Sounds interesting: can sonification help us design new proteins?
Identifieur interne : 000406 ( Main/Exploration ); précédent : 000405; suivant : 000407Sounds interesting: can sonification help us design new proteins?
Auteurs : Sebastian L. Franjou [États-Unis] ; Mario Milazzo [États-Unis, Italie] ; Chi-Hua Yu [États-Unis, Taïwan] ; Markus J. Buehler [États-Unis]Source :
- Expert review of proteomics [ 1744-8387 ]
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical : Proteins.
- trends : Protein Engineering.
- Amino Acid Sequence, Humans, Machine Learning, Music, Protein Structure, Secondary.
Abstract
Introduction: The practice of turning scientific data into music, a practice known as sonification, is a growing field. Driven by analogies between the hierarchical structures of proteins and many forms of music, multiple attempts of mapping proteins to music have been made. Previous works have either worked at a low level, mapping amino acid to notes, or at a higher level, using the overall structure as a basis for composition.Areas covered: We report a comprehensive mapping strategy that encompasses the encoding of the geometry of proteins, in addition to the amino acid sequence and secondary structure information. This leads to a piece of music that is both more complete and closely linked to the original protein. By using this mapping, we can invert the process and map music to proteins, retrieving not only the amino acid sequence but also the secondary structure and folding from musical data.Expert opinion: We can train a machine learning model on 'protein music' to generate new music that can be translated to new proteins. By selecting proper datasets and conditioning parameters on the generative model, we could tune de novo proteins with high level parameters to achieve certain protein design features.
DOI: 10.1080/14789450.2019.1697236
PubMed: 31756126
Affiliations:
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Buehler</name><affiliation wicri:level="2"><nlm:affiliation>Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, Cambridge, MA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, Cambridge, MA</wicri:regionArea><placeName><region type="state">Massachusetts</region></placeName></affiliation></author></analytic><series><title level="j">Expert review of proteomics</title><idno type="eISSN">1744-8387</idno></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Amino Acid Sequence (MeSH)</term><term>Humans (MeSH)</term><term>Machine Learning (MeSH)</term><term>Music (MeSH)</term><term>Protein Engineering (trends)</term><term>Protein Structure, Secondary (MeSH)</term><term>Proteins (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Apprentissage machine (MeSH)</term><term>Humains (MeSH)</term><term>Ingénierie des protéines (tendances)</term><term>Musique (MeSH)</term><term>Protéines (MeSH)</term><term>Structure secondaire des protéines (MeSH)</term><term>Séquence d'acides aminés (MeSH)</term></keywords><keywords scheme="MESH" type="chemical" xml:lang="en"><term>Proteins</term></keywords><keywords scheme="MESH" qualifier="tendances" xml:lang="fr"><term>Ingénierie des protéines</term></keywords><keywords scheme="MESH" qualifier="trends" xml:lang="en"><term>Protein Engineering</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Amino Acid Sequence</term><term>Humans</term><term>Machine Learning</term><term>Music</term><term>Protein Structure, Secondary</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Apprentissage machine</term><term>Humains</term><term>Musique</term><term>Protéines</term><term>Structure secondaire des protéines</term><term>Séquence d'acides aminés</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><b>Introduction</b>: The practice of turning scientific data into music, a practice known as sonification, is a growing field. Driven by analogies between the hierarchical structures of proteins and many forms of music, multiple attempts of mapping proteins to music have been made. Previous works have either worked at a low level, mapping amino acid to notes, or at a higher level, using the overall structure as a basis for composition.<b>Areas covered</b>: We report a comprehensive mapping strategy that encompasses the encoding of the geometry of proteins, in addition to the amino acid sequence and secondary structure information. This leads to a piece of music that is both more complete and closely linked to the original protein. By using this mapping, we can invert the process and map music to proteins, retrieving not only the amino acid sequence but also the secondary structure and folding from musical data.<b>Expert opinion</b>: We can train a machine learning model on 'protein music' to generate new music that can be translated to new proteins. By selecting proper datasets and conditioning parameters on the generative model, we could tune <i>de novo</i> proteins with high level parameters to achieve certain protein design features.