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Related Concept Videos

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
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Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes02:16

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The present-day mitochondrial and chloroplast genomes have retained some of the characteristics of their ancestral prokaryotes and also have acquired new attributes during their evolution within eukaryotic cells. Like prokaryotic genomes, mitochondrial and chloroplast genomes neither bind with histone-like proteins nor show complex packaging into chromosome-like structures, as observed in eukaryotes. Unlike mitotic cell divisions observed in eukaryotic cells, mitochondria and chloroplasts...
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Protein Transport to the Outer Chloroplast Membrane01:11

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Chloroplast outer membrane proteins encoded by the nucleus are synthesized in the cytosol. Soon after synthesis, they bind cytosolic factors such as 14-3-3 protein and the Hsp70 chaperones that keep these precursors in an unfolded state until their translocation.
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Protein Transport to the Inner Chloroplast Membrane01:18

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Proteins targeted to the inner chloroplast membrane, or plastid proteins, are transported by two general pathways: the stop-transfer and the re-insertion or post-import pathways. Most plastid proteins carry N-terminal transit sequences and internal import sequences targeting it to the specific chloroplast subcompartment. Proteins targeted by the stop-transfer pathway have internal hydrophobic sequences that inhibit their translocation into the stroma. As a result, these precursors are arrested...
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An Effective Multi-Label Protein Sub-Chloroplast Localization Prediction by Skipped-Grams of Evolutionary Profiles

Sanjay Bankapur, Nagamma Patil

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |November 11, 2020
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    Summary

    This study introduces a new computational method using skip-grams and deep learning to accurately predict the multiple locations of proteins within chloroplasts. This approach significantly improves upon existing methods for protein sub-chloroplast localization (PSCL) prediction.

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    Area of Science:

    • Plant Biology
    • Cell Biology
    • Bioinformatics

    Background:

    • Identifying protein locations within chloroplasts is crucial for understanding their functions in algae and plant cells.
    • Experimental methods for determining Protein Sub-Chloroplast Localization (PSCL) are costly and time-consuming.
    • Existing computational predictors for multi-label PSCL have shown poor performance.

    Purpose of the Study:

    • To develop a novel computational model for accurate multi-label prediction of Protein Sub-Chloroplast Localization (PSCL).
    • To improve prediction accuracy beyond current state-of-the-art methods.

    Main Methods:

    • Utilized a novel skip-gram technique to extract discriminative patterns from evolutionary profiles.
    • Employed a multi-label deep neural network for PSCL prediction.
    • Evaluated the model on two public datasets: Benchmark and Novel.

    Main Results:

    • The proposed model significantly outperforms existing state-of-the-art multi-label PSCL predictors.
    • Achieved an enhanced Overall Actual Accuracy of at least 6.7% on the Benchmark dataset and 7.9% on the Novel dataset.
    • Statistical t-tests confirmed the significant performance improvement.

    Conclusions:

    • The developed model is an effective computational tool for solving the challenging multi-label PSCL prediction problem.
    • The improved accuracy offers a valuable resource for plant and algae cell biology research.
    • The prediction model is accessible via a web server for public use.