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Bioinformatics Methods in Predicting Amyloid Propensity of Peptides and Proteins

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Computer Simulations of Aggregation of Proteins and Peptides

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2340))

Abstract

Several computational methods have been developed to predict amyloid propensity of a protein or peptide. These bioinformatics tools are time- and cost-saving alternatives to expensive and laborious experimental methods which are used to confirm self-aggregation of a protein. Computational approaches not only allow preselection of reliable candidates for amyloids but, most importantly, are capable of a thorough and informative analysis of a protein, indicating the sequence determinants of protein aggregation, identifying the potential causal mutations and likely mechanisms. Bioinformatics modeling applies several different approaches, which most typically include physicochemical or structure-based modeling, machine learning, or statistics based modeling. Bioinformatics methods typically use the amino acid sequence of a protein as an input, some also include additional information, for example, an available structure. This chapter describes the methods currently used to computationally predict amyloid propensity of a protein or peptide. Since the accuracy of bioinformatics methods may be highly dependent on reference data used to develop and evaluate the predictors, we also briefly present the main databases of amyloids used by the authors of bioinformatics tools.

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Acknowledgment

This work was partially supported by the National Science Centre, Poland, Grant 2019/35/B/NZ2/03997.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland

    Małgorzata Kotulska & Jakub W. Wojciechowski

Authors
  1. Małgorzata Kotulska
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  2. Jakub W. Wojciechowski
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Corresponding author

Correspondence to Małgorzata Kotulska .

Editor information

Editors and Affiliations

  1. Division of Theoretical Physics, Polish Academy of Sciences, Warsaw, Poland

    Mai Suan Li

  2. Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, OH, USA

    Andrzej Kloczkowski

  3. Instutute of Physics, Polish Academy of Sciences, Warsaw, Poland

    Marek Cieplak

  4. Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, OH, USA

    Maksim Kouza

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Kotulska, M., Wojciechowski, J.W. (2022). Bioinformatics Methods in Predicting Amyloid Propensity of Peptides and Proteins. In: Li, M.S., Kloczkowski, A., Cieplak, M., Kouza, M. (eds) Computer Simulations of Aggregation of Proteins and Peptides . Methods in Molecular Biology, vol 2340. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1546-1_1

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  • DOI: https://doi.org/10.1007/978-1-0716-1546-1_1

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1545-4

  • Online ISBN: 978-1-0716-1546-1

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