Deep Neural Networks for Learning Protein Vibrational Behaviors to Characterize Structure and Function
Name
Granberry-darnellg-meng-eecs-2022-thesis.pdf
Description
Thesis PDF
Size
44.61 MB
Format
Adobe PDF
Checksum (MD5)
b2ae3eaa35327b2caa31e4f16360dd9d
Author(s)
Granberry Jr., Darnell Scott
Advisor(s)
Buehler, Markus J.
Date Issued
February 2022
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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