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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...
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...
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 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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Related Experiment Video

Updated: Jun 1, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Predicting metal-binding sites from protein sequence.

Andrea Passerini1, Marco Lippi, Paolo Frasconi

  • 1University of Trento, Trento.

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|May 25, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for predicting metal-binding sites in proteins using sequence data. The approach leverages structured-output learning and matroid theory for efficient and accurate identification of these crucial functional sites.

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Last Updated: Jun 1, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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Published on: November 3, 2011

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
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Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

Area of Science:

  • Computational biology
  • Bioinformatics
  • Structural biology

Background:

  • Determining protein function from genomic sequences is crucial for understanding biological systems.
  • Predicting metal-binding sites from sequence alone is challenging due to combinatorial complexity.
  • Existing methods often rely on 3D structures or limited pattern matching, hindering discovery of novel sites.

Purpose of the Study:

  • To develop a novel computational method for predicting transition-metal-binding sites using only protein sequence information.
  • To address the limitations of existing sequence-based prediction methods for novel binding site identification.
  • To apply structured-output learning and matroid theory for efficient and accurate binding site prediction.

Main Methods:

  • Developed a structured-output learning framework for predicting metal-binding sites coordinated by cysteines and histidines.
  • Utilized matroid theory to simplify the inference problem, enabling an efficient greedy algorithm.
  • Validated the predictor on a stringent dataset with distinct training and testing SCOP folds.

Main Results:

  • Achieved 56% precision and 60% recall in identifying ligand-ion bonds.
  • Demonstrated the effectiveness of the matroid-based approach for intractable inference problems.
  • Successfully predicted metal-binding sites in a cross-domain setting, indicating generalizability.

Conclusions:

  • The developed method offers a significant advancement in sequence-based prediction of metal-binding sites.
  • Structured-output learning combined with matroid theory provides an efficient and accurate solution.
  • This approach has the potential to accelerate functional annotation of uncharacterized proteins on a genomic scale.