
  • To leverage advanced machine learning and Natural Language Processing (NLP) techniques for enhanced prediction accuracy.
  • To provide an accessible tool for researchers studying protein-carbohydrate binding.

Main Methods:

  • ProtCB-Bind employs an ensemble of machine learning classifiers with a common averaging approach for predictions.
  • The model is trained using sequence-based, evolutionary-based protein features, and amino acid physicochemical properties.
  • Transformer-based NLP features are integrated to improve predictive performance.

Main Results:

  • ProtCB-Bind was systematically optimized for classifier and feature combinations.
  • The model demonstrated superior performance compared to existing state-of-the-art predictors like SPRINT-CBH, StackCB-Pred, and StackCB-Embed.
  • An approximate 3% improvement in overall performance was achieved on a benchmark dataset.

Conclusions:

  • ProtCB-Bind represents a significant advancement in computational prediction of protein-carbohydrate interactions.
  • The model's enhanced accuracy and integration of NLP features offer valuable insights into biomolecular recognition.
  • The source code is publicly available, facilitating further research and application in the field.