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A Protocol for Computer-Based Protein Structure and Function Prediction
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Seq2seq Fingerprint with Byte-Pair Encoding for Predicting Changes in Protein Stability upon Single Point Mutation.

Keisuke Kawano, Satoshi Koide, Chie Imamura

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |April 5, 2019
    PubMed
    Summary

    We developed a new method using byte-pair encoding (BPE) and sequence-to-sequence (seq2seq) models to predict protein stability changes from mutations. This approach achieves state-of-the-art accuracy, even on unseen protein data.

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    Area of Science:

    • * Biotechnology and Bioinformatics
    • * Computational Biology and Protein Engineering

    Background:

    • * Protein stability is critical for industrial applications.
    • * Existing machine learning models for predicting protein stability changes from single point mutations have limitations.
    • * Accurate prediction of protein stability is essential for protein design and engineering.

    Purpose of the Study:

    • * To introduce a novel unsupervised descriptor for protein sequences to enhance stability prediction.
    • * To improve the accuracy of predicting protein stability changes caused by single point mutations.
    • * To enable efficient training of deep learning models for protein sequence analysis.

    Main Methods:

    • * Development of a novel unsupervised descriptor for protein sequences.
    • * Integration of a sequence-to-sequence (seq2seq) neural network model.
    • * Application of byte-pair encoding (BPE) for efficient protein sequence compression.

    Main Results:

    • * Byte-pair encoding (BPE) effectively compresses protein sequences into shorter representations.
    • * The proposed descriptor enables efficient training of the seq2seq model.
    • * The developed predictor achieved state-of-the-art accuracy on independent test datasets.

    Conclusions:

    • * The combination of BPE and seq2seq models offers a powerful new descriptor for protein sequences.
    • * This method significantly improves the accuracy of predicting protein stability changes due to mutations.
    • * The approach demonstrates high performance on proteins not encountered during training, indicating strong generalization capabilities.