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ProSPs: Protein Sites Prediction Based on Sequence Fragments

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Machine Learning, Optimization, and Data Science (LOD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13163))

Abstract

Identifying interacting sites of proteins is a relevant aspect for drug and vaccine design, and it provides clues for understanding the protein function. Although such a prediction is a problem extensively addressed in the literature, just a few approaches consider the protein sequence only. The use of the protein sequences is an important issue because the three-dimensional structure of proteins could be unknown. Moreover, such a structural determination experimentally is expensive and time-consuming, and it may contain errors due to experimentation. On the other hand, sequence based method suffers when the knowledge of sequence is incomplete.

In this work, we present ProSPs, a method for predicting the protein residues considering protein sequence fragments, which are obtained using sliding windows and become the samples for an unbalance binary classification problem. We use the Random Forest classifier for data training. Each amino acid is enriched using a selected subset of physicochemical and biochemical amino acid characteristics from the AAIndex1 database. We test the framework on two classes of proteins, Antibody-Antigen and Antigen-Bound Antibody, extracted from the Protein-Protein Docking Benchmark 5.0. The obtained results evaluated in terms of the area under the ROC curve (AU-ROC) on these classes outperform the sequence-based algorithms in the literature and are comparable with the ones based on three-dimensional structure.

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Author information

Authors and Affiliations

  1. Department of Information Engineering, University of Padova, Via Gradenigo 6/A, 35131, Padova, PD, Italy

    Massimo Cavallin & Carlo Ferrari

  2. School of Science and Technology, University of Camerino, Via Madonna delle Carceri, 9, Camerino, MC, Italy

    Michela Quadrini

  3. Sorint.Tek, Sorint.LAB group, Via Giovanni Savelli 102, 35129, Padova, PD, Italy

    Sebastian Daberdaku

Authors
  1. Michela Quadrini
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  2. Massimo Cavallin
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  3. Sebastian Daberdaku
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  4. Carlo Ferrari
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Corresponding author

Correspondence to Michela Quadrini .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. Department of Computer Science, University of Reading, Reading, UK

    Varun Ojha

  3. Department of Computer Science, University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. Cambridge Judge Business School, University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. Department of Biochemistry, University of Cambridge, Cambridge, UK

    Giorgio Jansen

  6. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Department of Informatics, Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Quadrini, M., Cavallin, M., Daberdaku, S., Ferrari, C. (2022). ProSPs: Protein Sites Prediction Based on Sequence Fragments. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2021. Lecture Notes in Computer Science(), vol 13163. Springer, Cham. https://doi.org/10.1007/978-3-030-95467-3_41

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  • DOI: https://doi.org/10.1007/978-3-030-95467-3_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95466-6

  • Online ISBN: 978-3-030-95467-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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