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Protein Networks02:26

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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.
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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...
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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...
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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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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.
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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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An Integrated Approach for Microprotein Identification and Sequence Analysis
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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Protein Sequence Analysis by Proximities.

Frank-Michael Schleif1

  • 1School of Computer Science, University of Birmingham, Birmingham, Edgbaston, B15 2TT, UK. schleify@cs.bham.ac.uk.

Methods in Molecular Biology (Clifton, N.J.)
|November 1, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces novel mathematical strategies for analyzing nonstandard biological sequence data. It offers accurate methods for clustering, classification, and embedding protein sequence scores, moving beyond ad hoc solutions.

Keywords:
Indefinite-kernelMachine learningProtein sequence analysisProximity data

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Biological sequence data, often variable in length, presents analysis challenges due to nonstandard formats.
  • Standard data analysis methods struggle with nonmetric score functions common in sequence data.

Purpose of the Study:

  • To provide mathematically accurate strategies for analyzing nonstandard sequence score data.
  • To offer alternatives to common ad hoc solutions in biological sequence analysis.

Main Methods:

  • Exploration of recoding concepts for sequence score data.
  • Development of algorithms for clustering, classification, and embedding tasks.
  • Application focused on protein sequence data.

Main Results:

  • Demonstration of mathematically sound approaches for sequence data analysis.
  • Framework for handling nonmetric score functions in biological sequences.

Conclusions:

  • The proposed strategies offer a robust framework for analyzing biological sequence data.
  • The methods are applicable beyond protein sequences, enhancing broader biological data interpretation.