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Related Concept Videos

Membrane Fluidity01:23

Membrane Fluidity

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Cell membranes are composed of phospholipids, proteins, and carbohydrates loosely attached to one another through chemical interactions. Molecules are generally able to move about in the plane of the membrane, giving the membrane its flexible nature called fluidity. Two other features of the membrane contribute to membrane fluidity: the chemical structure of the phospholipids and the presence of cholesterol in the membrane.
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Conserved Binding Sites01:49

Conserved Binding Sites

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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.
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...
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Membrane Fluidity01:26

Membrane Fluidity

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Membrane fluidity is explained by the fluid mosaic model of the cell membrane, which describes the plasma membrane structure as a mosaic of components—including phospholipids, cholesterol, proteins, and carbohydrates—that gives the membrane a fluid character.
Mosaic nature of the membrane
The mosaic characteristic of the membrane helps the plasma membrane remain fluid. The integral proteins and lipids exist as separate but loosely-attached molecules in the membrane. The membrane is...
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Protein Folding Quality Check in the RER01:29

Protein Folding Quality Check in the RER

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ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
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¹H NMR of Conformationally Flexible Molecules: Variable-Temperature NMR01:15

¹H NMR of Conformationally Flexible Molecules: Variable-Temperature NMR

1.6K
The axial and equatorial protons in cyclohexane can be distinguished by performing a variable-temperature NMR experiment. In this process, except for one proton, the remaining eleven protons are replaced by deuterium. The deuterium substitution avoids the possible peak splitting caused by the spin-spin coupling between the adjacent protons. The remaining proton flips between the axial and equatorial positions.
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¹H NMR of Conformationally Flexible Molecules: Temporal Resolution00:52

¹H NMR of Conformationally Flexible Molecules: Temporal Resolution

1.2K
At room temperature, the chair conformer of cyclohexane undergoes rapid ring flipping between two equivalent chair conformers at a rate of approximately 105 times per second. These two chair conformers are in equilibrium. The rapid ring flipping results in the interconversion of the axial proton to an equatorial proton and an equatorial to the axial proton. Such interconversions are too rapid and cannot be detected on the NMR timescale. Hence, the NMR spectrometer cannot distinguish between the...
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Related Experiment Videos

RNA Flexibility Prediction With Sequence Profile and Predicted Solvent Accessibility.

Hong Wei, Boling Wang, Jianyi Yang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |December 4, 2019
    PubMed
    Summary
    This summary is machine-generated.

    We developed RNAbval, a new method for predicting RNA B-factors, which are crucial for understanding structural flexibility. RNAbval significantly outperforms existing methods, offering a valuable tool for RNA structure analysis.

    Related Experiment Videos

    Area of Science:

    • Structural biology
    • Bioinformatics
    • Computational chemistry

    Background:

    • Structural flexibility is vital for biological processes.
    • B-factor quantifies protein and RNA flexibility.
    • Existing methods for RNA B-factor prediction are limited.

    Purpose of the Study:

    • To develop an accurate method for predicting RNA B-factors.
    • To improve upon the state-of-the-art in RNA B-factor prediction.

    Main Methods:

    • A novel method, RNAbval, was developed.
    • Random forest algorithm was employed.
    • Features included sequence profile and predicted solvent accessibility.

    Main Results:

    • RNAbval demonstrated significant improvement over existing methods.
    • Performance gains ranged from 9.2% to 20.5% on benchmark datasets.

    Conclusions:

    • RNAbval offers a more accurate approach to predicting RNA B-factors.
    • The method provides a valuable tool for RNA structural analysis.