2026-09-19T19:47:10Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/664582022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Srinivas Devadas, Bonnie Berger and Susan Lindquist.
O'Donnell, Charles William
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2011-10-17T21:28:17Z
2011-10-17T21:28:17Z
2011
2011
http://hdl.handle.net/1721.1/66458
756041265
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
In title on title page, [beta] appears as lower case Greek letter. Cataloged from PDF version of thesis.
Includes bibliographical references (p. 149-161).
Our ability to characterize protein structure and dynamics is vastly outpaced by the speed of modern genetic sequencing, creating a growing divide between our knowledge of biological sequence and structure. Structural modeling algorithms offer the hope to bridge this gap through computational exploration of the sequence determinants of structure diversity. In this thesis, we introduce new algorithms that enable the efficient modeling of protein structure ensembles and their sequence variants. These statistical mechanics-based constructions enable the identification of all energetically likely sequence/structure states for a family of proteins. Beyond improved structure predictions, this approach enables a framework for thermodynamically-driven mutational and comparative analysis as well as the approximation of kinetic protein folding pathways. We have applied these techniques to two protein types that are notoriously difficult to characterize biochemically: transmembrane P-barrel proteins and amyloid fibrils. For these we advance the state-of-the-art in structure prediction, mutational analysis, and sequence alignment. Further, we have collaborated to apply these methods to open scientific questions about amyloid fibrils and bacterial biofilms.
by Charles William O'Donnell.
Ph.D.
161 p.
eng
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Ensemble modeling of [beta]-sheet proteins
Thesis
application/pdf
Publication
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unknown
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<Abstract>Our ability to characterize protein structure and dynamics is vastly outpaced by the speed of modern genetic sequencing, creating a growing divide between our knowledge of biological sequence and structure. Structural modeling algorithms offer the hope to bridge this gap through computational exploration of the sequence determinants of structure diversity. In this thesis, we introduce new algorithms that enable the efficient modeling of protein structure ensembles and their sequence variants. These statistical mechanics-based constructions enable the identification of all energetically likely sequence/structure states for a family of proteins. Beyond improved structure predictions, this approach enables a framework for thermodynamically-driven mutational and comparative analysis as well as the approximation of kinetic protein folding pathways. We have applied these techniques to two protein types that are notoriously difficult to characterize biochemically: transmembrane P-barrel proteins and amyloid fibrils. For these we advance the state-of-the-art in structure prediction, mutational analysis, and sequence alignment. Further, we have collaborated to apply these methods to open scientific questions about amyloid fibrils and bacterial biofilms.</Abstract>
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