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.

简介：将科​​学数据转化为音乐的做法（称为超音波）正在不断发展。在蛋白质的层次结构与多种音乐形式之间的类比驱动下，进行了多次尝试将蛋白质映射到音乐的尝试。以前的作品使用整体结构作为构成的基础，将氨基酸映射到音符的层次较低，或者层次较高。

涵盖的领域：我们报告了一种全面的作图策略，除氨基酸序列和二级结构信息外，还涵盖蛋白质几何形状的编码。这会导致一段音乐更加完整，并且与原始蛋白质紧密相连。通过使用这种映射，我们可以反转过程并将音乐映射到蛋白质，不仅检索氨基酸序列，而且检索二级结构并从音乐数据中折叠。

专家意见：我们可以在“蛋白质音乐”上训练机器学习模型，以生成可以转化为新蛋白质的新音乐。通过在生成模型上选择适当的数据集和条件参数，我们可以对具有高水平参数的从头蛋白质进行调节，以实现某些蛋白质设计功能。