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  • Towards sequence-based prediction of mutation-induced stability changes in unseen non-homologous proteins

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    Author(s)
    Folkman, Lukas
    Stantic, Bela
    Sattar, Abdul
    Griffith University Author(s)
    Stantic, Bela
    Sattar, Abdul
    Folkman, Lukas
    Year published
    2014
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    Abstract
    Background: Reliable prediction of stability changes induced by a single amino acid substitution is an important aspect of computational protein design. Several machine learning methods capable of predicting stability changes from the protein sequence alone have been introduced. Prediction performance of these methods is evaluated on mutations unseen during training. Nevertheless, different mutations of the same protein, and even the same residue, as encountered during training are commonly used for evaluation. We argue that a faithful evaluation can be achieved only when a method is tested on previously unseen proteins with ...
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    Background: Reliable prediction of stability changes induced by a single amino acid substitution is an important aspect of computational protein design. Several machine learning methods capable of predicting stability changes from the protein sequence alone have been introduced. Prediction performance of these methods is evaluated on mutations unseen during training. Nevertheless, different mutations of the same protein, and even the same residue, as encountered during training are commonly used for evaluation. We argue that a faithful evaluation can be achieved only when a method is tested on previously unseen proteins with low sequence similarity to the training set. Results: We provided experimental evidence of the limitations of the evaluation commonly used for assessing the prediction performance. Furthermore, we demonstrated that the prediction of stability changes in previously unseen non-homologous proteins is a challenging task for currently available methods. To improve the prediction performance of our previously proposed method, we identified features which led to over-fitting and further extended the model with new features. The new method employs Evolutionary And Structural Encodings with Amino Acid parameters (EASE-AA). Evaluated with an independent test set of more than 600 mutations, EASE-AA yielded a Matthews correlation coefficient of 0.36 and was able to classify correctly 66% of the stabilising and 74% of the destabilising mutations. For real-value prediction, EASE-AA achieved the correlation of predicted and experimentally measured stability changes of 0.51. Conclusions: Commonly adopted evaluation with mutations in the same protein, and even the same residue, randomly divided between the training and test sets lead to an overestimation of prediction performance. Therefore, stability changes prediction methods should be evaluated only on mutations in previously unseen non-homologous proteins. Under such an evaluation, EASE-AA predicts stability changes more reliably than currently available methods.
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    Journal Title
    BMC Genomics
    Volume
    15
    Issue
    Suppl 1
    DOI
    https://doi.org/10.1186/1471-2164-15-S1-S4
    Copyright Statement
    © 2014 Folkman et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
    Note
    Page numbers are not for citation purposes. Instead, this article has the unique article number of (Suppl 1):S4.
    Subject
    Biological sciences
    Information and computing sciences
    Biomedical and clinical sciences
    Publication URI
    http://hdl.handle.net/10072/61398
    Collection
    • Journal articles

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