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oai:dspace.mit.edu:1721.1/1432872022-06-16T03:26:25Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Buehler, Markus J. Granberry Jr., Darnell Scott Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2022-06-15T13:09:54Z 2022-06-15T13:09:54Z 2022-02 2022-02-22T18:32:13.393Z https://hdl.handle.net/1721.1/143287 Proteins’ structures and motions are essential for nearly all biological functions and malfunctions, making them prime targets for uncovering and controlling processes associated with metabolism and disease. Normal mode analysis is a powerful method that allows us to understand the mechanisms of these functions in high detail, but not without significant cost. Replacing this method with inference by a machine learning model could potentially eliminate this restriction while still providing useful accuracy. Prior work has demonstrated success in a simplified version of this problem that used features computed from each protein’s structure, and predicted parameters for a geometric function-of-best-fit relating the modes, not the explicit modes themselves. In this work, we seek to develop a fully end-toend model that will allow researchers to predict a protein’s normal mode spectrum directly from its peptide sequence, allowing us to bypass the costs associated with both normal mode analysis and protein structure determination. We additionally explore the parallels between protein science and music theory, and provide analysis of a deep neural network trained to understand Bach’s highly structured Goldberg Variations. M.Eng. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ Deep Neural Networks for Learning Protein Vibrational Behaviors to Characterize Structure and Function Thesis application/pdf Master Master of Engineering in Electrical Engineering and Computer Science Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="92fca70e-a0f4-4abe-91ab-92610fd536c5"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Deep Neural Networks for Learning Protein Vibrational Behaviors to Characterize Structure and Function</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2022-02</PublicationDate> <Authors> <Author> <DisplayName>Granberry Jr., Darnell Scott</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>Proteins’ structures and motions are essential for nearly all biological functions and malfunctions, making them prime targets for uncovering and controlling processes associated with metabolism and disease. Normal mode analysis is a powerful method that allows us to understand the mechanisms of these functions in high detail, but not without significant cost. Replacing this method with inference by a machine learning model could potentially eliminate this restriction while still providing useful accuracy. Prior work has demonstrated success in a simplified version of this problem that used features computed from each protein’s structure, and predicted parameters for a geometric function-of-best-fit relating the modes, not the explicit modes themselves. In this work, we seek to develop a fully end-toend model that will allow researchers to predict a protein’s normal mode spectrum directly from its peptide sequence, allowing us to bypass the costs associated with both normal mode analysis and protein structure determination. We additionally explore the parallels between protein science and music theory, and provide analysis of a deep neural network trained to understand Bach’s highly structured Goldberg Variations.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>