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Related Concept Videos

Protein Families02:47

Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key locations, protein...
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: May 21, 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

POOL server: machine learning application for functional site prediction in proteins.

Srinivas Somarowthu1, Mary Jo Ondrechen

  • 1Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, USA.

Bioinformatics (Oxford, England)
|June 5, 2012
PubMed
Summary
This summary is machine-generated.

We developed a machine learning tool called POOL (partial order optimum likelihood) to predict catalytic residues in proteins using their 3D structures. This method combines electrostatic and geometric data, applicable to novel proteins without known similar structures.

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A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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Last Updated: May 21, 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
  • Structural bioinformatics
  • Machine learning in biochemistry

Background:

  • Identifying catalytic residues is crucial for understanding enzyme function and engineering new biocatalysts.
  • Existing methods often rely on sequence or structural homology, limiting their applicability to novel protein families.

Purpose of the Study:

  • To present a novel, automated web server for predicting catalytic residues in protein structures.
  • To introduce a machine learning approach that integrates diverse biophysical and structural features for accurate prediction.

Main Methods:

  • Developed the Partial Order Optimum Likelihood (POOL) web server.
  • Utilized THEMATICS for electrostatic analysis and ConCavity for geometric pocket identification as input features.
  • Incorporated optional evolutionary information from INTREPID for enhanced accuracy.

Main Results:

  • The POOL server accurately predicts catalytic residues by combining electrostatic and geometric data.
  • The method is applicable to proteins with novel folds and engineered proteins, as it does not require sequence or structural similarity.
  • Optional inclusion of evolutionary data further improves prediction accuracy.

Conclusions:

  • POOL provides a high-performance, automated solution for catalytic residue prediction from 3D protein structures.
  • The server is accessible and applicable to a wide range of proteins, including those with no known homologs.
  • This tool facilitates enzyme function elucidation and protein design.