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  • EASE-MM: Sequence-Based Prediction of Mutation-Induced Stability Changes with Feature-Based Multiple Models

    Author(s)
    Folkman, Lukas
    Stantic, Bela
    Sattar, Abdul
    Zhou, Yaoqi
    Griffith University Author(s)
    Stantic, Bela
    Sattar, Abdul
    Folkman, Lukas
    Year published
    2016
    Metadata
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    Abstract
    Protein engineering and characterisation of non-synonymous single nucleotide variants (SNVs) require accurate prediction of protein stability changes (ΔΔGu) induced by single amino acid substitutions. Here, we have developed a new prediction method called Evolutionary, Amino acid, and Structural Encodings with Multiple Models (EASE-MM), which comprises five specialised support vector machine (SVM) models and makes the final prediction from a consensus of two models selected based on the predicted secondary structure and accessible surface area of the mutated residue. The new method is applicable to single-domain monomeric ...
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    Protein engineering and characterisation of non-synonymous single nucleotide variants (SNVs) require accurate prediction of protein stability changes (ΔΔGu) induced by single amino acid substitutions. Here, we have developed a new prediction method called Evolutionary, Amino acid, and Structural Encodings with Multiple Models (EASE-MM), which comprises five specialised support vector machine (SVM) models and makes the final prediction from a consensus of two models selected based on the predicted secondary structure and accessible surface area of the mutated residue. The new method is applicable to single-domain monomeric proteins and can predict ΔΔGu with a protein sequence and mutation as the only inputs. EASE-MM yielded a Pearson correlation coefficient of 0.53–0.59 in 10-fold cross-validation and independent testing and was able to outperform other sequence-based methods. When compared to structure-based energy functions, EASE-MM achieved a comparable or better performance. The application to a large dataset of human germline non-synonymous SNVs showed that the disease-causing variants tend to be associated with larger magnitudes of ΔΔGu predicted with EASE-MM. The EASE-MM web-server is available at http://sparks-lab.org/server/ease.
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    Journal Title
    Journal of Molecular Biology
    Volume
    428
    Issue
    6
    DOI
    https://doi.org/10.1016/j.jmb.2016.01.012
    Subject
    Medicinal and biomolecular chemistry
    Biochemistry and cell biology
    Biochemistry and cell biology not elsewhere classified
    Microbiology
    Publication URI
    http://hdl.handle.net/10072/99141
    Collection
    • Journal articles

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