Machine learning for understanding protein sequence and structure
Name
1237266130-MIT.pdf
Size
22.14 MB
Format
Adobe PDF
Checksum (MD5)
04dd762e328239a5b9f829d6cad840f1
Author(s)
Bepler, Tristan(Tristan Wendland)
Advisor(s)
Bonnie Berger.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Proteins are the fundamental building blocks of life, carrying out a vast array of functions at the molecular level. Understanding these molecular machines has been a core problem in biology for decades. Recent advances in cryo-electron microscopy (cryoEM) has enabled high resolution experimental measurement of proteins in their native states. However, this technology remains expensive and low throughput. At the same time, ever growing protein databases offer new opportunities for understanding the diversity of natural proteins and for linking sequence to structure and function. This thesis introduces a variety of machine learning methods for accelerating protein structure determination by cryoEM and for learning from large protein databases. We first consider the problem of protein identification in the large images collected in cryoEM. We propose a positive-unlabeled learning framework that enables high accuracy particle detection with few labeled data points, both improving data quality and analysis speed. Next, we develop a deep denoising model for cryo-electron micrographs. By learning the denoising model from large amounts of real cryoEM data, we are able to capture the noise generation process and accurately denoise micrographs, improving the ability of experamentalists to examine and interpret their data. We then introduce a neural network model for understanding continuous variability in proteins in cryoEM data by explicitly disentangling variation of interest (structure) for nuisance variation due to rotation and translation. Finally, we move beyond cryoEM and propose a method for learning vector embeddings of proteins using information from structure and sequence. Many of the machine learning methods developed here are general purpose and can be applied to other data domains.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, February, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 183-200).
Subjects
Computational and Systems Biology Program.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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