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A Protocol for Computer-Based Protein Structure and Function Prediction
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Pr[m]: An Algorithm for Protein Motif Discovery.

Rahul Semwal, Imlimaong Aier, Utkarsh Raj

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |August 6, 2020
    PubMed
    Summary
    This summary is machine-generated.

    A new k-mer based method, Pr[m], efficiently detects statistically significant protein motifs of variable lengths. This computational biology tool outperforms existing methods like MEME and GLAM2 in predictive quality and performance.

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    Area of Science:

    • Computational biology
    • Bioinformatics
    • Structural biology

    Background:

    • Evolutionarily conserved motifs are crucial for protein structure and function.
    • Identifying these motifs in protein sequences is a key challenge in bioinformatics.
    • Existing algorithms struggle with discovering variable-length motifs due to fixed parameter dependencies.

    Purpose of the Study:

    • To propose a novel k-mer based approach, Pr[m], for identifying statistically significant, un-gapped motif patterns.
    • To address the challenge of discovering motifs with variable lengths.
    • To evaluate the performance of Pr[m] against established motif discovery tools.

    Main Methods:

    • Developed a k-mer based motif discovery algorithm named Pr[m].
    • The approach detects un-gapped motif patterns, accommodating wildcard characters.
    • Comparative analysis performed using a dataset of 7,500 protein sequences.

    Main Results:

    • Pr[m] demonstrated superior performance compared to MEME and GLAM2.
    • The proposed method achieved higher predictive quality.
    • Pr[m] showed improved overall performance in motif discovery.

    Conclusions:

    • Pr[m] offers an effective solution for identifying variable-length protein motifs.
    • The tool provides enhanced accuracy and performance in motif pattern detection.
    • Pr[m] is publicly available for researchers at https://bioserver.iiita.ac.in/Pr[m].