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Turning Failures into Applications: The Problem of Protein ΔΔG Prediction

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Data Mining Techniques for the Life Sciences

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2449))

Abstract

After nearly two decades of research in the field of computational methods based on machine learning and knowledge-based potentials for ΔG and ΔΔG prediction upon variations, we now realize that all the approaches are poorly performing when tested on specific cases and that there is large space for improvement. Why this is so? Is it wrong the underlying assumption that experimental protein thermodynamics in solution reflects the thermodynamics of a single protein? Both machine learning and knowledge-based computational methods are rigorous and we know the solid theory behind. We are now in a critical situation, which suggests that predictions of protein instability upon variation should be considered with care. In the following, we will show how to cope with the problem of understanding which protein positions may be of interest for biotechnological and biomedical purposes. By applying a consensus procedure, we indicate possible strategies for the result interpretation.

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Author information

Authors and Affiliations

  1. Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy

    Rita Casadio, Castrense Savojardo, Emidio Capriotti & Pier Luigi Martelli

  2. Department of Medical Sciences, University of Torino, Turin, Italy

    Piero Fariselli

Authors
  1. Rita Casadio
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  2. Castrense Savojardo
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  3. Piero Fariselli
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  4. Emidio Capriotti
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  5. Pier Luigi Martelli
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Corresponding author

Correspondence to Rita Casadio .

Editor information

Editors and Affiliations

  1. University of Pavia, Pavia, Italy

    Oliviero Carugo

  2. Genome Institute and Bioinformatics Institute, Singapore, Singapore

    Frank Eisenhaber

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Casadio, R., Savojardo, C., Fariselli, P., Capriotti, E., Martelli, P.L. (2022). Turning Failures into Applications: The Problem of Protein ΔΔG Prediction. In: Carugo, O., Eisenhaber, F. (eds) Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol 2449. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2095-3_6

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  • DOI: https://doi.org/10.1007/978-1-0716-2095-3_6

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2094-6

  • Online ISBN: 978-1-0716-2095-3

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