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Related Concept Videos

Protein Organization01:24

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence.
Protein Organization01:13

Protein Organization

Overview
Protein Organization01:24

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence.
Protein Organization01:13

Protein Organization

Overview
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...

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Related Experiment Video

Updated: May 13, 2026

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

An ensemble method for predicting subnuclear localizations from primary protein structures.

Guo Sheng Han1, Zu Guo Yu, Vo Anh

  • 1School of Mathematics and Computational Science, Xiangtan University, Xiangtan City, Hunan, China.

Plos One
|March 6, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel ensemble method for predicting protein subnuclear localization, improving accuracy for both single and multi-localized proteins. The developed system offers enhanced performance over existing approaches.

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Proteomics

Background:

  • Predicting protein subnuclear localization is crucial but challenging.
  • Previous methods using non-sequence information have limitations.
  • This study addresses these limitations by proposing novel feature extraction and ensemble methods.

Purpose of the Study:

  • To develop a novel individual feature extraction method.
  • To create an ensemble method for improved protein subnuclear localization prediction.
  • To utilize comprehensive information via high-dimensional feature vectors from 11 extraction methods.

Main Methods:

  • A novel two-stage multiclass support vector machine (SVM) was developed.
  • Feature extraction focused on amino acid classifications and physicochemical properties.
  • An automatic kernel parameter search method was employed for efficiency.

Main Results:

  • The method achieved 75.2% accuracy for 6 localizations on the Lei dataset and 72.1% for 9 localizations on the SNL9 dataset (leave-one-out cross-validation).
  • It demonstrated superior performance for both single- and multi-localization proteins compared to existing methods.
  • Accuracy improvements of 4.0%, 4.7%, and 6.5% were observed for single- and multi-localization proteins, respectively.

Conclusions:

  • The proposed method is effective and valuable for predicting protein subnuclear localization.
  • A web server implementing the method is available for public use.
  • The approach provides more balanced sensitivities and specificities across different localization sizes.