Sounds interesting: can sonification help us design new proteins?

Sebastian L. Franjou, Mario Milazzo, Chi Hua Yu, Markus J. Buehler

研究成果: Article同行評審

13 引文 斯高帕斯(Scopus)


Introduction: The practice of turning scientific data into music, a practice known as sonification, is a growing field. Driven by analogies between the hierarchical structures of proteins and many forms of music, multiple attempts of mapping proteins to music have been made. Previous works have either worked at a low level, mapping amino acid to notes, or at a higher level, using the overall structure as a basis for composition. Areas covered: We report a comprehensive mapping strategy that encompasses the encoding of the geometry of proteins, in addition to the amino acid sequence and secondary structure information. This leads to a piece of music that is both more complete and closely linked to the original protein. By using this mapping, we can invert the process and map music to proteins, retrieving not only the amino acid sequence but also the secondary structure and folding from musical data. Expert opinion: We can train a machine learning model on ‘protein music’ to generate new music that can be translated to new proteins. By selecting proper datasets and conditioning parameters on the generative model, we could tune de novo proteins with high level parameters to achieve certain protein design features.

頁(從 - 到)875-879
期刊Expert Review of Proteomics
出版狀態Published - 2019 12月 2

All Science Journal Classification (ASJC) codes

  • 生物化學
  • 分子生物學


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