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Protein Tetrahedral Networks by Invariant Shape Coordinates

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Computational Science and Its Applications – ICCSA 2023 Workshops (ICCSA 2023)

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Abstract

Representations of large molecules, typically proteins or DNA, for classification purposes, are commonly obtained by molecular networks. These are usually made up by a set of specific atoms (e.g. C\(_\alpha \) atoms in the amino acids) of the molecule plus a neighbourhood criterium, that establishes links between the centers, so building the network. The main objectve of such approaches is the discrimination of structures, the induction of grouping of them, depending on the basis of structural molecular properties. Here we propose applications of invariant shape coordinates as parameters for the construction of enhanced molecular networks for protein classification.

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Acknowledgments

Thanks are due to the Dipartimento di Chimica, Biologia e Biotecnologie dell’Università di Perugia (FRB, Fondo per la Ricerca di Base 2019 and 2020). A. L. and N. F. L. acknowledge financial support from Fondazione Cassa di Risparmio di Perugia, n.# 19839.2020.0513. A.L. thanks the OU Supercomputing Center for Education & Research (OSCER) at the University of Oklahoma, and the Italian GARR for allocated computing time.

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Authors and Affiliations

  1. Dipartimento di Chimica, Biologia e Biotecnologie, Università di Perugia, Perugia, Italy

    Lombardi Andrea, Noelia Faginas-Lago & Leonardo Pacifici

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  1. Lombardi Andrea
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  2. Noelia Faginas-Lago
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  3. Leonardo Pacifici
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Correspondence to Lombardi Andrea .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Basilicata, Potenza, Italy

    Francesco Scorza

  6. University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

  7. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

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Andrea, L., Faginas-Lago, N., Pacifici, L. (2023). Protein Tetrahedral Networks by Invariant Shape Coordinates. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14111. Springer, Cham. https://doi.org/10.1007/978-3-031-37126-4_9

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