Dissertation/Thesis Abstract

Structure prediction and visualization in molecular biology
by Poultney, Christopher S., Ph.D., New York University, 2010, 131; 3408352
Abstract (Summary)

The tools of computer science can be a tremendous help to the working biologist. Two broad areas where this is particularly true are visualization and prediction. In visualization, the size of the data involved often makes meaningful exploration of the data and discovery of salient features difficult and time-consuming. Similarly, intelligent prediction algorithms can greatly reduce the lab time required to achieve significant results, or can reduce an intractable space of potential experiments to a tractable size.

We describe two specific projects designed with these precepts in mind. The first, a software program called Sungear (Poultney et al., 2007; Poultney and Shasha, 2009), is a visualization tool that shows the outcomes of N experiments on M entities. Sungear displays sets of entities—the intersections of these outcomes—within a generalization of the traditional Venn diagram, and provides an interactive, visual system to answer queries about the data. The second project addresses the issue of rational design of temperature-sensitive mutants. Temperature-sensitive mutations are a tremendously valuable research tool, but to date, most methods for generating such mutants involve large-scale random mutations followed by an intensive screening and characterization process. Surprisingly little work has been done in the area of predicting these mutations given the importance they play in many genetic and functional studies of gene function. Furthermore, the existing methods are based entirely on secondary sequence, and do not use 3-dimensional structure. We describe a system that, given the structure of a protein of interest, uses a combination of protein structure prediction and machine learning to provide a ranked list of likely candidates for temperature-sensitive mutations.

Indexing (document details)
Advisor: Shasha, Dennis E.
Commitee: Bonneau, Richard, Butterfoss, Glenn L., Gunsalus, Kristin C., LeCun, Yann
School: New York University
Department: Computer Science
School Location: United States -- New York
Source: DAI-B 71/07, Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Bioinformatics, Computer science
Keywords: Conditional mutations, Machine learning, Protein structure, Rosetta, Visualization
Publication Number: 3408352
ISBN: 9781124044705
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