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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Conserved Binding Sites01:49

Conserved Binding Sites

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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Protein-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...
Ligand Binding Sites02:40

Ligand Binding Sites

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...
Ligand Binding Sites02:40

Ligand Binding Sites

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...
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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 the...

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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Regression applied to protein binding site prediction and comparison with classification.

Joachim Giard1, Jérôme Ambroise, Jean-Luc Gala

  • 1Communications and Remote Sensing Laboratory, Université Catholique de Louvain, Place du Levant 2, 1348 Louvain-la-Neuve, Belgium. joachim.giard@uclouvain.be

BMC Bioinformatics
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PubMed
Summary

Regression tools outperform classification for predicting protein binding sites. This study developed a flexible patches-based method, demonstrating regression

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Machine learning in protein science

Background:

  • Structural genomics yields numerous protein structures with unknown functions, necessitating automated function determination methods.
  • Protein function can be elucidated by analyzing physical interaction networks, making potential binding site identification crucial.
  • Existing binding site prediction methods predominantly rely on classification tools.

Purpose of the Study:

  • To demonstrate the superiority of regression tools over classification tools for patch-based binding site prediction.
  • To introduce a novel, flexible patches-based binding site localization method.
  • To compare the performance of the developed method against existing web server-based approaches.

Main Methods:

  • Development of a patches-based binding site localization method adaptable for both regression and classification.
  • Comparative analysis of regression tools versus machine learning classifiers using leave-one-out cross-validation.
  • Benchmarking the developed method (using Multilayer Perceptron) against three established web server methods.

Main Results:

  • Regression tools yielded superior predictive performance compared to classification tools in binding site prediction.
  • The Multilayer Perceptron, among regression tools, demonstrated the highest prediction accuracy.
  • The developed patches-based method showed comparable performance to existing web server-based prediction tools.

Conclusions:

  • Regression is a more effective approach than classification for the presented binding site localization method.
  • Implementing regression in lieu of classification for other binding site predictors is likely to enhance their performance.
  • The developed method's adjustable binding site size offers flexibility for managing false positive and negative rates.