A Self-Consistent Sonification Method to Translate Amino Acid Sequences into Musical Compositions and Application in Protein Design Using Artificial Intelligence

We report a self-consistent method
to translate amino acid sequences
into audible sound, use the representation in the musical space to
train a neural network, and then apply it to generate protein designs
using artificial intelligence (AI). The sonification method proposed
here uses the normal mode vibrations of the amino acid building blocks
of proteins to compute an audible representation of each of the 20
natural amino acids, which is fully defined by the overlay of its
respective natural vibrations. The vibrational frequencies are transposed
to the audible spectrum following the musical concept of transpositional
equivalence, playing or writing music in a way that makes it sound
higher or lower in pitch while retaining the relationships between
tones or chords played. This transposition method ensures that the
relative values of the vibrational frequencies within each amino acid
and among different amino acids are retained. The characteristic frequency
spectrum and sound associated with each of the amino acids represents
a type of musical scale that consists of 20 tones, the “amino
acid scale”. To create a playable instrument, each tone associated
with the amino acids is assigned to a specific key on a piano roll,
which allows us to map the sequence of amino acids in proteins into
a musical score. To reflect higher-order structural details of proteins,
the volume and duration of the notes associated with each amino acid
are defined by the secondary structure of proteins, computed using
DSSP and thereby introducing musical rhythm. We then train a recurrent
neural network based on a large set of musical scores generated by
this sonification method and use AI to generate musical compositions,
capturing the innate relationships between amino acid sequence and
protein structure. We then translate the de novo musical
data generated by AI into protein sequences, thereby obtaining de novo protein designs that feature specific design characteristics.
We illustrate the approach in several examples that reflect the sonification
of protein sequences, including multihour audible representations
of natural proteins and protein-based musical compositions solely
generated by AI. The approach proposed here may provide an avenue
for understanding sequence patterns, variations, and mutations and
offers an outreach mechanism to explain the significance of protein
sequences. The method may also offer insight into protein folding
and understanding the context of the amino acid sequence in defining
the secondary and higher-order folded structure of proteins and could
hence be used to detect the effects of mutations through sound.