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Related Concept Videos

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.
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Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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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.
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Protein Organization01:24

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Protein and Protein Structure

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Related Experiment Video

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A New Approach for the Comparative Analysis of Multiprotein Complexes Based on 15N Metabolic Labeling and Quantitative Mass Spectrometry
08:04

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Published on: March 13, 2014

Comparison study on k-word statistical measures for protein: from sequence to 'sequence space'.

Qi Dai1, Tianming Wang

  • 1Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, PR China. daiailiu2004@yahoo.com.cn

BMC Bioinformatics
|September 25, 2008
PubMed
Summary

This study introduces a novel protein sequence space for improved comparison. Utilizing this space enhances statistical measures like gsm.k and gre.k for better protein classification and phylogenetic analysis.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Traditional protein sequence comparison relies on k-word frequencies.
  • Existing methods do not leverage information from related protein sequences.
  • This study addresses the gap by incorporating related sequence data.

Purpose of the Study:

  • To propose a novel 'sequence space' construction method using related protein sequences.
  • To evaluate the performance of statistical measures within this 'sequence space'.
  • To introduce and assess two new statistical measures: gre.k and gsm.k.

Main Methods:

  • Construction of a protein 'sequence space' incorporating related sequences.
  • Systematic comparison of statistical measures with and without 'sequence space' information.
  • Evaluation using Receiver Operating Curve (ROC) analysis for classification.
  • Assessment of measures in phylogenetic analysis.

Main Results:

  • The 'sequence space' approach significantly improves statistical measure performance, especially for less redundant data.
  • The novel gsm.k measure demonstrates high efficiency, followed by cos.k.
  • gre.k performs well on less redundant data, outperforming others in classification.
  • Gdis.k based on 'sequence space' proves reliable for phylogenetic analysis.

Conclusions:

  • Exploring 'sequence space' is a promising strategy to enhance protein comparison.
  • Novel measures like gsm.k and gre.k offer improved performance over existing methods.
  • Guidelines for using 'sequence space' and statistical measures are provided based on data redundancy.