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Updated: Dec 12, 2025

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Engineering a Histone Reader Protein by Combining Directed Evolution, Sequencing, and Neural Network Based Ordinal

Jonathan Parkinson, Ryan Hard, Richard I Ainsworth

    Journal of Chemical Information and Modeling
    |August 14, 2020
    PubMed
    Summary

    We introduce ordinal regression to predict protein mutants with improved binding affinity. This method accelerates protein engineering by reducing selection rounds and identifying novel strong binders.

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    Area of Science:

    • Protein engineering
    • Computational biology
    • Biochemistry

    Background:

    • Directed evolution enhances protein affinity/specificity but is laborious and samples limited sequence space.
    • Current methods require extensive screening and mutagenesis over many rounds.

    Purpose of the Study:

    • To develop a novel computational approach using ordinal regression to predict protein mutants with enhanced binding affinity.
    • To reduce the number of selection rounds needed in protein engineering.
    • To identify strong binders outside the initial mutant library.

    Main Methods:

    • Ordinal regression modeling of protein sequence data from directed evolution sorting.
    • Contextual regression for interpreting nonlinear model predictions.
    • Experimental validation of predicted high-affinity mutants.

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    Main Results:

    • Ordinal regression models trained on minimal data (two sorts) accurately predicted chromodomain CBX1 mutants with higher binding affinity for H3K9me3 peptide.
    • Feature extraction via contextual regression identified key predictive elements.
    • Successfully guided the discovery of potent binders not present in the original library.
    • Achieved comparable binding affinity improvements to traditional, more intensive directed evolution methods.

    Conclusions:

    • Ordinal regression offers a more efficient alternative to traditional directed evolution for protein engineering.
    • This approach significantly reduces the effort required to isolate proteins with desired binding properties.
    • Enables the identification of novel, high-affinity protein variants through predictive modeling.