Researchers from MIT have constructed a musical scale from amino acids, enabling them to equate structural motifs to musical ones and reverse engineer entirely novel proteins.
A team of researchers from the Massachusetts Institute of Technology (MIT; MA, USA) has composed a new way to conceptualize proteins and their structure. Led by Markus Buehler, the team converted each amino acid into a tone that can then be used to construct a tune from the sequence of a protein’s structure. By reverse engineering this process, the team has then been able to develop completely novel synthetic proteins.
The 20-tone amino acid scale was developed using quantum chemical theories to convert the vibrational frequencies of amino acids into notes. These notes were then transposed to a range that humans are able to perceive. On compiling the information alongside a protein’s amino acid sequence, tunes were then produced that enabled the researchers to equate structural motifs to musical ones. Buehler, for instance, can listen to a composition and identify β-sheets and α-helices as they play out.
The inspiration for the idea came from a desire to better interpret and understand the nebulously complicated laws and languages of protein structure and folding, using humanity’s innate ability to conceptualize the complex patterns used in music. What’s more, the information from a protein’s structure can be coded into different conformations of pitch, volume and duration; adding further levels of detail to the protein’s sonic representation.
“Markus Buehler has been gifted with a most creative soul, and his explorations into the inner workings of biomolecules are advancing our understanding of the mechanical response of biological materials in a most significant manner."
Once the protein sequences are converted to tunes, the amino acid discography can be analyzed by an AI system. This process can take a few days for the AI to establish all the patterns and rules within a class of proteins. However, once completed, the program is then capable of designing a new variant of the protein within milliseconds. Enthralled by the AI’s capability, Buehler explained that due to the “trillions and trillions” of potential combinations, a human “wouldn’t be able to do it from scratch, but that’s what the AI can do.”
This model of studying structure also imparts a greater understanding of the reality of protein structure: “When you look at a molecule in a textbook, it’s static,” Buehler said. “But it’s not static at all. It’s moving and vibrating. Every bit of matter is a set of vibrations. And we can use this concept as a way of describing matter,” explained Buehler.
There are, however, some limitations to this model. Firstly, the AI cannot explain the interactions and laws that it has studied and learned but can only employ them itself. Secondly, the newly designed proteins cannot be made to order; any alterations to the faculties of the protein will be entirely random. “You still need to do the experiment, there’s no way to predict what it will do,” explains Buehler.
Excitingly, this research bears interests beyond the purely scientific. The team used the 20-tone scale and motifs from naturally occurring and newly designed proteins to compose pieces of music without the use of synthesizers or instruments (below).
Commending the ingenuity of the research, Marc Meyers (University of California at San Diego; CA, USA) stated that: “Markus Buehler has been gifted with a most creative soul, and his explorations into the inner workings of biomolecules are advancing our understanding of the mechanical response of biological materials in a most significant manner. The focusing of this imagination to music is a novel and intriguing direction. This is experimental music at its best. The rhythms of life, including the pulsations of our heart, were the initial sources of repetitive sounds that engendered the marvelous world of music. Markus has descended into the nanospace to extract the rhythms of the amino acids, the building blocks of life.”
Written By Tristan Free
Updated 15 July, 2019
Source Yu C-H, Qin Z, Martin-Martinez F, Buehler M. A self-consistent sonification method to translate amino acid sequences into musical compositions and application in protein design using artificial intelligence. ACS Nano. doi:10.1021/acsnano.9b02180 (2019); https://pubs.acs.org/doi/10.1021/acsnano.9b02180# http://news.mit.edu/2019/translating-proteins-music-0626