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Artificial intelligence and protein engineering : information theoretical approaches to modeling enzymatic catalysis

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dc.contributor.advisor Bruce Tidor. en_US
dc.contributor.author Weis, James W. (James Woodward) en_US
dc.contributor.other Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. en_US
dc.date.accessioned 2017-05-11T19:58:24Z
dc.date.available 2017-05-11T19:58:24Z
dc.date.copyright 2017 en_US
dc.date.issued 2017 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/108969
dc.description Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. en_US
dc.description Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references (pages 57-62). en_US
dc.description.abstract The importance of dynamics in enzymatic function has been extensively recognized in the literature. Despite this, the vast majority of enzyme engineering approaches are based on the energetic optimization of static molecular structures. Using computational sampling techniques, especially rare-event sampling methods like Transition Path Sampling, we can now generate large datasets of reactive and non-reactive enzyme trajectories. However, due to the high dimensionality of phase space, as well as the highly correlated nature of proteins, using these simulations to understand the drivers of reactivity, or as a starting point for enzyme engineering, is difficult. In this work, we apply information theoretic techniques to both formulate and propose potential solutions to this problem. We also present a novel feature selection algorithm that leverages entropy approximations to find the subset of features X1 .. .Xd such that H(Y/X1...Xd), the entropy of the output variable conditioned on the identified features, is minimized. We validate the advantages of this algorithm through a comparative analysis on synthetic datasets-demonstrating both improved predictive performance, as well as reduced conditional entropy-and finally apply these techniques in the analysis of a large dataset of simulated ketol-acid reductoisomerase trajectories. We find not only that we are are capable of predicting enzyme reactivity with a small number of well-chosen molecular features, but also that the GCMI algorithm constitutes a powerful framework for feature selection, and is well-suited for the analysis of such datasets. en_US
dc.description.statementofresponsibility by James W. Weis. en_US
dc.format.extent 72 pages en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights 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. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Electrical Engineering and Computer Science. en_US
dc.title Artificial intelligence and protein engineering : information theoretical approaches to modeling enzymatic catalysis en_US
dc.title.alternative Information theoretical approaches to modeling enzymatic catalysis en_US
dc.type Thesis en_US
dc.description.degree S.M. en_US
dc.contributor.department Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. en_US
dc.identifier.oclc 986496861 en_US


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