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Amy Keating. Xue, Vincent Massachusetts Institute of Technology. Computational and Systems Biology Program. Massachusetts Institute of Technology. Computational and Systems Biology Program 2019-02-14T15:52:21Z 2019-02-14T15:52:21Z 2018 2018 http://hdl.handle.net/1721.1/120446 1084657995 Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 153-164). Protein-protein interactions (PPIs) play a major role in cellular function, mediating signal processing and regulating enzymatic activity. Understanding how proteins interact is essential for predicting new binding partners and engineering new functions. Mutational analysis is one way to study the determinants of protein interaction. Traditionally, the biophysical study of protein interactions has been limited by the number of mutants that could be made and analyzed, but advances in high-throughput sequencing have enabled rapid assessment of thousands of variants. The Keating lab has developed an experimental protocol that can rank peptides based on their binding affinity for a designated receptor. This technique, called SORTCERY, takes advantage of cell sorting and deep-sequencing technologies to provide more binding data at a higher resolution than has previously been achievable. New computational methods are needed to process and analyze the high-throughput datasets. In this thesis, I show how experimental data from SORTCERY experiments can be processed, modeled, and used to design novel peptides with select specificity characteristics. I describe the computational pipeline that I developed to curate the data and regression models that I constructed from the data to relate protein sequence to binding. I applied models trained on experimental data sets to study the peptide-binding specificity landscape of the Bc1-xL, Mc1-1, and Bf1-1 anti-apoptotic proteins, and I designed novel peptides that selectively bind tightly to only one of these receptors, or to a pre-specified combination of receptors. My thesis illustrates how data-driven models combined with high-throughput binding assays provide new opportunities for rational design. by Vincent Xue. Ph.D. 164 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Computational and Systems Biology Program. Modeling and designing Bc1-2 family protein interactions using high-throughput interaction data Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="f9b64f89-19d9-4bea-aa4f-54f4187aeee0"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Modeling and designing Bc1-2 family protein interactions using high-throughput interaction data</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Xue, Vincent</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Computational and Systems Biology Program.</Keyword> <Abstract>Protein-protein interactions (PPIs) play a major role in cellular function, mediating signal processing and regulating enzymatic activity. Understanding how proteins interact is essential for predicting new binding partners and engineering new functions. Mutational analysis is one way to study the determinants of protein interaction. Traditionally, the biophysical study of protein interactions has been limited by the number of mutants that could be made and analyzed, but advances in high-throughput sequencing have enabled rapid assessment of thousands of variants. The Keating lab has developed an experimental protocol that can rank peptides based on their binding affinity for a designated receptor. This technique, called SORTCERY, takes advantage of cell sorting and deep-sequencing technologies to provide more binding data at a higher resolution than has previously been achievable. New computational methods are needed to process and analyze the high-throughput datasets. In this thesis, I show how experimental data from SORTCERY experiments can be processed, modeled, and used to design novel peptides with select specificity characteristics. I describe the computational pipeline that I developed to curate the data and regression models that I constructed from the data to relate protein sequence to binding. I applied models trained on experimental data sets to study the peptide-binding specificity landscape of the Bc1-xL, Mc1-1, and Bf1-1 anti-apoptotic proteins, and I designed novel peptides that selectively bind tightly to only one of these receptors, or to a pre-specified combination of receptors. My thesis illustrates how data-driven models combined with high-throughput binding assays provide new opportunities for rational design.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>