This piece is constructed entirely from the sounds generated from amino acids, the building blocks of proteins. Encoded by the DNA sequence, amino acids form chains that assembly into hierarchical materials that serve diverse functions in biology (as enzymes, structural materials, signal transmitters, and many others).
All sounds used to compose this piece are generated from the sounds of amino acids, computed from their quantum mechanical vibrations, reflecting their elementary molecular features. For further details on the scientific foundation, see .
The main melody is derived from the amino acid sequence of three proteins:
1) 6cjc (adrenodoxin reductase, a flavoenzyme that is involved in the biosynthesis of steroid hormones; https://www.rcsb.org/structure/1CJC)
2) A de novo protein synthesized using a machine learning algorithm as reported in 
3) 194l (lysozyme enzyme; found in tears of humans and animals and egg white; https://www.rcsb.org/structure/194L)
The melodic backbone is composed of a progression from proteins 1) to 2) to 3). Motifs derived from these proteins are used throughout to generate repetitive elements and to conjoin melodic concepts (e.g., segments extracted from 6cjc are used as transitional elements between 1cjc and the de novo protein and again during the transition to 194l.
All rhythmic elements (bass drum, snare-like, hi-hat, and others) are generated from soundings of amino acids. Both melodic and rhythmic patterns used are derived from the three proteins cited above.
No other synthetic or sampled sounds have been used in the composition. What you hear is a composition in the natural 20-tone amino acid scale.
Materials and music have been intimately connected throughout centuries of human evolution and civilization. Indeed, materials such as wood, animal skin or metals are the basis for most musical instruments used throughout history. Today, we are able to use advanced computing algorithms to blur the boundary between material and sound and use hierarchical representations of materials in distinct spaces such as sound or language to advance design objectives. The approach used in this work is that the translation of protein materials representations into music not only allows us to create musical instruments, but also enables us to exploit deep neural network models to represent and manipulate protein designs in the audio space. Thereby we take advantage of longer-range structure that is important in music, and which is equivalently important in protein design (in connecting amino acid sequence to secondary structure and folding). This paradigm goes beyond proteins but rather enables us to connect nanostructures and music in a reversible way, providing an approach to design nanomaterials, DNA, proteins, or other molecular architectures from the nanoscale upwards.
 C.H. Yu, Z. Qin, F. Martinez, M.J. Buehler, A Self-Consistent Sonification Method to Translate Amino Acid Sequences into Musical Compositions and Application in Protein Design using Artificial Intelligence, ACS Nano, in press, 2019
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