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Related Concept Videos

Leaky Scanning02:28

Leaky Scanning

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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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Peptide Identification Using Tandem Mass Spectrometry01:33

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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Related Experiment Video

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DeepAVP: A Dual-Channel Deep Neural Network for Identifying Variable-Length Antiviral Peptides.

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    Antiviral peptides (AVPs) show promise for targeting viruses. A new deep learning model, DeepAVP, accurately predicts effective AVPs, advancing peptide-based antiviral research.

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    Area of Science:

    • Biochemistry
    • Computational Biology
    • Virology

    Background:

    • Antiviral peptides (AVPs) are experimentally validated to inhibit viral entry into host cells.
    • Utilizing AVPs presents a promising strategy for combating medically significant viral infections.

    Purpose of the Study:

    • To develop a novel dual-channel deep neural network ensemble for analyzing variable-length antiviral peptides.
    • To create an accurate predictor, DeepAVP, for identifying effective antiviral peptides.

    Main Methods:

    • A dual-channel deep neural network ensemble incorporating LSTM and CONV channels was proposed.
    • The model analyzes variable-length peptide sequences and fine-tunes substitution matrices for functional specificity.
    • The method was applied to a novel, experimentally verified dataset of AVPs.

    Main Results:

    • DeepAVP achieved state-of-the-art performance with 96% accuracy and 0.85 MCC.
    • The predictor significantly outperformed existing methods for identifying antiviral peptides.
    • The developed web server, DeepAVP, facilitates the prediction of effective AVPs.

    Conclusions:

    • DeepAVP demonstrates superior performance in predicting antiviral peptides.
    • The model contributes significantly to advancing peptide-based antiviral research.
    • DeepAVP offers a valuable tool for identifying novel therapeutic peptides against viral diseases.