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Related Concept Videos

Protein Organization01:24

Protein Organization

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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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Protein Organization01:13

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Overview
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Protein and Protein Structure02:15

Protein and Protein Structure

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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
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Newman Projections02:06

Newman Projections

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Different notations are used to represent the three-dimensional structure of molecules on two-dimensional surfaces. One of the most commonly used representations is the dash-wedge formula. The dashed wedges, solid wedges, and the plane lines indicate the groups situated behind the plane, coming out of the plane, and in the plane, respectively.
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Fischer Projections02:18

Fischer Projections

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Learning to draw Fischer projections of molecules and understanding their relevance plays a crucial role in the visual depiction of organic molecules. A Fischer projection is a two-dimensional projection on a planar surface to simplify the three-dimensional wedge–dash representation of molecules. This is especially helpful in the case of molecules with multiple chiral centers that can be difficult to draw. Here, all the bonds of interest are represented as horizontal or vertical lines. While...
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Protein and Protein Structures02:15

Protein and Protein Structures

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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
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Structural Class Classification of 3D Protein Structure Based on Multi-View 2D Images.

Chendra Hadi Suryanto, Hiroto Saigo, Kazuhiro Fukui

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |January 24, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel protein structure comparison method using 2D images, bypassing difficult 3D alignments. The new approach, utilizing subspace analysis, accurately classifies protein structures and outperforms existing alignment-based techniques.

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    Area of Science:

    • Structural Biology
    • Bioinformatics
    • Computational Biology

    Background:

    • Comparing 3D protein structures is crucial but challenging due to difficulties in defining optimal structural alignments for dissimilar proteins.
    • Conventional methods often rely on precise structural alignments, which can be problematic for proteins with significant structural variations.

    Purpose of the Study:

    • To develop a novel similarity measure for protein structure comparison that does not require precise structural alignment.
    • To evaluate the proposed method's effectiveness in classifying protein structures across different SCOP classes and folds.

    Main Methods:

    • Representing 3D protein structures as subspaces derived from sets of multi-view 2D images.
    • Calculating protein structure similarity using canonical angles between these subspaces.
    • Employing Grassmann Discriminant Analysis (GDA) for subspace-based learning within a classification framework.

    Main Results:

    • The proposed subspace-based method demonstrated superior performance in classifying seven SCOP structural classes compared to k-nearest neighbor (k-NN) using conventional alignment-based methods (CE, FATCAT, TM-align).
    • The method showed improved recognition of the HEM-binding four-helical bundle (f.21) fold in membrane proteins compared to TM-Align.

    Conclusions:

    • The novel subspace-based approach offers an effective alternative for protein structure comparison and classification, overcoming limitations of alignment-dependent methods.
    • This image-based subspace method provides a robust and accurate tool for analyzing protein structural diversity and function.