A machine learning approach to 3D protein structure sonification

Ronan, Isabel; Mi, Yanlin; Yallapragada, Venkata Vamsi Bharadwaj; Ó Nuanáin, Cárthach; Tabirca, Sabin

A machine learning approach to 3D protein structure sonification

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Files

IJMSTA_Paper_2.pdf(2.31 MB)

Published Version

Date

2024-01

Authors

Ronan, Isabel

Mi, Yanlin

Yallapragada, Venkata Vamsi Bharadwaj

Ó Nuanáin, Cárthach

Tabirca, Sabin

Publisher

Studio Musica Press

Published Version

https://doi.org/10.48293/IJMSTA-106

Abstract

Proteins are intricate structures that can be analysed by biologists and presented to the public using visualisations. However, with an increase in the amount of readily available protein-related information, new forms of data representation are needed. Sonification offers multiple advantages when conveying large amounts of complex data to interested audiences. Previous attempts have been made to sonify protein data; these techniques mainly focus on using amino acid sequences and secondary structures. This paper proposes a novel protein sonification algorithm involving atomic coordinates, B-factors, and occupancies to investigate new ways of displaying 3D protein structure data. This study culminates in creating a cultural showcase involving some of nature's most significant molecular structures. Results of both musical analysis and the showcase indicate that protein sonification has the potential to act as a helpful outreach and engagement tool for biologists while also helping experts in the field glean new insights from complex data.

Keywords

Machine learning , KNN , Protein , Sonification , Music

Citation

Ronan, I., Mi, Y., Yallapragada, V. V. B., Ó Nuanáin, C. and Tabirca, S. (2024) 'A machine learning approach to 3D protein structure sonification', International Journal of Music Science, Technology and Art, 6(1), pp. 9-18. https://doi.org/10.48293/IJMSTA-106