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Bioinspired translation of classical music intode novoprotein structures using deep learning and molecular modeling.
Milazzo, Mario; Anderson, Grace I; Buehler, Markus J.
Afiliación
  • Milazzo M; Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, United States of America.
  • Anderson GI; Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, United States of America.
  • Buehler MJ; Center for Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, United States of America.
Bioinspir Biomim ; 17(1)2021 11 26.
Article en En | MEDLINE | ID: mdl-34700310
Architected biomaterials, as well as sound and music, are constructed from small building blocks that are assembled across time- and length-scales. Here we present a novel deep learning-enabled integrated algorithmic workflow to merge the two concepts for radical discovery ofde novoprotein materials, exploiting musical creativity as the foundation, and extrapolating through a recursive method to increase protein complexity by successively injecting protein chemistry into the process. Indeed, music is one of the few universal expressions that can create bridges between cultures, find associations between seemingly unrelated concepts, and can be used as a novel way to generate bio-inspired designs that derive functions from the imaginations of the creative mind. Earlier work has offered a pathway to convert proteins into sound, and sound into proteins. Here we build on this paradigm and translate a piece of classical music into matter. Based on Bach's Goldberg variations, we offer a series of case studies to convert the musical data imagined by the composer into protein design, and folded into a 3D structure using deep learning. The quest we seek to address is to identify semblances, or memories, or information content in such musical creation, that offers new insights into pattern relationships between distinct manifestations of information. Using basic local alignment search tool analysis, we find that several fragments of the new proteins display similarities to existing protein sequences found in proteobacteria among other organisms, especially in regions of low complexity and repetitive motifs. The resulting protein forms the basis for iterative musical composition, and an evolutionary paradigm that defines a variational pathway for melodic development, complementing conventional creative or mathematical methods. This paper broadens the concept of what is understood as bio-inspiration to include a broad array of systems created by humans, animals, or other natural mechanisms.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo / Música Límite: Animals Idioma: En Revista: Bioinspir Biomim Asunto de la revista: BIOLOGIA / ENGENHARIA BIOMEDICA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo / Música Límite: Animals Idioma: En Revista: Bioinspir Biomim Asunto de la revista: BIOLOGIA / ENGENHARIA BIOMEDICA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos
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