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Related Concept Videos

Protein Families02:47

Protein Families

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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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Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
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Conservation of Protein Domains Over Different Proteins02:26

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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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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
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A New Alignment-Free Approach to Compare Protein Secondary Structure Families Through SSEs.

Debrupa Pal, Papri Ghosh, Subhram Das

    IEEE Transactions on Computational Biology and Bioinformatics
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    PubMed
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    This study introduces an alignment-free method to compare protein structures using secondary structural elements (SSEs). The Manhattan distance metric proved most effective for classifying protein families.

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    Area of Science:

    • Structural bioinformatics
    • Protein classification
    • Computational biology

    Background:

    • Protein secondary structure elements (SSEs) like helix (H), strand (E), and coil (C) are fundamental to protein structure and function.
    • Comparing protein families requires robust methods to interpret structural similarities and dissimilarities.

    Purpose of the Study:

    • To develop a novel alignment-free approach for comparing protein families based on SSEs.
    • To quantify structural similarity and dissimilarity among protein SSEs of varying lengths.

    Main Methods:

    • Numerical representation of SSEs using a four-component descriptor derived from the moment of inertia.
    • Computation of distance matrices using Manhattan, Standard, Euclidean, and Maximum distance metrics.
    • Phylogenetic tree construction via the Neighbor-Joining (NJ) method and evaluation on SCOP benchmark datasets.

    Main Results:

    • The Manhattan distance metric demonstrated superior reliability in capturing relationships between protein structural families.
    • The proposed alignment-free method shows robustness and effectiveness compared to existing approaches.
    • Successful evaluation across diverse protein superfamilies and datasets of varying lengths.

    Conclusions:

    • The developed descriptor and comparative framework offer a reliable, quantitative method for assessing structural similarity in protein SSEs.
    • This alignment-free approach provides a valuable tool for structural bioinformatics and protein classification.
    • The study highlights the significance of SSEs in understanding protein structural diversity and relationships.