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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...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Related Experiment Video

Updated: Jun 23, 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

Prediction of functionally important sites from protein sequences using sparse kernel least squares classifiers.

Ke Tang1, Ganesan Pugalenthi, P N Suganthan

  • 1NICAL, Department of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China.

Biochemical and Biophysical Research Communications
|April 28, 2009
PubMed
Summary
This summary is machine-generated.

Predicting functionally important sites (FIS) in proteins is crucial. This study uses sparse kernel least squares classifiers (SKLSC) on protein sequences, achieving around 70% sensitivity for various FIS.

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A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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Last Updated: Jun 23, 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

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning in protein science

Background:

  • Identifying functionally important sites (FIS) in proteins is vital, especially when structural data is scarce.
  • Machine learning (ML) has shown success in classifying complex biological problems.
  • Predicting FIS aids in understanding protein function and designing new proteins.

Purpose of the Study:

  • To apply the sparse kernel least squares classifiers (SKLSC) approach for predicting FIS using protein sequence-derived features.
  • To evaluate the performance of SKLSC in classifying different types of FIS.
  • To assess the potential of sequence-based ML methods for FIS prediction.

Main Methods:

  • Utilized sparse kernel least squares classifiers (SKLSC) for classification.
  • Applied the SKLSC algorithm to 5435 FIS from 312 protein alignments.
  • Conducted a large-scale benchmarking study on 101 protein families (1899 FIS).

Main Results:

  • Achieved 68.28% sensitivity and 68.66% specificity on the training dataset.
  • Obtained 65.34% sensitivity and 66.88% specificity on the testing dataset.
  • Demonstrated an average of approximately 70% sensitivity in predicting active, metal, ligand, and protein binding sites.

Conclusions:

  • The SKLSC approach shows potential for predicting FIS using only protein sequence information.
  • Active and metal binding sites were found to be more predictable than ligand and protein binding sites.
  • This study highlights the utility of sequence-derived features and ML for FIS identification in the absence of structural data.