The music of proteins is made audible by a computer program that learns from Chopin
With the right computer program, proteins become enjoyable music.
There are many surprising analogies between protein, the building blocks of life, and musical notation. These analogies can be used not only to advance research, but also to make the complexity of proteins accessible to the public.
We are computational biologists who believe that hearing the sound of life at the molecular level could help people learn more about biology and computational science. While creating protein-based music was nothing new, different musical styles and compositional algorithms remained to be explored. So we led a team of high school students and other academics to figure out how to create classical music from protein.
Musical analogies of proteins
Proteins are structured as folded chains. These chains are made up of small units of 20 possible amino acids, each labeled with a letter of the alphabet.
A protein chain can be represented as a string of these alphabetical letters, much like a string of musical notes in alphabetical notation.
Protein chains can also bend in wavy and curved patterns with ups, downs, bends, and curls. Likewise, the music consists of sound waves of upper and lower pitches, with changing tempos and repeated patterns.
Protein-music algorithms can thus map the structural and physicochemical characteristics of a chain of amino acids to the musical characteristics of a chain of notes.
Improve the musicality of protein mapping
The protein-to-music mapping can be refined based on the characteristics of a specific musical style. This improves the musicality, or melody of the song, when converting properties of amino acids, such as sequence patterns and variations, to analogous musical properties, such as pitch, note length, and chords.
For our study, we specifically selected classical piano music from the Romantic period of the 19th century, which includes composers like Chopin and Schubert, as a guide, as it generally covers a wide range of notes with more complex characteristics such as the chromaticism, like playing with both white and black. piano keys in order of pitch and chords. Music from this period also tends to have lighter, more graceful, and emotional melodies. The songs are generally homophonic, which means that they follow a central melody with accompaniment. These features allowed us to test a wider range of notes in our protein-to-music mapping algorithm. In this case, we chose to analyze the characteristics of Chopin’s “Fantaisie-Impromptu” to guide our program development.
To test the algorithm, we applied it to 18 proteins that play key roles in various biological functions. Each amino acid in the protein is associated with a particular note based on how often they appear in the protein, and other aspects of their biochemistry match other aspects of music. A larger amino acid, for example, would have a shorter note length, and vice versa.
The resulting music is complex, with notable variations in pitch, volume and rhythm. Since the algorithm was entirely based on amino acid sequence and no protein shares the same amino acid sequence, each protein will produce a separate song. It also means that there are variations in musicality between the different pieces and that interesting patterns can emerge.
For example, music generated from the receptor protein that binds to the hormone and neurotransmitter oxytocin has recurring patterns due to the repetition of certain small sequences of amino acids.
On the other hand, the music generated from the tumor antigen p53, a protein that prevents cancer formation, is highly chromatic, producing particularly fascinating phrases where the music sounds almost like a toccata, a style that often features a fast and virtuoso technique.
By guiding the analysis of amino acid properties through specific musical styles, protein music can sound much more pleasing to the ear. This can be developed and applied to a wider variety of musical styles, including pop and jazz.
Protein music is an example of how the combination of biological and computer science can produce beautiful works of art. Our hope is that this work will encourage researchers to compose protein music of different styles and inspire audiences to learn more about the basics of life.
This study was developed in collaboration with Nicole Tay, Fanxi Liu, Chaoxin Wang and Hui Zhang.
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