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">31756126</PMID><DateCompleted><Year>2020</Year><Month>07</Month><Day>07</Day></DateCompleted><DateRevised><Year>2020</Year><Month>07</Month><Day>07</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1744-8387</ISSN><JournalIssue CitedMedium="Internet"><Volume>16</Volume><Issue>11-12</Issue><PubDate><MedlineDate>2019 Nov - Dec</MedlineDate></PubDate></JournalIssue><Title>Expert review of proteomics</Title><ISOAbbreviation>Expert Rev Proteomics</ISOAbbreviation></Journal><ArticleTitle>Sounds interesting: can sonification help us design new proteins?</ArticleTitle><Pagination><MedlinePgn>875-879</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1080/14789450.2019.1697236</ELocationID><Abstract><AbstractText><b>Introduction</b>: The practice of turning scientific data into music, a practice known as sonification, is a growing field. Driven by analogies between the hierarchical structures of proteins and many forms of music, multiple attempts of mapping proteins to music have been made. Previous works have either worked at a low level, mapping amino acid to notes, or at a higher level, using the overall structure as a basis for composition.<b>Areas covered</b>: We report a comprehensive mapping strategy that encompasses the encoding of the geometry of proteins, in addition to the amino acid sequence and secondary structure information. This leads to a piece of music that is both more complete and closely linked to the original protein. By using this mapping, we can invert the process and map music to proteins, retrieving not only the amino acid sequence but also the secondary structure and folding from musical data.<b>Expert opinion</b>: We can train a machine learning model on 'protein music' to generate new music that can be translated to new proteins. By selecting proper datasets and conditioning parameters on the generative model, we could tune <i>de novo</i> proteins with high level parameters to achieve certain protein design features.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Franjou</LastName><ForeName>Sebastian L</ForeName><Initials>SL</Initials><AffiliationInfo><Affiliation>Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, Cambridge, MA, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Music and Theater Arts, Massachusetts Institute of Technology, Cambridge, MA, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Milazzo</LastName><ForeName>Mario</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, Cambridge, MA, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yu</LastName><ForeName>Chi-Hua</ForeName><Initials>CH</Initials><AffiliationInfo><Affiliation>Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, Cambridge, MA, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Buehler</LastName><ForeName>Markus J</ForeName><Initials>MJ</Initials><AffiliationInfo><Affiliation>Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, Cambridge, MA, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>U01 EB014976</GrantID><Acronym>EB</Acronym><Agency>NIBIB NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>11</Month><Day>27</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Expert Rev Proteomics</MedlineTA><NlmUniqueID>101223548</NlmUniqueID><ISSNLinking>1478-9450</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D011506">Proteins</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000595" MajorTopicYN="N">Amino Acid Sequence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000069550" MajorTopicYN="Y">Machine Learning</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009146" MajorTopicYN="Y">Music</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015202" MajorTopicYN="N">Protein Engineering</DescriptorName><QualifierName UI="Q000639" MajorTopicYN="N">trends</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D017433" MajorTopicYN="N">Protein Structure, Secondary</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011506" MajorTopicYN="Y">Proteins</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword 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Franjou</name><name sortKey="Milazzo, Mario" sort="Milazzo, Mario" uniqKey="Milazzo M" first="Mario" last="Milazzo">Mario Milazzo</name><name sortKey="Yu, Chi Hua" sort="Yu, Chi Hua" uniqKey="Yu C" first="Chi-Hua" last="Yu">Chi-Hua Yu</name></country><country name="Italie"><region name="Toscane"><name sortKey="Milazzo, Mario" sort="Milazzo, Mario" uniqKey="Milazzo M" first="Mario" last="Milazzo">Mario Milazzo</name></region></country><country name="Taïwan"><noRegion><name sortKey="Yu, Chi Hua" sort="Yu, Chi Hua" uniqKey="Yu C" first="Chi-Hua" last="Yu">Chi-Hua Yu</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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